3:I[39275,[],""]
5:I[61343,[],""]
6:I[25254,["922","static/chunks/c15bf2b0-77a7fe2edf0e5bbc.js","6648","static/chunks/6648-bd55b61b3b302c00.js","7138","static/chunks/7138-467a231c2eee4048.js","8171","static/chunks/8171-1bce9f9ee10c42b8.js","5254","static/chunks/5254-bf8429c6ce99e10d.js","3185","static/chunks/app/layout-0c54c3a074634d7b.js"],"default"]
4:["slug","hn-techno-ai-ml-data-science-training-internship-ahmedabad","d"]
0:["kUyMH7N9QWwyONJ9A0s1t",[[["",{"children":[["slug","hn-techno-ai-ml-data-science-training-internship-ahmedabad","d"],{"children":["__PAGE__?{\"slug\":\"hn-techno-ai-ml-data-science-training-internship-ahmedabad\"}",{}]}]},"$undefined","$undefined",true],["",{"children":[["slug","hn-techno-ai-ml-data-science-training-internship-ahmedabad","d"],{"children":["__PAGE__",{},[["$L1","$L2"],null],null]},["$","$L3",null,{"parallelRouterKey":"children","segmentPath":["children","$4","children"],"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","notFoundStyles":"$undefined","styles":[["$","link","0",{"rel":"stylesheet","href":"/_next/static/css/88f4883499169169.css","precedence":"next","crossOrigin":"$undefined"}],["$","link","1",{"rel":"stylesheet","href":"/_next/static/css/31db57dba135ef29.css","precedence":"next","crossOrigin":"$undefined"}]]}],null]},[["$","$L6",null,{"children":["$","$L3",null,{"parallelRouterKey":"children","segmentPath":["children"],"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"},"children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":{"display":"inline-block","margin":"0 20px 0 0","padding":"0 23px 0 0","fontSize":24,"fontWeight":500,"verticalAlign":"top","lineHeight":"49px"},"children":"404"}],["$","div",null,{"style":{"display":"inline-block"},"children":["$","h2",null,{"style":{"fontSize":14,"fontWeight":400,"lineHeight":"49px","margin":0},"children":"This page could not be found."}]}]]}]}]],"notFoundStyles":[],"styles":null}],"params":{}}],null],null],[[["$","link","0",{"rel":"stylesheet","href":"/_next/static/css/d3df112486f97f47.css","precedence":"next","crossOrigin":"$undefined"}],["$","link","1",{"rel":"stylesheet","href":"/_next/static/css/69f81e06845cd7ae.css","precedence":"next","crossOrigin":"$undefined"}],["$","link","2",{"rel":"stylesheet","href":"/_next/static/css/921ac715acaa8639.css","precedence":"next","crossOrigin":"$undefined"}],["$","link","3",{"rel":"stylesheet","href":"/_next/static/css/c2230015d978ec07.css","precedence":"next","crossOrigin":"$undefined"}]],"$L7"]]]]
8:I[26228,["6648","static/chunks/6648-bd55b61b3b302c00.js","7138","static/chunks/7138-467a231c2eee4048.js","3153","static/chunks/3153-81caed117848c59d.js","193","static/chunks/193-cd63bea46c532713.js","6042","static/chunks/app/%5Bslug%5D/page-069fc419a1df8136.js"],"default"]
b:I[40193,["6648","static/chunks/6648-bd55b61b3b302c00.js","7138","static/chunks/7138-467a231c2eee4048.js","3153","static/chunks/3153-81caed117848c59d.js","193","static/chunks/193-cd63bea46c532713.js","6042","static/chunks/app/%5Bslug%5D/page-069fc419a1df8136.js"],"default"]
c:I[30908,["6648","static/chunks/6648-bd55b61b3b302c00.js","7138","static/chunks/7138-467a231c2eee4048.js","3153","static/chunks/3153-81caed117848c59d.js","193","static/chunks/193-cd63bea46c532713.js","6042","static/chunks/app/%5Bslug%5D/page-069fc419a1df8136.js"],"default"]
9:T1a7b,Embark on a transformative career journey with HN Techno's unparalleled AI, Machine Learning, Data Science & Full Stack Development Training + Internship program in Ahmedabad. This meticulously crafted course provides an immersive, hands-on learning experience, guiding you from the fundamental principles of Core Web Development, Python programming, and SQL basics to the cutting-edge frontiers of Artificial Intelligence.
Dive deep into Data Science Core & Machine Learning pipelines, mastering NumPy, Pandas, Matplotlib, and algorithms like Linear Regression, Decision Trees, SVMs, and Clustering. Progress to Deep Learning, exploring Neural Networks, CNNs, RNNs, and advanced architectures (TensorFlow, PyTorch). Unlock the power of Generative AI with extensive modules on LLMs (GPT, BERT), Prompt Engineering, RAG systems, and AI Agents.
Parallel to AI mastery, you'll gain robust Web Development skills with Django and FastAPI, building dynamic applications and integrating REST APIs. Our unique curriculum emphasizes MLOps, Docker, CI/CD, and Cloud Deployment on AWS, GCP, ensuring you can build, deploy, and manage AI solutions at scale.
Beyond comprehensive instruction, HN Techno guarantees practical, real-time project experience across every module, culminating in multiple capstone projects. The program is uniquely structured to include a valuable industry internship opportunity in Ahmedabad, providing essential local placement support and bridging the gap between learning and a high-demand tech career. Join us offline in Ahmedabad or access our program globally online to become a truly future-ready AI Engineer & Full Stack Professional.
Why this extended description is effective:
-
Keyword Richness: Integrates a vast array of keywords naturally (AI, ML, Data Science, Full Stack, Web Development, Python, Django, SQL, Deep Learning, Generative AI, LLMs, RAG, Agents, MLOps, Cloud Deployment, AWS, GCP, Internship, Ahmedabad, Hands-on, practical, real-time projects, placements).
-
Narrative Flow: Tells a story of progression from basics to advanced, maintaining engagement.
-
Clear Value Proposition: Emphasizes "transformative career journey," "unparalleled," "immersive," "guaranteed local internship," "local placement support," "future-ready."
-
Local Focus Strong: Clearly states "in Ahmedabad" multiple times and highlights "local internship" and "local placement support."
-
Global Reach: Still mentions online global access subtly.
-
Highlights Breadth & Depth: Shows the immense scope of the course, reassuring potential students about the comprehensive nature.
-
Addresses Different Search Intents: Caters to those looking for specific technologies, career outcomes, or practical experience.
a:Taa64,
- Module A – Core Foundations & Basic Web Dev
- Logic – Overview
- Need of Logic
- Functional oriented, pure & partially object oriented
- Desktop, web, mobile, console base application
- Synchronous vs Asynchronous
- Difference b/w LAN And WAN
- Local Environment Setup
- Difference btw C, C++ , Java, php, python,etc.
- Difference btw procedure & object oriented
- Difference btw client-side & serverside scripting
- Create file with “hello” program in 3-4 language
- Variables & Data Types
- Variables & data types in deeply
- Static, dynamic variables
- Primitive & non primitive datatype
- Memory management
- String handling
- Number Methods
- Type casting
- Regular expression
- Date datatype with practical
- Operators
- Math Class
- Need Of Datatype & Primitive, Non Primitive Datatypes
- What is the Need Of Datatypes?
- Need of Datatype
- Explanation Of Primitive Datatype
- Primitive Datatype
- Explanation Of Non Primitive Datatype
- Non primitive Datatype
- Need Of Wrapper Class
- Difference b/w Type Casting And Type Conversion
- Wrapper Class
- Why String Data Type is Biggest?
- Static variable vs dynamic variable
- Basic-task
- Different Methods Of Type Conversion
- parseInt vs Number
- Practical of parseInt vs Number
- parseInt vs Radix
- parseInt with Radix
- Difference b/w parseFloat vs parseInt
- parseFloat vs parseInt
- Ways of String To Number Conversion
- Ways of String To Number Conversion Files
- Date Datatype
- Date Objects
- Date Formats
- Get Date Methods
- Set Date Methods
- Different b/w Function And Method
- Operator And Operation
- Explanation Of Operators and Operations
- Conditional Operators, Conditions & Conditional Statement ....
- Conditional Operator with practically
- Conditional operator files
- What is Condition in practically?
