#No 1 Artificial Intelligence , Machine Learning & Data Science Program
Build Machine Learning Models, Deep Learning Solutions, Predictive Systems, and Data-Driven AI Projects.
The Ultimate AIML & DS Program For Students
- Complete Job Ready Training
- Unlimited Training Access
- Internship Offer Letter
- Internship Experience Letter
Training Module
Module 1 : Introduction to Artificial Intelligence & Data Science
- Introduction to Artificial Intelligence
- History and Evolution of AI
- AI Applications Across Industries
- AI vs Machine Learning vs Deep Learning
- AI vs Data Science vs Data Analytics
- Types of AI
- AI Development Lifecycle
- Introduction to Data Science and Data Analytics
- Career Opportunities in AI, ML, Data Science & Analytics
Module 2 : Python Programming
- Introduction to Python : Introduction to Programming Languages, Python Installation and IDE Setup, Python Syntax and Indentation Rules, Writing First Python Program
- Variables and Data Types : Python Variables, Primitive Data Types – int, float, string, Boolean. Type Conversion and Type Casting
- Operators and Expressions : Arithmetic Operators, Relational Operators, Logical Operators, Assignment Operators
- Control Statements :Conditional Statements – If, If Else, Nested If, Looping Statements – For Loop, While Loop, Break and Continue Statements
- Functions and Modules : Defining Functions, Function Parameters and Return Values, Lambda Functions, Python Modules and Packages
- Exception Handling : Errors vs Exceptions, Try, Except, Finally Blocks, Custom Exceptions
Module 3 : SQL for Data Science & AI
- Database Fundamentals
- DBMS and RDBMS
- SQL Basics
- Filtering and Sorting Data
- Aggregate Functions
- Joins
- Subqueries
- Views
- Indexes
- SQL with Python
- Working with Real Datasets
Module 4 : Python for Data Science & AI (Data Analysis)
- Introduction to NumPy : NumPy Arrays, Array Creation Methods, Array Indexing and Slicing, Mathematical Operations on Arrays, Broadcasting Concepts
- Introduction to Pandas : Pandas Series and DataFrames, Data Import and Export, Data Cleaning Techniques, Handling Missing Values, Data Aggregation and GroupBy Operations, Data Filtering and Sorting
Module 5 : Data Visualization
- Introduction to Data Visualization : Importance of Data Visualization, Data Visualization Best Practices, Types of Charts and Graphs
- Matplotlib : Line Charts, Bar Charts, Scatter Plots, Histograms, Plot Customization Techniques
- Seaborn : Statistical Data Visualization, Heatmaps, Distribution Plots, Pair Plots, Correlation Visualization
Module 6 : Mathematics for Machine Learning
- Linear Algebra : Vectors and Matrices, Matrix Operations, Matrix Multiplication, Eigenvalues and Eigenvectors Basics
- Statistics : Mean, Median, Mode, Variance, Standard Deviation, Correlation and Covariance
- Probability : Probability Basics, Probability Distributions, Conditional Probability, Bayes Theorem
Module 7 : Machine Learning
- Introduction to Machine Learning : Artificial Intelligence vs Machine Learning vs Deep Learning, Types of Machine Learning – Supervised, Unsupervised, Reinforcement Learning, Machine Learning Workflow
- Supervised Learning : Linear Regression, Logistic Regression, Decision Trees, Random Forest Algorithms, Support Vector Machines
- Unsupervised Learning : Clustering Concepts, K-Means Clustering, Hierarchical Clustering, Dimensionality Reduction – PCA
- Model Evaluation : Training and Testing Data, Accuracy, Precision, Recall, F1 Score, Cross Validation Techniques
Module 8 : Deep Learning
- Introduction to Neural Networks : Artificial Neural Networks, Activation Functions, Forward Propagation, Backpropagation Algorithm
- Deep Learning Frameworks : Introduction to TensorFlow, Introduction to PyTorch, Model Training and Optimization
- Applications of Deep Learning : Image Classification, Object Detection Basics, Pattern Recognition Systems
Module 9 : Computer Vision
- Introduction to Computer Vision
- Image Processing Fundamentals
- OpenCV Basics
- Image Classification
- Object Detection Basics
- Face Detection & Recognition
- YOLO Introduction
- CNN (Convolutional Neural Networks)
- Transfer Learning
- Computer Vision Applications
Module 10 : Natural Language Processing (NLP)
- Text Processing : Tokenization, Stop Words Removal, Stemming and Lemmatization, Text Cleaning Techniques
- Text Vectorization : Bag of Words Model, TF-IDF Vectorization
- NLP Applications : Sentiment Analysis, Text Classification, Chatbot Development Basics
Module 11 : Generative AI
- Introduction to Generative AI : Generative AI Concepts, Large Language Models Overview, AI Model Capabilities and Limitations
- Prompt Engineering : Writing Effective Prompts, Prompt Optimization Techniques, Chain of Thought Prompting
- AI Application Development : Integrating AI APIs, Building AI Chat Applications, AI Automation Tools
Module 12 : Large Language Models
- Introduction to LLMs
- Transformer Architecture
- GPT Models
- Tokenization
- Attention Mechanism
- Fine Tuning Basics
- Open Source LLMs
- LLM Evaluation
Module 13 : RAG (Retrieval Augmented Generation)
- Introduction to RAG
- Embeddings
- Vector Search
- Chunking Strategies
- Semantic Search
- LangChain Basics
- Building RAG Applications
- Knowledge Base Chatbot
Module 14 : Vector Databases & Embeddings
- Embeddings Fundamentals
- Sentence Transformers
- Vector Similarity Search
- FAISS
- ChromaDB
- Pinecone
- Building Semantic Search Systems
Module 15 : Agentic AI
- Introduction to AI Agents
- Agent Architecture
- Tool Calling
- Function Calling
- Memory Management
- Planning and Reasoning
- Agent Workflows
Module 16 : Multi Agent Systemm
- Multi-Agent Concepts
- Agent Communication
- Task Delegation
- CrewAI
- AutoGen
- Multi-Agent Collaboration
- Business Use Cases
Module 17 : Version Control & Collaboration
- Git Fundamentals
- GitHub
- Branching and Merging
- Pull Requests
- Repository Management
- Portfolio Development
Module 18 : MLOps & Model Deployments
- Model Serialization (Pickle, Joblib)
- FastAPI
- Docker
- Model Deployment
- API Development
- Monitoring
- CI/CD Basics
- Cloud Deployment
Module 19 : Industry Projects
- Machine Learning Project : Prediction Model Development using Real Dataset
- NLP Project : Sentiment Analysis Application Development
- Deep Learning Project : Image Classification System Development
- Final Capstone Project : End-to-End AI Application Development
Module 20 : Placement & Career Preparation
- Resume Building
- GitHub Portfolio Development
- LinkedIn Profile Optimization
- Mock Interviews
