Introduction to ML
What is ML? Why ML
Definition and goals
Relationship with Data Science
Applications in realworld scenarios
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Key ML Concepts
Training, validation, and testing datasets
Overfitting vs. underfitting
Biasvariance tradeoff

Mathematics for Machine Learning
Linear Algebra:
Vectors, Matrices, and Operations
Eigenvalues and Eigenvectors
Applications in ML
Probability and Statistics:
Basic Probability Concepts
Distributions (Normal, Binomial, etc.)
Bayes’ Theorem
Hypothesis Testing
Calculus:
Derivatives and Gradients
Optimization (Gradient Descent)
Optimization Techniques:
Convex Optimization
Regularization (L1, L2)

Data Preprocessing and Feature Engineering
Data Cleaning:
Handling Missing Data
Outliers Detection and Treatment
Feature Scaling (Normalization/Standardization)
Encoding Categorical Data
Feature Selection Techniques:
Correlation
Recursive Feature Elimination
Feature Engineering:
Polynomial Features
Interaction Terms
Data Splitting (TrainTest Split, CrossValidation)

Supervised Learning
Regression Algorithms:
Linear Regression
Ridge and Lasso Regression
Polynomial Regression
Decision Tree Regression
Classification Algorithms:
Logistic Regression
kNearest Neighbors (kNN)
Support Vector Machines (SVM)
Decision Trees
Random Forest
Gradient Boosting (XGBoost, LightGBM, CatBoost)

Unsupervised Learning
Clustering Algorithms:
kMeans Clustering
Hierarchical Clustering
DBSCAN
Dimensionality Reduction:
Principal Component Analysis (PCA)
tSNE
Autoencoders
Anomaly Detection:
Isolation Forest
OneClass SVM

Deep Learning
Introduction to   Neural Networks
Activation Functions (ReLU, Sigmoid, etc.)
Loss Functions (CrossEntropy, Mean Squared Error)
Optimization Algorithms (Adam, SGD, etc.)
Architectures:
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Transformers (Introduction to BERT, GPT)
Frameworks:
TensorFlow
PyTorch

Reinforcement Learning
Markov Decision Processes
QLearning
Deep QNetworks
Applications of Reinforcement Learning (e.g., Game AI)

 Introduction to Artificial Intelligence
What is AI? History and Evolution
Applications of AI in Real Life (Healthcare, Finance, Robotics, etc.)
Types of AI:
Narrow AI vs. General AI vs. Super AI
Reactive, Limited Memory, Theory of Mind, SelfAware AI
AI vs Machine Learning vs Deep Learning
Mathematics and Logic for AI
Linear Algebra:
Vectors, Matrices, and Tensor Operations
Probability and Statistics:
Random Variables, Bayes’ Theorem
Distributions (Normal, Gaussian, etc.)
Calculus:
Derivatives, Gradients, Partial Derivatives
Logic and Reasoning:
Propositional and Predicate Logic
Fuzzy Logic
Heuristic Search Techniques
ProblemSolving and Search Techniques
State Space Search
Search Strategies:
Uninformed Search (DFS, BFS)
Informed Search (A*, Greedy)
Optimization Problems:
Constraint Satisfaction Problems (CSPs)
Hill Climbing, Simulated Annealing
Genetic Algorithms
Knowledge Representation and Reasoning
Knowledge Representation:
Semantic Networks
Frames and Scripts
Ontologies
Reasoning Techniques:
RuleBased Systems
Forward and Backward Chaining
Probabilistic Reasoning (Bayesian Networks)
Markov Models
Markov Models

Natural Language Processing (NLP)
Text Preprocessing:
Tokenization, Lemmatization, Stemming
Stop Word Removal
Language Models:
Bag of Words, TFIDF
Word Embeddings (Word2Vec, GloVe)
Applications of NLP:
Sentiment Analysis, Text Classification, Chatbots
Deep Learning in NLP:
RNNs, LSTMs, Transformers
Introduction to GPT and BERT
7. Computer Vision
Basics of Image Processing:
Filtering, Edge Detection, Segmentation
Feature Extraction:
SIFT, SURF
Object Detection and Recognition:
CNNs (Convolutional Neural Networks)
YOLO, Faster RCNN
Generative Models for Vision:
GANs (Generative Adversarial Networks)
8. Robotics and Perception
Introduction to Robotics
Path Planning and Obstacle Avoidance
Sensors and Perception:
Camera, LIDAR, Infrared
Robot Kinematics and Dynamics
Reinforcement Learning for Robotics
9. Reinforcement Learning
Basics of Reinforcement Learning:
Agents, States, Actions, Rewards
Key Algorithms:
QLearning
Deep QLearning
Applications:
Game AI, Robotics, Personalized Recommendations
10. Advanced Topics in AI
Deep Learning Architectures:
CNNs, RNNs, LSTMs, Transformers
Explainable AI (XAI)
Federated Learning
Edge AI and IoT Integration
AI in Healthcare, Autonomous Vehicles, Finance
11. Ethics and AI Governance
Ethical Challenges in AI:
Bias, Fairness, and Accountability
Privacy Concerns
AI Safety:
Adversarial Attacks
Safe Deployment of AI Models
Governance and Policies:
GDPR, Responsible AI Frameworks

AI Tools, Libraries, and Platforms
Programming Languages:
Python, R
Libraries and Frameworks:
TensorFlow, PyTorch, OpenCV, NLTK, Hugging Face
Cloud Platforms:
AWS AI Services, Google AI, Microsoft Azure AI
AISpecific Tools:
Jupyter Notebooks, Kaggle, Colab
AI Applications and Case Studies
AI in:
Healthcare (Disease Prediction, Diagnostics)
Finance (Fraud Detection, Stock Market Predictions)
Marketing (Customer Segmentation, Personalization)
Entertainment (Recommendation Systems)
Case Studies and Success Stories

HandsOn AI Projects:
Build a Chatbot
AI for Predictive Maintenance
Image Classification or Object Detection
NLPbased Sentiment Analysis

Test
Mock Interview