- Condition briefly explanation practical files
- Introduction Of IF...ELSE
- IF_ELSE Explanation practicals
- IF_ELSE practicals - 1
- IF_ELSE briefly practicals - 1
- IF_ELSE briefly practicals - 2
- IF_ELSE briefly practicals - 2
- Need of Nested IF_ELSE with Practical
- Nested IF_ELSE Practicals
- Need Of Ladder IF_ELSE With Practicals
- Conditional Operators, Conditions & Conditional Statement
- Need Of Ladder IF_ELSE With Practicals Files
- Need Of Ladder IF_ELSE With Practicals - 2
- Ladder IF_ELSE Practical files - 2
- System Code in IF_ELSE
- System code in IF_ELSE Files
- Code Compress with Flag in IF_ELSE Practical
- Code Compress with Flag in IF_ELSE Practical files
- Array vs List vs Set
- Array in deep introduction
- Types of array
- Difference btw array & list
- Need of collections (list, set)
- Types of collections (list, set)
- Arrays with all datatypes
- N-dimension Array
- Arrays Methods Practical
- Real time data visualization
- Difference btw List & Set with practical
- Very big practical in above all topics
- Collections, Data Structure
- Types of collections & data structure
- Difference btw datatype & data structure
- Real time data visualization
- Need of collections
- Need of data structure
- Difference btw List & Set with practical
- Using Lists as Stacks
- Using Lists as Queues
- ArrayList, LinkedList, Vector
- HashSet, LinkedHashSet, TreeSet
- Collection’s real time programs
- Nth-dimension Collections
- Types of Maps
- Collections, Data Structure (Continued)
- HashMap, LinkedHashMap, TreeMap & Hashtable
- Maps in real time program
- Real time system with collections
- Very big practical in above all topics
- Loops (Entry & Exit Control)
- Need of Loops
- For loop with multiple practical
- N – dimension for loop
- Random star patterns in for loops
- For with in & of (foreach)
- While loop with multiple practical
- N – dimension while loop
- Break and continue Statements
- Else Clauses on Loops
- Random star patterns in while loop
- Logic buildup with loops
- Conditional Statement
- Need of Conditional Statement
- Simple If..else with multiple practical
- Ladder If..else with multiple practical
- Nested if..else with multiple practical
- Very big practical in above all topics
- Loops, Loops with IF_ELSE Combo, Patterns, Practicals
- Need Of Loop in Practically
- Different Loops Explanation
- Difference b/w Conditional Statement And Conditional Loop
- For Loop Structure and One Dimension For Loop Practical
- Two Dimension For Loop Theoretically
- Two Dimension For Loop Output & Input
- Pattern solutions in for loop part
- Combination of for loop and if_else practical
- Pattern solutions in for loop and if_else
- String Datatype, Static Array, Data structure, Flag Concept, Break Keyword
- Need of Array Data structure with String Datatype
- String Datatype & Data structure, Static & Dynamic Memory Practical
- String Datatype, Static & Dynamic Memory
- Difference B/W String literal and object Practical
- Character array And String Datastructure with practical
- Immutable String with Array swiping Practical
- How To Create String Datatype with Practical
- String with Loop Practically
- Call By Value & Call By Reference
- Replace, Remove, Unique, etc Practicals on String with Index Management
- Break Keyword Practically
- String Datatype, Static Array, Data structure, Flag Concept, Break Keyword (Continued)
- Uses of Flag Practicals
- Random Generate Numbers & Characters practical
- Login & Registration Program using String Data Structure
- List, Sets, Key Value Array
- List methods with practical
- Sets methods with practical
- Key value Array methods
- Sorting Lists
- Lists Iteration
- Lists Const
- N-dimension List with practical
- N-dimension key value Array with practical
- Very big practical in above all topics
- Entry Control (while), Exit Control Loop (do..while), For in, For of Loop, Switch Statement
- Difference B/W IF_ELSE, For & While Loop Practicals
- Difference b/w Entry Control & Exit Control Loop Practicals
- For of Loop with List Practical
- For in Loop with Practical
- Switch Case Statement Practical
- Logic Build-up
- N-dimension all concepts
- Arrays with loops practical
- Arrays, loops, conditional statement practical
- Keywords
- Real time data visualization
- Real time system visualization
- Very big practical in above all topics
- Key Value Array vs Arrays
- Need of key value Array
- Difference btw key value Array vs Arrays
- Key value Array with practical
- N-dimension key value Array practical
- Arrays with key value Array practical
- Key value Array with Arrays practical
- Key value Array iteration
- Real time data visualization
- Real time system visualization
- Very big practical in above all topics
- Types of Function
- Need Of Function
- All types function’s multiple practical
- Local & global variable in function
- Data travels in function
- Run time type casting
- Default parameter function practical
- Real time use of function in application
- Recursion
- Difference btw loop & recursion
- Real time system visualization
- Very big practical in above all topics
- Recursion Algorithm And Practicals
- Need Of Recursion Algorithm With Practical
- How To Work Recursion Algorithm Practical
- Recursion Algorithm Step By Step Explanation Practically
- Apply Recursion Algorithm On Real Time Practical
- Static Keyword
- Need of static keyword
- Static variables
- Static function
- Static block
- Errors, Exception Handling
- Exception handling
- Difference btw Error & Exception
- Raising Exceptions
- Try, catch, throw, throws
- Nested try-catch
- Final keyword, finally block & finalize method
- Very big practical in above all topics
- File Handling
- File Open with practical
- Read Lines with practical
- Close Files with practical
- Create a New File practical
- Write to an Existing File practical
- Delete a File with practical
- Delete Folder with practical
- Class, Object
- Need of Class
- Class with practical
- Class with variables practical
- Class with list practical
- Class with function practical
- One class properties to another class
- Need of object
- Constructor with practical
- Access properties via object
- Run Time Initialization
- Need of Constructor
- Store data one class to another
- Run time initialization via variable
- Run time initialization via function
- This keyword
- Run time initialization via constructor
- Real time system creation with class
- Very big practical in above all topics
- Inheritance
- Need of Inheritance
- Single level Inheritance practical
- Multi level Inheritance practical
- Multiple Inheritance practical
- Hierarchical Inheritance practical
- Hybrid Inheritance practical
- What is extends keyword?
- Difference btw this & super
- Super keyword with practical
- Super constructor with practical
- Properties overriding
- Use inheritance in real system
- Very big practical in above all topics
- Polymorphism
- Need of Polymorphism
- Compile type or function overloading
- Why not support Compile type?
- Run type or function overriding
- Information hiding with practical
- Anonymous object with practical
- Use Polymorphism in real system
- Very big practical in above all topics
- Overloading
- Function overloading with practical
- Constructor overloading with practical
- Abstraction
- Need of Abstraction
- Abstract class with practical
- Abstract function with practical
- Abstraction rules
- Information hiding practical
- Anonymous object practical
- Constructor in abstract class
- Use Abstraction in real system
- Very big practical in above all topics
- Access Modifiers
- Need of Access Modifiers
- Difference btw public, private, protected
- Why default is public?
- Apply access modifiers on properties
- How to data hiding
- Apply security on data
- Use Access Modifiers in real system
- Very big practical in above all topics
- Encapsulation
- Need of Encapsulation
- Private access modifier
- Data hiding with practical
- Setter and getter
- Use Encapsulation in real system
- Very big practical in above all topics
- Interface
- Need of Interface
- Abstract class vs interface
- Multiple inheritance
- Implements keywords
- Extends vs implements
- Information hiding
- Use Interface in real system
- Very big practical in above all topics
- Algorithms & Logic
- Linear Search, Binary Search
- Bubble Sort, Selection Sort
- Recursion & Problem Solving
- Advanced Python
- Generators, Decorators
- *args, **kwargs
- Modules & Packages, Virtual Environments
- Command Line & Linux Basics
- File Operations: ls, cd, mkdir, rm, cat
- Pipes, grep, Environment Variables
- SQL Basics
- CRUD Operations: SELECT, INSERT, UPDATE, DELETE
- Joins: INNER, LEFT, RIGHT, FULL OUTER
- Aggregates: COUNT, SUM, AVG, MIN, MAX
- Transactions & Views
- Module B – Web Development with Django & APIs
- Django Fundamentals
- Django Introduction
- Introduction
- MVT design pattern
- Environment Setup
- pip with practical
- Install Django
- Static vs Dynamic web page
- What is server?
- Client side & Server side scripting
- Http protocol
- Http request & response
- Create “Hello” Django project
- Run-on server with practical
- Explain folder structure with practical
- 02 MVT Framework | Django
- Need Of MVT
- What is MVT Framework?
- Manage.py with practical
- Settings.py with practical
- Urls.py with practical
- Routing with practical
- What is controller or views.py?
- Html Rendering in Controller
- Http request with render
- Template with practical
- Simple Django Project with Html
- 03 Template, Static, Url Mapping
- Html theme integration
- Static load with practical
- Css, js, images load in project
- Multiple URL handling practical
- Url Mapping with Controller
- TemplateView with practical
- Raising a 404 error
- A shortcut: get_object_or_404()
- Real time system creation
- Very big practical with above all concept
- 04 Data Passing One Page to Other
- Need of data transfer
- Dictionaries through data transfer
- Multiple list with dictionaries
- Request & Response
- 06 Master Page
- Need of Master Page
- Include with practical
- Variables in Include
- Extends with practical
- Custom Master Page
- Include vs Extends
- Apply in Real time system
- Very big practical with above all concept
- 05 Django Templates
- Template Variables with practical
- Template Tags with practical
- If Statement with practical
- Conditional Tags with practical
- For Loops with practical
- Loop Variables with practical
- Comments with practical
- Cycles Tag with practical
- Filter Tag with practical
- 07 Django App Creation
- Need of Django App
- Create multiple App with practical
- Module & Submodule with practical
- Include with practical
- Register App in installed app
- Subfolder in templates
- Apply in Real time system
- Very big practical with above all concept
- 08 Database Setup
- Need Of Database
- Sql vs No Sql
- What is Table or Collection?
- Database Setup in settings.py
- Install connectors using pip
- Install database server
- Install database tools
- Database, Table creation
- 09 Models.py
- Need of Models.py
- Field types with practical
- Field options with practical
- Automatic primary key fields
- Verbose field names with practical
- Migrations with practical
- Migrate with practical
- What is primary key?
- What is unique key?
- Use in real time system
- Very big practical with above all concept
- 10 CRUD, Form, File Handling
- Custom Form in Html
- Form handling with views.py
- Manage with models.py
- Difference get & getlist
- Insert data with practical
- File Upload with practical
- Read data with practical
- Update data with practical
- Delete data with practical
- Use in real time system
- Very big practical with above all concept
- 11 forms.py
- Needs forms.py
- Form component & widget
- Binding forms.py & models.py
- Model Form with practical
- Meta class with practical
- Validation with practical
- Css class in widget practical
- forms.py with views.py practical
- forms.py through auto create form
- CRUD with forms.py
- Use in real time system
- Very big practical with above all concept
- 12 Object-Relational Mapper (ORM)
- Needs of ORM
- One To One Relationship practical
- What is Foreign Key with practical?
