AI & Machine Learning Training Course

Definition and Scope of AI
History and Evolution of AI
Applications of AI in Real Life
Types of AI: Narrow, General, Super
Weak AI vs Strong AI
AI vs Machine Learning vs Deep Learning
Symbolic AI and Classical Approaches
Intelligent Agents
Turing Test and Rational Agents
Ethics and Social Implications of AI

Introduction to Python Programming
Data Types, Variables, and Operators
Control Structures and Loops
Functions and Lambda Expressions
Lists, Tuples, Dictionaries, Sets
File Handling in Python
Exception Handling
Modules and Packages
Working with Jupyter Notebooks
Introduction to Virtual Environments

Introduction to OOP
Classes and Objects
Constructors and Destructors
Inheritance (Single, Multiple, Multilevel)
Polymorphism
Encapsulation
Abstraction
Method Overloading and Overriding
Class vs Instance Variables
Real-world Examples using OOP

NumPy for Numerical Computation
Pandas for Data Manipulation
Matplotlib for Data Visualization
Seaborn for Statistical Plots
Scikit-learn for ML Models
TensorFlow for Deep Learning
PyTorch for Neural Networks
OpenCV for Computer Vision
NLTK and spaCy for NLP
Hugging Face Transformers Library

Linear Algebra Basics (Vectors, Matrices)
Matrix Multiplication & Inversion
Calculus Basics (Derivatives & Integrals)
Partial Derivatives and Chain Rule
Probability and Statistics
Descriptive vs Inferential Statistics
Mean, Variance, Standard Deviation
Bayes’ Theorem
Gradient and Gradient Descent
Eigenvalues and Eigenvectors

Understanding Structured and Unstructured Data
Data Collection Methods
Data Cleaning and Imputation
Feature Engineering
Feature Scaling (Normalization, Standardization)
Handling Missing and Outlier Values
Encoding Categorical Variables
Data Splitting: Train/Test/Validation
Data Visualization for EDA
Data Pipelines and Automation

Machine Learning Fundamentals
Feature Extraction
Fine Tuning Hyperparameters
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Semi-Supervised
Gradient descent
Model Evaluation & Selection
Regression
GoogleColab

Introduction to Supervised Learning
Linear Regression
Logistic Regression
Decision Trees
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
Naive Bayes Classifier
Random Forests
Gradient Boosting Machines (XGBoost, LightGBM)
Evaluation Metrics (Accuracy, Precision, Recall, F1)

Introduction to Unsupervised Learning
K-Means Clustering
Hierarchical Clustering
DBSCAN
Principal Component Analysis (PCA)
t-SNE for Dimensionality Reduction
Anomaly Detection
Association Rule Mining
Recommendation Systems
Evaluation of Clustering

Basics of RL and Agents
Environment and Reward System
Markov Decision Process (MDP)
Q-Learning
Deep Q-Networks (DQN)
Policy Gradient Methods
Actor-Critic Models
Exploration vs Exploitation
OpenAI Gym Environments
Real-life Applications of RL

Introduction to Neural Networks
Perceptron’s and MLPs
Activation Functions
Forward and Backpropagation
Loss Functions
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short Term Memory(LSTM)
Generative Adversarial Network(GAN)
Autoencoders
Deep Learning Frameworks (TF, PyTorch)

Text Preprocessing (Tokenization, Lemmatization)
Bag of Words and TF-IDF
Word Embedding’s (Word2Vec, GloVe)
Named Entity Recognition (NER)
POS Tagging
Sentiment Analysis
Text Classification
Topic Modeling (LDA)
Transformers in NLP
Language Translation and Generation

Introduction to CV and Image Processing
Image Representation and Color Models
Image Filtering and Edge Detection
Object Detection (Haar, HOG)
CNNs for Image Classification
Image Augmentation Techniques
Face Detection and Recognition
Transfer Learning with CNNs
Image Segmentation
OCR (Optical Character Recognition)

What are LLMs?
Transformer Architecture
GPT, BERT, T5 Comparison
Pertaining and Fine-tuning
Prompt Engineering
Zero-shot, Few-shot Learning
Using Hugging Face Models
Text Generation and Completion
Semantic Search with LLMs
LLM APIs (OpenAI, Cohere)

Definition and Evolution of AI Agents
ReAct and Chain-of-Thought Reasoning
LangChain Agents
AutoGPT and BabyAGI
Tool Use and Function Calling
Agent Memory and Planning
Multi-Agent Collaboration
Task Automation with Agents
Human-in-the-Loop Design
Guardrails and Safety

Introduction to Robotics
Sensors and Actuators
Robotic Kinematics
Perception and SLAM
Path Planning Algorithms
Robotic Control Systems
Vision-Based Navigation
ROS (Robot Operating System)
Simulators (Gazebo, Webots)
AI for Manipulation and Locomotion

Bias in AI Models
Explainable AI (XAI)
AI Governance and Compliance
Data Privacy and Protection
AI and Job Displacement
Fairness Metrics
Red-teaming and Safety Protocols
Adversarial Attacks
Responsible AI Development
AI Regulation (EU, India, Global)

Model Serialization (Pickle, Joblib)
REST APIs with FastAPI / Flask
Model Deployment with Docker
Model Monitoring and Logging
Version Control for Models
A/B Testing Models
CI/CD for ML Pipelines
Using Streamlit and Gradio
Cloud Deployments (AWS, GCP, Azure)
MLflow and Model Registry

Defining Project Objectives
Problem Formulation
Data Collection and Cleaning
Model Selection
Training and Tuning
Evaluation and Metrics
Deployment Strategy
Feedback Loop and Maintenance
Documentation and Reporting
Case Study Development