- Django Queryset with practical
- Database regarding queries practical
- One To Many Relationship practical
- Many To One Relationship practical
- Many To Many Relationship practical
- Use in real time system
- Very big practical with above all concept
- 13 Authentication
- Needs of Authentication
- What is Superuser in django?
- Users.py models with practical
- Custom Authentication practical
- MIDDLEWARE practical
- django.contrib.auth practical
- LOGIN_REDIRECT_URL, LOGIN_URL, PasswordChangeView, LoginView,
- PasswordChangeDoneView with practical
- Customizing Users and authentication
- Use in real time system
- Very big practical with above all concept
- LOGOUT_URL
- LogoutView
- 14 Permissions and Authorization
- Needs of Authorization
- Authentication in web requests
- user.is_authenticated in web page
- @login_required with practical
- Session Tracking with practical
- request.session set & get data
- Use in real time system
- Very big practical with above all concept
- 15 admin.py
- Django Admin Interface practical
- Login, logout Admin Interface
- admin.site.register with practical
- generic.ListView with practical
- Render Model in Django Admin Interface
- Customize Django Admin Interface
- Use in real time system
- Very big practical with above all concept
- 16 RestFramework
- Need of RestFramework
- Installation of restframework
- rest_framework register in INSTALLED_APPS
- rest_framework.response practical
- api_view decorators practical
- Serialization practically
- ModelSerializer with practical
- Serializers class creation practical
- many=True with practical
- JsonResponse with practical
- Response with practical
- Use in real time system
- Very big practical with above all concept
- 17 Database
- Real time queries with practical
- Procedure, events, trigger knowledge
- Real time database knowledge
- FastAPI Fundamentals
- Endpoints: Path & Query Parameters
- CRUD APIs with SQLAlchemy/Database Models
- Pydantic Models for Request/Response
- JWT Authentication
- Async/Await Requests
- REST API Integration
- External API Consumption (Weather, Currency, etc.)
- JSON Parsing, Rendering in Django Templates
- Module C – Data Science Core & ML
- Mathematics
- Algebra: Equations, Inequalities, Functions
- Probability: Random Variables, Bayes’ Theorem, Distributions (Normal, Binomial, Poisson)
- Statistics: Mean, Median, Mode, Variance, Std Dev, Sampling, CLT, Hypothesis Testing
- Linear Algebra: Vectors, Matrices, Multiplication, Eigenvalues/Eigenvectors
- Calculus: Derivatives, Integrals, Partial Derivatives, Gradient Descent Intuition
- Python Libraries
- NumPy: Arrays, Indexing, Broadcasting, Vectorized Operations
- Pandas: Series, DataFrame, Indexing, GroupBy, Merge, Pivot Tables
- Matplotlib/Seaborn: Plots, Charts, Heatmaps, Pairplots
- Module 1: Machine Learning Pipeline
- 1. Data Preprocessing
- ML workflow
- Data Cleaning
- Data Preprocessing in Python
- Feature Scaling
- Feature Extraction
- Feature Engineering
- Feature Selection Techniques
- 2. Exploratory Data Analysis
- Exploratory Data Analysis
- Exploratory Data Analysis in Python
- Advance EDA
- Time Series Data Visualization
- 3. Model Evaluation
- Regularization in Machine Learning
- Confusion Matrix
- Precision, Recall and F1-Score
- AUC-ROC Curve
- Cross-validation
- Hyperparameter Tuning
- Module 2: Supervised Learning
- 1. Linear Regression
- Introduction to Linear Regression
- Gradient Descent in Linear Regression
- Multiple Linear Regression
- 2. Logistic Regression
- Understanding Logistic Regression
- Cost function in Logistic Regression
- 3. Decision Trees
- Decision Tree in Machine Learning
- Types of Decision tree algorithms
- Decision Tree - Regression (Implementation)
- Decision tree - Classification (Implementation)
- 4. Support Vector Machines (SVM)
- Understanding SVMs
- SVM Hyperparameter Tuning - GridSearchCV
- Non-Linear SVM
- 5. k-Nearest Neighbors (k-NN)
- Introduction to KNN
- Decision Boundaries in K-Nearest Neighbors (KNN)
- 6. Naïve Bayes
- Introduction to Naive Bayes
- Gaussian Naive Bayes
- Multinomial Naive Bayes
- Bernoulli Naive Bayes
- Complement Naive Bayes
- 7. Random Forest (Bagging Algorithm)
- Introduction to Random forest
- Random Forest Classifier
- Random Forest Regression
- Hyperparameter Tuning in Random Forest
- Introduction to Ensemble Learning
- Ensemble learning combines multiple simple models to create a stronger, smarter model.
- Bagging: combines multiple models trained independently.
- Boosting: builds models sequentially each correcting the errors of the previous one.
- Module 3: Unsupervised learning
- 1. Clustering
- Clustering algorithms group data points into clusters based on their similarities or differences.
- Centroid-based Methods:
- K-Means clustering
- Elbow Method for optimal value of k in KMeans
- K-Means++ clustering
- K-Mode clustering
- Fuzzy C-Means (FCM) Clustering
- Distribution-based Methods:
- Gaussian mixture models
- Expectation-Maximization Algorithm
- Dirichlet process mixture models (DPMMs)
- Connectivity based methods:
- Hierarchical clustering
- Agglomerative Clustering
- Divisive clustering
- Affinity propagation
- Density Based methods:
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- OPTICS (Ordering Points To Identify the Clustering Structure)
- 2. Dimensionality Reduction
- Dimensionality reduction is used to simplify datasets by reducing the number of features while retaining the most important information.
- Principal Component Analysis (PCA)
- t-distributed Stochastic Neighbor Embedding (t-SNE)
- Non-negative Matrix Factorization (NMF)
- Independent Component Analysis (ICA)
- Isomap
- Locally Linear Embedding (LLE)
- 3. Association Rule
- Find patterns between items in large datasets typically in market basket analysis.
- Apriori algorithm
- Implementing apriori algorithm
- FP-Growth (Frequent Pattern-Growth)
- ECLAT (Equivalence Class Clustering and bottom-up Lattice Traversal)
- Module 4: Reinforcement Learning
- Reinforcement learning interacts with environment and learn from them based on rewards.
- 1. Model-Based Methods
- These methods use a model of the environment to predict outcomes and help the agent plan actions by simulating potential results.
- Markov decision processes (MDPs)
- Bellman equation
- Value iteration algorithm
- Monte Carlo Tree Search
- 2. Model-Free Methods
- The agent learns directly from experience by interacting with the environment and adjusting its actions based on feedback.
- Q-Learning
- SARSA
- Monte Carlo Methods
- Reinforce Algorithm
- Actor-Critic Algorithm
- Asynchronous Advantage Actor-Critic (A3C)
- Module 5: Semi Supervised Learning
- It uses a mix of labeled and unlabeled data making it helpful when labeling data is costly or it is very limited.
- Semi Supervised Classification
- Self-Training in Semi-Supervised Learning
- Few-shot learning in Machine Learning
- Module 6: Forecasting Models
- Forecasting models analyze past data to predict future trends, commonly used for time series problems like sales, demand or stock prices.
- ARIMA (Auto-Regressive Integrated Moving Average)
- SARIMA (Seasonal ARIMA)
- Exponential Smoothing (Holt-Winters)
- Module 7: Deployment of ML Models
- The trained ML model must be integrated into an application or service to make its predictions accessible.
- Machine learning deployment
- Deploy ML Model using Streamlit Library
- Deploy ML web app on Heroku
- Create UIs for prototyping Machine Learning model with Gradio
- APIs allow other applications or systems to access the ML model's functionality and integrate them into larger workflows.
- Deploy Machine Learning Model using Flask
- Deploying ML Models as API using FastAPI
- MLOps ensure they are deployed, monitored and maintained efficiently in real-world production systems.
- MLOps
- Continuous Integration and Continuous Deployment (CI/CD) in MLOps
- End-to-End MLOps
- Module D – Deep Learning
- Introduction to Neural Networks
- Neural Networks
- Biological Neurons vs Artificial Neurons
- Single Layer Perceptron
- Multi-Layer Perceptron
- Artificial Neural Networks (ANNs)
- Types of Neural Networks
- Architecture and Learning process in neural network
- Basic Components of Neural Networks
- Layers in Neural Networks
- Weights and Biases
- Forward Propagation
- Activation Functions
- Loss Functions
- Backpropagation
- Learning Rate
- Optimization Algorithm in Deep Learning
- Optimization algorithms in deep learning
- Gradient Descent
- Stochastic Gradient Descent (SGD)
- Batch Normalization
- Mini-batch Gradient Descent
- Adam (Adaptive Moment Estimation)
- Momentum-based Gradient Optimizer
- Adagrad Optimizer
- RMSProp Optimizer
- Deep Learning Frameworks (TensorFlow, PyTorch, Keras)
- Types of Deep Learning Models
- 1. Convolutional Neural Networks (CNNs)
- Deep Learning Algorithms
- Convolutional Neural Networks (CNNs)
- Basics of Digital Image Processing
- Importance for CNN
- Padding
- Convolutional Layers
- Pooling Layers
- Fully Connected Layers
- Backpropagation in CNNs
- CNN based Image Classification using PyTorch
- CNN based Images Classification using TensorFlow
- CNN Based Architectures:
- Convolutional Neural Network (CNN) Architectures
- LeNet-5
- AlexNet
- VGGnet
- VGG-16 Network
- GoogLeNet/Inception
- ResNet (Residual Network)
- MobileNet
- 2. Recurrent Neural Networks (RNNs)
- Recurrent Neural Networks (RNNs)
- How RNN Differs from Feedforward Neural Networks
- Backpropagation Through Time (BPTT)
- Vanishing Gradient and Exploding Gradient Problem
- Training of RNN in TensorFlow
- Sentiment Analysis with RNN
- Types of Recurrent Neural Networks:
- Types of Recurrent Neural Networks
- Bidirectional RNNs
- Long Short-Term Memory (LSTM)
- Bidirectional Long Short-Term Memory (Bi-LSTM)
- Gated Recurrent Units (GRU)
- 3. Generative Models in Deep Learning
- Generative Adversarial Networks (GANs)
- Autoencoders
- GAN vs. Transformer Models
- Types of Generative Adversarial Networks (GANs):
- Deep Convolutional GAN (DCGAN)
- Conditional GAN (cGAN)
- Cycle-Consistent GAN (CycleGAN)
- Super-Resolution GAN (SRGAN)
- StyleGAN
- Types of Autoencoders:
- Types of Autoencoders
- Sparse Autoencoder
- Denoising Autoencoder
- Convolutional Autoencoder
- Variational Autoencoder
- 4. Deep Reinforcement Learning
- Deep Reinforcement Learning (DRL)
- Deep Reinforcement Learning
- Reinforcement Learning
- Markov Decision Processes
- Key Algorithms in Deep Reinforcement Learning
- Deep Q-Networks (DQN)
- REINFORCE
- Actor-Critic Methods
- Proximal Policy Optimization (PPO)
- Module E – Artificial Intelligence
- Types of Artificial Intelligence
- Types of AI Based on Capabilities
- Types of AI Based on Functionalities
- What is an AI Agent?
- AI agent
- Types of AI Agents
- Problem Solving in AI
- 1. Search Algorithms in AI
- Search algorithms
- Breadth-First Search (BFS)
- Depth-First Search (DFS)
- Uniform Cost Search (UCS)
- Bidirectional search
- Greedy Best-First Search
- A Search* Algorithm
- 2. Local Search Algorithms
- Local search algorithms
- Hill-Climbing Search Algorithm
- Local Beam Search
- 3. Adversarial Search in AI
- Adversarial search
- Minimax Algorithm
- Alpha-Beta Pruning
- 4. Constraint Satisfaction Problems
- Constraint Satisfaction Problem (CSP)
- Constraint Propagation in CSP’s
- Backtracking Search for CSP’s
- Knowledge, Reasoning and Planning in AI
- Knowledge Representation:
- Knowledge representation in Artificial Intelligence (AI)
- Semantic Networks
- Frames
- Ontologies
- Logical Representation
- First Order Logic in Artificial Intelligence:
- First Order Logic (FOL)
- Knowledge Representation in First Order Logic
- Syntax and Semantics of First Order Logic
- Inference Rules in First Order Logic
- Reasoning in Artificial Intelligence:
- Reasoning in Artificial Intelligence (AI)
- Types of Reasoning in AI
- Deductive Reasoning
- Inductive Reasoning
- Abductive Reasoning
- Fuzzy Reasoning
- Planning in AI:
- Planning in AI
- Forward State Space Search
- Markov Decision Processes (MDPs)
- Hierarchical State Space Search (HSSS)
- Uncertain Knowledge and Reasoning:
- Uncertain Knowledge and Reasoning in AI
- Dempster-Shafer Theory
- Probabilistic Reasoning
- Fuzzy Logic
- Neural Networks with dropout
- Types of Learning in AI
- 1. Supervised Learning
- Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees
- Support Vector Machines (SVM)
- k-Nearest Neighbors
- Naïve Bayes
- Random Forests
- 2. Semi-supervised learning
- 3. Unsupervised Learning
- Unsupervised Learning
- K-Means Clustering
- Principal Component Analysis (PCA)
- Hierarchical Clustering
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- 4. Reinforcement Learning
- Reinforcement Learning
- Q-Learning
- Deep Q-Networks (DQN)
- Markov decision processes (MDPs)
- Bellman equation
- 5. Deep Learning
- Deep Learning
- Neurons
- Single Layer Perceptron
- Multi-Layer Perceptron
- Artificial Neural Networks (ANNs)
- Feedforward Neural Networks (FNN)
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM) networks
- Gated Recurrent Units Networks (GRU)
- Probabilistic models
- Probabilistic models
- Naive Bayes Classifier
- Monte Carlo Methods
- Expectation-Maximization (EM) Algorithm
- Communication, Perceiving and Acting in AI and Robotics
- 1. Natural Language Processing (NLP)
- Speech Recognition
- Natural Language Generation
- Chatbots
- Machine Translation
- 2. Computer Vision
- Image Recognition
- Facial Recognition
- Optical Character Recognition
- 3. Robotics
- Generative AI
- Large Language Models
- GPT (Generative Pre-trained Transformer)
- BERT (Bidirectional Encoder Representations from Transformers)
- T5 (Text-to-Text Transfer Transformer)
- Conditional GAN (cGAN)
- CycleGAN
- Style GANs
- Module F – Generative AI
- LLMs: GPT, BERT, LLaMA, Tokenization, Embeddings
- Prompt Engineering: Zero-Shot, Few-Shot, Chain-of-Thought, Prompt Tuning
- Fine-Tuning & Transfer Learning: LoRA, Pretrained Model Adaptation
- Generative Tasks
- Text: Chatbots, Summarization, Translation
- Image: GANs, Diffusion Models
- Frameworks: HuggingFace, LangChain
- 1. Tools for Generative AI
- To get started with Generative AI, you need to build expertise in the following tools and libraries:
- Python
- PyTorch
- TensorFlow
- Hugging Face Transformers
- LangChain
- LangGraph
- Langflow
- LlamaIndex
- Integration of Langchain with Llama-Index
- 2. Core Concepts in Generative AI
- Understanding the foundations of AI and deep learning is essential for working with GenAI models.
- What is Artificial Intelligence?
- What is Generative AI?
- Neural Networks
- RNNs, LSTMs, GRUs
- Transformers and Self-Attention
- Autoencoders and Latent Space
- GANs and Diffusion Models
- 3. Natural Language Processing (NLP) Basics
- Most Generative AI models are built on NLP concepts.
- Text Preprocessing in NLP
- Bag of Words & TF-IDF
- Word2Vec & GloVe
- Introduction to BERT
- Introduction to GPT Models
- Hugging Face Models
- 4. Prompt Engineering
- Prompt engineering is the practice of crafting inputs to get better outputs from LLMs.
- What is Prompt Engineering?
- Zero-Shot, One-Shot and Few-Shot Prompting
- Chain of Thought Prompting
- Role & Contextual Prompting
- ReAct (Reasoning + Acting) Prompting
- Retrieval-Augmented Prompting
- Self-Consistency Prompting
- Tree of Thought (ToT) prompting
- Guardrails in AI
- 5. Large Language Models (LLMs)
- LLMs are the backbone of modern Generative AI systems.
- Large Language Model
- LLM Parameters
- Scaling Laws in LLMs
- Fine-Tuning LLMs with LoRA, QLoRA and PEFT
- RLHF: Reinforcement Learning from Human Feedback
- LLM Distillation
- Popular LLMs: GPT, Claude, LLaMA, Gemini
- LLM APIs: OpenAI, Hugging Face, Gemini
- 6. Retrieval-Augmented Generation (RAG)
- RAG combines LLMs with external knowledge sources for more accurate responses.
- RAG in AI
- RAG Architecture
- Multimodal RAG
- Embeddings
- Vector Databases: FAISS, ChromaDB, Qdrant, Pinecone
- RAG System with Langchain and Langraph
- 7. Agentic AI & Multi-Agent Systems
- Agentic AI extends LLMs with autonomy, memory and collaboration.
- What is Agentic AI?
- Agent vs Traditional AI
- Agent Architectures & Memory
- Agent-to-Agent Communication
- AI Agent Frameworks
- Model Context Protocol (MCP)
- 8. CrewAI and Orchestration
- CrewAI is a framework for coordinating multiple AI agents to work collaboratively.
- Introduction to CrewAI
- CrewAI Tools
- Creating Custom Tools for CrewAI
- Memory in CrewAI
- CrewAI Embeddings
- CrewAI Collaboration
- CrewAI Knowledge
- CrewAI Planning and Reasoning
- CrewAI CLI
- CrewAI Flow
- Fraud Detection Using CrewAI Project
- 9. Automation with Agents and Deployment
- Generative AI can be extended into workflows for business automation.
- Agentic RAG
- Agentic RAG with LlamaIndex
- Introduction to n8n
- Automated Email Classifier with n8n
- AI Deployment with Gradio, Streamlit, FastAPI
- 10. Responsible & Ethical AI
- Generative and Agentic AI raise ethical challenges that must be addressed.
- Bias in AI Models
- Deepfakes
- Prompt Injection in LLM
- Responsible AI Practices
- Module G – Integration, MLOps & Cloud Deployment
- MLOps:
- DVC
- MLflow
- Docker
- CI/CD Concepts
- Cloud Deployment:
- AWS (S3, EC2, Lambda)
- GCP (Vertex AI)
- Azure ML
- Capstone Projects:
- Predictive Analytics
- Recommender System
- AI Chatbot
- Computer Vision App
- End-to-End Django + ML Deployment
e:{"$oid":"68e63d01d03dba04aaae285f"}
f:T1a7b,Embark on a transformative career journey with HN Techno's unparalleled AI, Machine Learning, Data Science & Full Stack Development Training + Internship program in Ahmedabad. This meticulously crafted course provides an immersive, hands-on learning experience, guiding you from the fundamental principles of Core Web Development, Python programming, and SQL basics to the cutting-edge frontiers of Artificial Intelligence.
Dive deep into Data Science Core & Machine Learning pipelines, mastering NumPy, Pandas, Matplotlib, and algorithms like Linear Regression, Decision Trees, SVMs, and Clustering. Progress to Deep Learning, exploring Neural Networks, CNNs, RNNs, and advanced architectures (TensorFlow, PyTorch). Unlock the power of Generative AI with extensive modules on LLMs (GPT, BERT), Prompt Engineering, RAG systems, and AI Agents.
Parallel to AI mastery, you'll gain robust Web Development skills with Django and FastAPI, building dynamic applications and integrating REST APIs. Our unique curriculum emphasizes MLOps, Docker, CI/CD, and Cloud Deployment on AWS, GCP, ensuring you can build, deploy, and manage AI solutions at scale.
Beyond comprehensive instruction, HN Techno guarantees practical, real-time project experience across every module, culminating in multiple capstone projects. The program is uniquely structured to include a valuable industry internship opportunity in Ahmedabad, providing essential local placement support and bridging the gap between learning and a high-demand tech career. Join us offline in Ahmedabad or access our program globally online to become a truly future-ready AI Engineer & Full Stack Professional.
Why this extended description is effective:
-
Keyword Richness: Integrates a vast array of keywords naturally (AI, ML, Data Science, Full Stack, Web Development, Python, Django, SQL, Deep Learning, Generative AI, LLMs, RAG, Agents, MLOps, Cloud Deployment, AWS, GCP, Internship, Ahmedabad, Hands-on, practical, real-time projects, placements).
-
Narrative Flow: Tells a story of progression from basics to advanced, maintaining engagement.
-
Clear Value Proposition: Emphasizes "transformative career journey," "unparalleled," "immersive," "guaranteed local internship," "local placement support," "future-ready."
-
Local Focus Strong: Clearly states "in Ahmedabad" multiple times and highlights "local internship" and "local placement support."
-
Global Reach: Still mentions online global access subtly.
-
Highlights Breadth & Depth: Shows the immense scope of the course, reassuring potential students about the comprehensive nature.
-
Addresses Different Search Intents: Caters to those looking for specific technologies, career outcomes, or practical experience.
10:Taa64,
- Module A – Core Foundations & Basic Web Dev
- Logic – Overview
- Need of Logic
- Functional oriented, pure & partially object oriented
- Desktop, web, mobile, console base application
- Synchronous vs Asynchronous
- Difference b/w LAN And WAN
- Local Environment Setup
- Difference btw C, C++ , Java, php, python,etc.
- Difference btw procedure & object oriented
- Difference btw client-side & serverside scripting
- Create file with “hello” program in 3-4 language
- Variables & Data Types
- Variables & data types in deeply
- Static, dynamic variables
- Primitive & non primitive datatype
- Memory management
- String handling
- Number Methods
- Type casting
- Regular expression
- Date datatype with practical
- Operators
- Math Class
- Need Of Datatype & Primitive, Non Primitive Datatypes
- What is the Need Of Datatypes?
- Need of Datatype
- Explanation Of Primitive Datatype
- Primitive Datatype
- Explanation Of Non Primitive Datatype
- Non primitive Datatype
- Need Of Wrapper Class
- Difference b/w Type Casting And Type Conversion
- Wrapper Class
- Why String Data Type is Biggest?
- Static variable vs dynamic variable
- Basic-task
- Different Methods Of Type Conversion
- parseInt vs Number
- Practical of parseInt vs Number
- parseInt vs Radix
- parseInt with Radix
- Difference b/w parseFloat vs parseInt
- parseFloat vs parseInt
- Ways of String To Number Conversion
- Ways of String To Number Conversion Files
- Date Datatype
- Date Objects
- Date Formats
- Get Date Methods
- Set Date Methods
- Different b/w Function And Method
- Operator And Operation
- Explanation Of Operators and Operations
- Conditional Operators, Conditions & Conditional Statement ....
- Conditional Operator with practically
- Conditional operator files
- What is Condition in practically?
- Condition briefly explanation practical files
- Introduction Of IF...ELSE
- IF_ELSE Explanation practicals
- IF_ELSE practicals - 1
- IF_ELSE briefly practicals - 1
- IF_ELSE briefly practicals - 2
- IF_ELSE briefly practicals - 2
- Need of Nested IF_ELSE with Practical
- Nested IF_ELSE Practicals
- Need Of Ladder IF_ELSE With Practicals
- Conditional Operators, Conditions & Conditional Statement
- Need Of Ladder IF_ELSE With Practicals Files
- Need Of Ladder IF_ELSE With Practicals - 2
- Ladder IF_ELSE Practical files - 2
- System Code in IF_ELSE
- System code in IF_ELSE Files
- Code Compress with Flag in IF_ELSE Practical
- Code Compress with Flag in IF_ELSE Practical files
- Array vs List vs Set
- Array in deep introduction
- Types of array
- Difference btw array & list
- Need of collections (list, set)
- Types of collections (list, set)
- Arrays with all datatypes
- N-dimension Array
- Arrays Methods Practical
- Real time data visualization
- Difference btw List & Set with practical
- Very big practical in above all topics
- Collections, Data Structure
- Types of collections & data structure
- Difference btw datatype & data structure
- Real time data visualization
- Need of collections
- Need of data structure
- Difference btw List & Set with practical
- Using Lists as Stacks
- Using Lists as Queues
- ArrayList, LinkedList, Vector
- HashSet, LinkedHashSet, TreeSet
- Collection’s real time programs
- Nth-dimension Collections
- Types of Maps
- Collections, Data Structure (Continued)
- HashMap, LinkedHashMap, TreeMap & Hashtable
- Maps in real time program
- Real time system with collections
- Very big practical in above all topics
- Loops (Entry & Exit Control)
- Need of Loops
- For loop with multiple practical
- N – dimension for loop
- Random star patterns in for loops
- For with in & of (foreach)
- While loop with multiple practical
- N – dimension while loop
- Break and continue Statements
- Else Clauses on Loops
- Random star patterns in while loop
- Logic buildup with loops
- Conditional Statement
- Need of Conditional Statement
- Simple If..else with multiple practical
- Ladder If..else with multiple practical
- Nested if..else with multiple practical
- Very big practical in above all topics
- Loops, Loops with IF_ELSE Combo, Patterns, Practicals
- Need Of Loop in Practically
- Different Loops Explanation
- Difference b/w Conditional Statement And Conditional Loop
- For Loop Structure and One Dimension For Loop Practical
- Two Dimension For Loop Theoretically
- Two Dimension For Loop Output & Input
- Pattern solutions in for loop part
- Combination of for loop and if_else practical
- Pattern solutions in for loop and if_else
- String Datatype, Static Array, Data structure, Flag Concept, Break Keyword
- Need of Array Data structure with String Datatype
- String Datatype & Data structure, Static & Dynamic Memory Practical
- String Datatype, Static & Dynamic Memory
- Difference B/W String literal and object Practical
- Character array And String Datastructure with practical
- Immutable String with Array swiping Practical
- How To Create String Datatype with Practical
- String with Loop Practically
- Call By Value & Call By Reference
- Replace, Remove, Unique, etc Practicals on String with Index Management
- Break Keyword Practically
- String Datatype, Static Array, Data structure, Flag Concept, Break Keyword (Continued)
- Uses of Flag Practicals
- Random Generate Numbers & Characters practical
- Login & Registration Program using String Data Structure
- List, Sets, Key Value Array
- List methods with practical
- Sets methods with practical
- Key value Array methods
- Sorting Lists
- Lists Iteration
- Lists Const
- N-dimension List with practical
- N-dimension key value Array with practical
- Very big practical in above all topics
- Entry Control (while), Exit Control Loop (do..while), For in, For of Loop, Switch Statement
- Difference B/W IF_ELSE, For & While Loop Practicals
- Difference b/w Entry Control & Exit Control Loop Practicals
- For of Loop with List Practical
- For in Loop with Practical
- Switch Case Statement Practical
- Logic Build-up
- N-dimension all concepts
- Arrays with loops practical
- Arrays, loops, conditional statement practical
- Keywords
- Real time data visualization
- Real time system visualization
- Very big practical in above all topics
- Key Value Array vs Arrays
- Need of key value Array
- Difference btw key value Array vs Arrays
- Key value Array with practical
- N-dimension key value Array practical
- Arrays with key value Array practical
- Key value Array with Arrays practical
- Key value Array iteration
- Real time data visualization
- Real time system visualization
- Very big practical in above all topics
- Types of Function
- Need Of Function
- All types function’s multiple practical
- Local & global variable in function
- Data travels in function
- Run time type casting
- Default parameter function practical
- Real time use of function in application
- Recursion
- Difference btw loop & recursion
- Real time system visualization
- Very big practical in above all topics
- Recursion Algorithm And Practicals
- Need Of Recursion Algorithm With Practical
- How To Work Recursion Algorithm Practical
- Recursion Algorithm Step By Step Explanation Practically
- Apply Recursion Algorithm On Real Time Practical
- Static Keyword
- Need of static keyword
- Static variables
- Static function
- Static block
- Errors, Exception Handling
- Exception handling
- Difference btw Error & Exception
- Raising Exceptions
- Try, catch, throw, throws
- Nested try-catch
- Final keyword, finally block & finalize method
- Very big practical in above all topics
- File Handling
- File Open with practical
- Read Lines with practical
- Close Files with practical
- Create a New File practical
- Write to an Existing File practical
- Delete a File with practical
- Delete Folder with practical
- Class, Object
- Need of Class
- Class with practical
- Class with variables practical
- Class with list practical
- Class with function practical
- One class properties to another class
- Need of object
- Constructor with practical
- Access properties via object
- Run Time Initialization
- Need of Constructor
- Store data one class to another
- Run time initialization via variable
- Run time initialization via function
- This keyword
- Run time initialization via constructor
- Real time system creation with class
- Very big practical in above all topics
- Inheritance
- Need of Inheritance
- Single level Inheritance practical
- Multi level Inheritance practical
- Multiple Inheritance practical
- Hierarchical Inheritance practical
- Hybrid Inheritance practical
- What is extends keyword?
- Difference btw this & super
- Super keyword with practical
- Super constructor with practical
- Properties overriding
- Use inheritance in real system
- Very big practical in above all topics
- Polymorphism
- Need of Polymorphism
- Compile type or function overloading
- Why not support Compile type?
- Run type or function overriding
- Information hiding with practical
- Anonymous object with practical
- Use Polymorphism in real system
- Very big practical in above all topics
- Overloading
- Function overloading with practical
- Constructor overloading with practical
- Abstraction
- Need of Abstraction
- Abstract class with practical
- Abstract function with practical
- Abstraction rules
- Information hiding practical
- Anonymous object practical
- Constructor in abstract class
- Use Abstraction in real system
- Very big practical in above all topics
- Access Modifiers
- Need of Access Modifiers
- Difference btw public, private, protected
- Why default is public?
- Apply access modifiers on properties
- How to data hiding
- Apply security on data
- Use Access Modifiers in real system
- Very big practical in above all topics
- Encapsulation
- Need of Encapsulation
- Private access modifier
- Data hiding with practical
- Setter and getter
- Use Encapsulation in real system
- Very big practical in above all topics
- Interface
- Need of Interface
- Abstract class vs interface
- Multiple inheritance
- Implements keywords
- Extends vs implements
- Information hiding
- Use Interface in real system
- Very big practical in above all topics
- Algorithms & Logic
- Linear Search, Binary Search
- Bubble Sort, Selection Sort
- Recursion & Problem Solving
- Advanced Python
- Generators, Decorators
- *args, **kwargs
- Modules & Packages, Virtual Environments
- Command Line & Linux Basics
- File Operations: ls, cd, mkdir, rm, cat
- Pipes, grep, Environment Variables
- SQL Basics
- CRUD Operations: SELECT, INSERT, UPDATE, DELETE
- Joins: INNER, LEFT, RIGHT, FULL OUTER
- Aggregates: COUNT, SUM, AVG, MIN, MAX
- Transactions & Views
- Module B – Web Development with Django & APIs
- Django Fundamentals
- Django Introduction
- Introduction
- MVT design pattern
- Environment Setup
- pip with practical
- Install Django
- Static vs Dynamic web page
- What is server?
- Client side & Server side scripting
- Http protocol
- Http request & response
- Create “Hello” Django project
- Run-on server with practical
- Explain folder structure with practical
- 02 MVT Framework | Django
- Need Of MVT
- What is MVT Framework?
- Manage.py with practical
- Settings.py with practical
- Urls.py with practical
- Routing with practical
- What is controller or views.py?
- Html Rendering in Controller
- Http request with render
- Template with practical
- Simple Django Project with Html
- 03 Template, Static, Url Mapping
- Html theme integration
- Static load with practical
- Css, js, images load in project
- Multiple URL handling practical
- Url Mapping with Controller
- TemplateView with practical
- Raising a 404 error
- A shortcut: get_object_or_404()
- Real time system creation
- Very big practical with above all concept
- 04 Data Passing One Page to Other
- Need of data transfer
- Dictionaries through data transfer
- Multiple list with dictionaries
- Request & Response
- 06 Master Page
- Need of Master Page
- Include with practical
- Variables in Include
- Extends with practical
- Custom Master Page
- Include vs Extends
- Apply in Real time system
- Very big practical with above all concept
- 05 Django Templates
- Template Variables with practical
- Template Tags with practical
- If Statement with practical
- Conditional Tags with practical
- For Loops with practical
- Loop Variables with practical
- Comments with practical
- Cycles Tag with practical
- Filter Tag with practical
- 07 Django App Creation
- Need of Django App
- Create multiple App with practical
- Module & Submodule with practical
- Include with practical
- Register App in installed app
- Subfolder in templates
- Apply in Real time system
- Very big practical with above all concept
- 08 Database Setup
- Need Of Database
- Sql vs No Sql
- What is Table or Collection?
- Database Setup in settings.py
- Install connectors using pip
- Install database server
- Install database tools
- Database, Table creation
- 09 Models.py
- Need of Models.py
- Field types with practical
- Field options with practical
- Automatic primary key fields
- Verbose field names with practical
- Migrations with practical
- Migrate with practical
- What is primary key?
- What is unique key?
- Use in real time system
- Very big practical with above all concept
- 10 CRUD, Form, File Handling
- Custom Form in Html
- Form handling with views.py
- Manage with models.py
- Difference get & getlist
- Insert data with practical
- File Upload with practical
- Read data with practical
- Update data with practical
- Delete data with practical
- Use in real time system
- Very big practical with above all concept
- 11 forms.py
- Needs forms.py
- Form component & widget
- Binding forms.py & models.py
- Model Form with practical
- Meta class with practical
- Validation with practical
- Css class in widget practical
- forms.py with views.py practical
- forms.py through auto create form
- CRUD with forms.py
- Use in real time system
- Very big practical with above all concept
- 12 Object-Relational Mapper (ORM)
- Needs of ORM
- One To One Relationship practical
- What is Foreign Key with practical?
- Django Queryset with practical
- Database regarding queries practical
- One To Many Relationship practical
- Many To One Relationship practical
- Many To Many Relationship practical
- Use in real time system
- Very big practical with above all concept
- 13 Authentication
- Needs of Authentication
- What is Superuser in django?
- Users.py models with practical
- Custom Authentication practical
- MIDDLEWARE practical
- django.contrib.auth practical
- LOGIN_REDIRECT_URL, LOGIN_URL, PasswordChangeView, LoginView,
- PasswordChangeDoneView with practical
- Customizing Users and authentication
- Use in real time system
- Very big practical with above all concept
- LOGOUT_URL
- LogoutView
- 14 Permissions and Authorization
- Needs of Authorization
- Authentication in web requests
- user.is_authenticated in web page
- @login_required with practical
- Session Tracking with practical
- request.session set & get data
- Use in real time system
- Very big practical with above all concept
- 15 admin.py
- Django Admin Interface practical
- Login, logout Admin Interface
- admin.site.register with practical
- generic.ListView with practical
- Render Model in Django Admin Interface
- Customize Django Admin Interface
- Use in real time system
- Very big practical with above all concept
- 16 RestFramework
- Need of RestFramework
- Installation of restframework
- rest_framework register in INSTALLED_APPS
- rest_framework.response practical
- api_view decorators practical
- Serialization practically
- ModelSerializer with practical
- Serializers class creation practical
- many=True with practical
- JsonResponse with practical
- Response with practical
- Use in real time system
- Very big practical with above all concept
- 17 Database
- Real time queries with practical
- Procedure, events, trigger knowledge
- Real time database knowledge
- FastAPI Fundamentals
- Endpoints: Path & Query Parameters
- CRUD APIs with SQLAlchemy/Database Models
- Pydantic Models for Request/Response
- JWT Authentication
- Async/Await Requests
- REST API Integration
- External API Consumption (Weather, Currency, etc.)
- JSON Parsing, Rendering in Django Templates
- Module C – Data Science Core & ML
- Mathematics
- Algebra: Equations, Inequalities, Functions
- Probability: Random Variables, Bayes’ Theorem, Distributions (Normal, Binomial, Poisson)
- Statistics: Mean, Median, Mode, Variance, Std Dev, Sampling, CLT, Hypothesis Testing
- Linear Algebra: Vectors, Matrices, Multiplication, Eigenvalues/Eigenvectors
- Calculus: Derivatives, Integrals, Partial Derivatives, Gradient Descent Intuition
- Python Libraries
- NumPy: Arrays, Indexing, Broadcasting, Vectorized Operations
- Pandas: Series, DataFrame, Indexing, GroupBy, Merge, Pivot Tables
- Matplotlib/Seaborn: Plots, Charts, Heatmaps, Pairplots
- Module 1: Machine Learning Pipeline
- 1. Data Preprocessing
- ML workflow
- Data Cleaning
- Data Preprocessing in Python
- Feature Scaling
- Feature Extraction
- Feature Engineering
- Feature Selection Techniques
- 2. Exploratory Data Analysis
- Exploratory Data Analysis
- Exploratory Data Analysis in Python
- Advance EDA
- Time Series Data Visualization
- 3. Model Evaluation
- Regularization in Machine Learning
- Confusion Matrix
- Precision, Recall and F1-Score
- AUC-ROC Curve
- Cross-validation
- Hyperparameter Tuning
- Module 2: Supervised Learning
- 1. Linear Regression
- Introduction to Linear Regression
- Gradient Descent in Linear Regression
- Multiple Linear Regression
- 2. Logistic Regression
- Understanding Logistic Regression
- Cost function in Logistic Regression
- 3. Decision Trees
- Decision Tree in Machine Learning
- Types of Decision tree algorithms
- Decision Tree - Regression (Implementation)
- Decision tree - Classification (Implementation)
- 4. Support Vector Machines (SVM)
- Understanding SVMs
- SVM Hyperparameter Tuning - GridSearchCV
- Non-Linear SVM
- 5. k-Nearest Neighbors (k-NN)
- Introduction to KNN
- Decision Boundaries in K-Nearest Neighbors (KNN)
- 6. Naïve Bayes
- Introduction to Naive Bayes
- Gaussian Naive Bayes
- Multinomial Naive Bayes
- Bernoulli Naive Bayes
- Complement Naive Bayes
- 7. Random Forest (Bagging Algorithm)
- Introduction to Random forest
- Random Forest Classifier
- Random Forest Regression
- Hyperparameter Tuning in Random Forest
- Introduction to Ensemble Learning
- Ensemble learning combines multiple simple models to create a stronger, smarter model.
- Bagging: combines multiple models trained independently.
- Boosting: builds models sequentially each correcting the errors of the previous one.
- Module 3: Unsupervised learning
- 1. Clustering
- Clustering algorithms group data points into clusters based on their similarities or differences.
- Centroid-based Methods:
- K-Means clustering
- Elbow Method for optimal value of k in KMeans
- K-Means++ clustering
- K-Mode clustering
- Fuzzy C-Means (FCM) Clustering
- Distribution-based Methods:
- Gaussian mixture models
- Expectation-Maximization Algorithm
- Dirichlet process mixture models (DPMMs)
- Connectivity based methods:
- Hierarchical clustering
- Agglomerative Clustering
- Divisive clustering
- Affinity propagation
- Density Based methods:
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- OPTICS (Ordering Points To Identify the Clustering Structure)
- 2. Dimensionality Reduction
- Dimensionality reduction is used to simplify datasets by reducing the number of features while retaining the most important information.
- Principal Component Analysis (PCA)
- t-distributed Stochastic Neighbor Embedding (t-SNE)
- Non-negative Matrix Factorization (NMF)
- Independent Component Analysis (ICA)
- Isomap
- Locally Linear Embedding (LLE)
- 3. Association Rule
- Find patterns between items in large datasets typically in market basket analysis.
- Apriori algorithm
- Implementing apriori algorithm
- FP-Growth (Frequent Pattern-Growth)
- ECLAT (Equivalence Class Clustering and bottom-up Lattice Traversal)
- Module 4: Reinforcement Learning
- Reinforcement learning interacts with environment and learn from them based on rewards.
- 1. Model-Based Methods
- These methods use a model of the environment to predict outcomes and help the agent plan actions by simulating potential results.
- Markov decision processes (MDPs)
- Bellman equation
- Value iteration algorithm
- Monte Carlo Tree Search
- 2. Model-Free Methods
- The agent learns directly from experience by interacting with the environment and adjusting its actions based on feedback.
- Q-Learning
- SARSA
- Monte Carlo Methods
- Reinforce Algorithm
- Actor-Critic Algorithm
- Asynchronous Advantage Actor-Critic (A3C)
- Module 5: Semi Supervised Learning
- It uses a mix of labeled and unlabeled data making it helpful when labeling data is costly or it is very limited.
- Semi Supervised Classification
- Self-Training in Semi-Supervised Learning
- Few-shot learning in Machine Learning
- Module 6: Forecasting Models
- Forecasting models analyze past data to predict future trends, commonly used for time series problems like sales, demand or stock prices.
- ARIMA (Auto-Regressive Integrated Moving Average)
- SARIMA (Seasonal ARIMA)
- Exponential Smoothing (Holt-Winters)
- Module 7: Deployment of ML Models
- The trained ML model must be integrated into an application or service to make its predictions accessible.
- Machine learning deployment
- Deploy ML Model using Streamlit Library
- Deploy ML web app on Heroku
- Create UIs for prototyping Machine Learning model with Gradio
- APIs allow other applications or systems to access the ML model's functionality and integrate them into larger workflows.
- Deploy Machine Learning Model using Flask
- Deploying ML Models as API using FastAPI
- MLOps ensure they are deployed, monitored and maintained efficiently in real-world production systems.
- MLOps
- Continuous Integration and Continuous Deployment (CI/CD) in MLOps
- End-to-End MLOps
- Module D – Deep Learning
- Introduction to Neural Networks
- Neural Networks
- Biological Neurons vs Artificial Neurons
- Single Layer Perceptron
- Multi-Layer Perceptron
- Artificial Neural Networks (ANNs)
- Types of Neural Networks
- Architecture and Learning process in neural network
- Basic Components of Neural Networks
- Layers in Neural Networks
- Weights and Biases
- Forward Propagation
- Activation Functions
- Loss Functions
- Backpropagation
- Learning Rate
- Optimization Algorithm in Deep Learning
- Optimization algorithms in deep learning
- Gradient Descent
- Stochastic Gradient Descent (SGD)
- Batch Normalization
- Mini-batch Gradient Descent
- Adam (Adaptive Moment Estimation)
- Momentum-based Gradient Optimizer
- Adagrad Optimizer
- RMSProp Optimizer
- Deep Learning Frameworks (TensorFlow, PyTorch, Keras)
- Types of Deep Learning Models
- 1. Convolutional Neural Networks (CNNs)
- Deep Learning Algorithms
- Convolutional Neural Networks (CNNs)
- Basics of Digital Image Processing
- Importance for CNN
- Padding
- Convolutional Layers
- Pooling Layers
- Fully Connected Layers
- Backpropagation in CNNs
- CNN based Image Classification using PyTorch
- CNN based Images Classification using TensorFlow
- CNN Based Architectures:
- Convolutional Neural Network (CNN) Architectures
- LeNet-5
- AlexNet
- VGGnet
- VGG-16 Network
- GoogLeNet/Inception
- ResNet (Residual Network)
- MobileNet
- 2. Recurrent Neural Networks (RNNs)
- Recurrent Neural Networks (RNNs)
- How RNN Differs from Feedforward Neural Networks
- Backpropagation Through Time (BPTT)
- Vanishing Gradient and Exploding Gradient Problem
- Training of RNN in TensorFlow
- Sentiment Analysis with RNN
- Types of Recurrent Neural Networks:
- Types of Recurrent Neural Networks
- Bidirectional RNNs
- Long Short-Term Memory (LSTM)
- Bidirectional Long Short-Term Memory (Bi-LSTM)
- Gated Recurrent Units (GRU)
- 3. Generative Models in Deep Learning
- Generative Adversarial Networks (GANs)
- Autoencoders
- GAN vs. Transformer Models
- Types of Generative Adversarial Networks (GANs):
- Deep Convolutional GAN (DCGAN)
- Conditional GAN (cGAN)
- Cycle-Consistent GAN (CycleGAN)
- Super-Resolution GAN (SRGAN)
- StyleGAN
- Types of Autoencoders:
- Types of Autoencoders
- Sparse Autoencoder
- Denoising Autoencoder
- Convolutional Autoencoder
- Variational Autoencoder
- 4. Deep Reinforcement Learning
- Deep Reinforcement Learning (DRL)
- Deep Reinforcement Learning
- Reinforcement Learning
- Markov Decision Processes
- Key Algorithms in Deep Reinforcement Learning
- Deep Q-Networks (DQN)
- REINFORCE
- Actor-Critic Methods
- Proximal Policy Optimization (PPO)
- Module E – Artificial Intelligence
- Types of Artificial Intelligence
- Types of AI Based on Capabilities
- Types of AI Based on Functionalities
- What is an AI Agent?
- AI agent
- Types of AI Agents
- Problem Solving in AI
- 1. Search Algorithms in AI
- Search algorithms
- Breadth-First Search (BFS)
- Depth-First Search (DFS)
- Uniform Cost Search (UCS)
- Bidirectional search
- Greedy Best-First Search
- A Search* Algorithm
- 2. Local Search Algorithms
- Local search algorithms
- Hill-Climbing Search Algorithm
- Local Beam Search
- 3. Adversarial Search in AI
- Adversarial search
- Minimax Algorithm
- Alpha-Beta Pruning
- 4. Constraint Satisfaction Problems
- Constraint Satisfaction Problem (CSP)
- Constraint Propagation in CSP’s
- Backtracking Search for CSP’s
- Knowledge, Reasoning and Planning in AI
- Knowledge Representation:
- Knowledge representation in Artificial Intelligence (AI)
- Semantic Networks
- Frames
- Ontologies
- Logical Representation
- First Order Logic in Artificial Intelligence:
- First Order Logic (FOL)
- Knowledge Representation in First Order Logic
- Syntax and Semantics of First Order Logic
- Inference Rules in First Order Logic
- Reasoning in Artificial Intelligence:
- Reasoning in Artificial Intelligence (AI)
- Types of Reasoning in AI
- Deductive Reasoning
- Inductive Reasoning
- Abductive Reasoning
- Fuzzy Reasoning
- Planning in AI:
- Planning in AI
- Forward State Space Search
- Markov Decision Processes (MDPs)
- Hierarchical State Space Search (HSSS)
- Uncertain Knowledge and Reasoning:
- Uncertain Knowledge and Reasoning in AI
- Dempster-Shafer Theory
- Probabilistic Reasoning
- Fuzzy Logic
- Neural Networks with dropout
- Types of Learning in AI
- 1. Supervised Learning
- Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees
- Support Vector Machines (SVM)
- k-Nearest Neighbors
- Naïve Bayes
- Random Forests
- 2. Semi-supervised learning
- 3. Unsupervised Learning
- Unsupervised Learning
- K-Means Clustering
- Principal Component Analysis (PCA)
- Hierarchical Clustering
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- 4. Reinforcement Learning
- Reinforcement Learning
- Q-Learning
- Deep Q-Networks (DQN)
- Markov decision processes (MDPs)
- Bellman equation
- 5. Deep Learning
- Deep Learning
- Neurons
- Single Layer Perceptron
- Multi-Layer Perceptron
- Artificial Neural Networks (ANNs)
- Feedforward Neural Networks (FNN)
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM) networks
- Gated Recurrent Units Networks (GRU)
- Probabilistic models
- Probabilistic models
- Naive Bayes Classifier
- Monte Carlo Methods
- Expectation-Maximization (EM) Algorithm
- Communication, Perceiving and Acting in AI and Robotics
- 1. Natural Language Processing (NLP)
- Speech Recognition
- Natural Language Generation
- Chatbots
- Machine Translation
- 2. Computer Vision
- Image Recognition
- Facial Recognition
- Optical Character Recognition
- 3. Robotics
- Generative AI
- Large Language Models
- GPT (Generative Pre-trained Transformer)
- BERT (Bidirectional Encoder Representations from Transformers)
- T5 (Text-to-Text Transfer Transformer)
- Conditional GAN (cGAN)
- CycleGAN
- Style GANs
- Module F – Generative AI
- LLMs: GPT, BERT, LLaMA, Tokenization, Embeddings
- Prompt Engineering: Zero-Shot, Few-Shot, Chain-of-Thought, Prompt Tuning
- Fine-Tuning & Transfer Learning: LoRA, Pretrained Model Adaptation
- Generative Tasks
- Text: Chatbots, Summarization, Translation
- Image: GANs, Diffusion Models
- Frameworks: HuggingFace, LangChain
- 1. Tools for Generative AI
- To get started with Generative AI, you need to build expertise in the following tools and libraries:
- Python
- PyTorch
- TensorFlow
- Hugging Face Transformers
- LangChain
- LangGraph
- Langflow
- LlamaIndex
- Integration of Langchain with Llama-Index
- 2. Core Concepts in Generative AI
- Understanding the foundations of AI and deep learning is essential for working with GenAI models.
- What is Artificial Intelligence?
- What is Generative AI?
- Neural Networks
- RNNs, LSTMs, GRUs
- Transformers and Self-Attention
- Autoencoders and Latent Space
- GANs and Diffusion Models
- 3. Natural Language Processing (NLP) Basics
- Most Generative AI models are built on NLP concepts.
- Text Preprocessing in NLP
- Bag of Words & TF-IDF
- Word2Vec & GloVe
- Introduction to BERT
- Introduction to GPT Models
- Hugging Face Models
- 4. Prompt Engineering
- Prompt engineering is the practice of crafting inputs to get better outputs from LLMs.
- What is Prompt Engineering?
- Zero-Shot, One-Shot and Few-Shot Prompting
- Chain of Thought Prompting
- Role & Contextual Prompting
- ReAct (Reasoning + Acting) Prompting
- Retrieval-Augmented Prompting
- Self-Consistency Prompting
- Tree of Thought (ToT) prompting
- Guardrails in AI
- 5. Large Language Models (LLMs)
- LLMs are the backbone of modern Generative AI systems.
- Large Language Model
- LLM Parameters
- Scaling Laws in LLMs
- Fine-Tuning LLMs with LoRA, QLoRA and PEFT
- RLHF: Reinforcement Learning from Human Feedback
- LLM Distillation
- Popular LLMs: GPT, Claude, LLaMA, Gemini
- LLM APIs: OpenAI, Hugging Face, Gemini
- 6. Retrieval-Augmented Generation (RAG)
- RAG combines LLMs with external knowledge sources for more accurate responses.
- RAG in AI
- RAG Architecture
- Multimodal RAG
- Embeddings
- Vector Databases: FAISS, ChromaDB, Qdrant, Pinecone
- RAG System with Langchain and Langraph
- 7. Agentic AI & Multi-Agent Systems
- Agentic AI extends LLMs with autonomy, memory and collaboration.
- What is Agentic AI?
- Agent vs Traditional AI
- Agent Architectures & Memory
- Agent-to-Agent Communication
- AI Agent Frameworks
- Model Context Protocol (MCP)
- 8. CrewAI and Orchestration
- CrewAI is a framework for coordinating multiple AI agents to work collaboratively.
- Introduction to CrewAI
- CrewAI Tools
- Creating Custom Tools for CrewAI
- Memory in CrewAI
- CrewAI Embeddings
- CrewAI Collaboration
- CrewAI Knowledge
- CrewAI Planning and Reasoning
- CrewAI CLI
- CrewAI Flow
- Fraud Detection Using CrewAI Project
- 9. Automation with Agents and Deployment
- Generative AI can be extended into workflows for business automation.
- Agentic RAG
- Agentic RAG with LlamaIndex
- Introduction to n8n
- Automated Email Classifier with n8n
- AI Deployment with Gradio, Streamlit, FastAPI
- 10. Responsible & Ethical AI
- Generative and Agentic AI raise ethical challenges that must be addressed.
- Bias in AI Models
- Deepfakes
- Prompt Injection in LLM
- Responsible AI Practices
- Module G – Integration, MLOps & Cloud Deployment
- MLOps:
- DVC
- MLflow
- Docker
- CI/CD Concepts
- Cloud Deployment:
- AWS (S3, EC2, Lambda)
- GCP (Vertex AI)
- Azure ML
- Capstone Projects:
- Predictive Analytics
- Recommender System
- AI Chatbot
- Computer Vision App
- End-to-End Django + ML Deployment
12:{"module_id":"63075908becd0a8d197e0466","module_name":"Module - 1","fees":100000}
13:{"module_id":"63075912becd0a8d197e046a","module_name":"Module - 2","fees":75000}
14:{"module_id":"63075919becd0a8d197e046e","module_name":"Module - 3","fees":60000}
11:["$12","$13","$14"]
16:{"$oid":"62efc93b569b2709a5a5f326"}
15:["$16"]
17:[]
18:[]
19:{"$date":"2025-10-08T10:29:21.516Z"}
1a:{"$date":"2025-10-08T10:29:21.516Z"}
d:{"_id":"$e","coursename":"AI & ML with Deep Learning, Data Science, GenAI & MLOps","iconname":"fa-fa","desc":"$f","icon":"uploads\\course\\1759919361314.png","photo":"uploads\\course\\1759919361317.png","broucher":"uploads\\course\\1759919361319.pdf","banner":"uploads\\course\\1759919361371.PNG","certificate":"uploads\\course\\1759919361371.PNG","shortdesc":"$10","typeofcourse":"Trending","youtubelink":"https://www.youtube.com/embed/LhABUIRhgP4","other":"Unlock expert-level skills in AI(Artificial Intelligence), Machine Learning, Data Science, Deep Learning, Generative AI & Full Stack Development Training + Internship program in Ahmedabad. This comprehensive HN Techno course spans Python, Django, Neural Networks, LLMs, Computer Vision, NLP, and MLOps. Build practical projects and deploy models, culminating in an invaluable internship opportunity.\r\nKey Highlights: Specific technologies, practical projects, deployment, internship value.\r\nKeywords: AI, ML, Data Science, Full Stack, Python, Django, Neural Networks, LLMs, Computer Vision, NLP, MLOps, projects, deploy, internship.","moduleFees":"$11","selectedItems":"$15","showwebsite":"Yes","slug":"hn-techno-ai-ml-data-science-training-internship-ahmedabad","topics":"$17","material":"$18","createdAt":"$19","updatedAt":"$1a","__v":0}
2:[["$","$L8",null,{"courseName":{"_id":{"$oid":"68e63d01d03dba04aaae285f"},"coursename":"AI & ML with Deep Learning, Data Science, GenAI & MLOps","iconname":"fa-fa","desc":"$9","icon":"uploads\\course\\1759919361314.png","photo":"uploads\\course\\1759919361317.png","broucher":"uploads\\course\\1759919361319.pdf","banner":"uploads\\course\\1759919361371.PNG","certificate":"uploads\\course\\1759919361371.PNG","shortdesc":"$a","typeofcourse":"Trending","youtubelink":"https://www.youtube.com/embed/LhABUIRhgP4","other":"Unlock expert-level skills in AI(Artificial Intelligence), Machine Learning, Data Science, Deep Learning, Generative AI & Full Stack Development Training + Internship program in Ahmedabad. This comprehensive HN Techno course spans Python, Django, Neural Networks, LLMs, Computer Vision, NLP, and MLOps. Build practical projects and deploy models, culminating in an invaluable internship opportunity.\r\nKey Highlights: Specific technologies, practical projects, deployment, internship value.\r\nKeywords: AI, ML, Data Science, Full Stack, Python, Django, Neural Networks, LLMs, Computer Vision, NLP, MLOps, projects, deploy, internship.","moduleFees":[{"module_id":"63075908becd0a8d197e0466","module_name":"Module - 1","fees":100000},{"module_id":"63075912becd0a8d197e046a","module_name":"Module - 2","fees":75000},{"module_id":"63075919becd0a8d197e046e","module_name":"Module - 3","fees":60000}],"selectedItems":[{"$oid":"62efc93b569b2709a5a5f326"}],"showwebsite":"Yes","slug":"hn-techno-ai-ml-data-science-training-internship-ahmedabad","topics":[],"material":[],"createdAt":{"$date":"2025-10-08T10:29:21.516Z"},"updatedAt":{"$date":"2025-10-08T10:29:21.516Z"},"__v":0}}],["$","$Lb",null,{}],["$","$Lc",null,{"courseName":"$d"}]]
7:[["$","meta","0",{"name":"viewport","content":"width=device-width, initial-scale=1"}],["$","meta","1",{"charSet":"utf-8"}]]
1:null