AI with Machine Learning & Deep Learning
Introduction to Artificial Intelligence Duration: 3 Month
- What is Artificial Intelligence?
- History & Evolution of AI
- Applications of AI (Healthcare, Finance, Robotics, etc.)
- Branches of AI (ML, DL, NLP, Computer Vision)
- AI vs ML vs DL – Key Differences
Python Programming for AI & ML
- Python Basics (Syntax, Variables, Data Types, Loops, Functions)
- Working with Libraries (NumPy, Pandas, Matplotlib)
- Data Structures (Lists, Tuples, Dictionaries, Sets)
- File Handling & Exception Handling
- Object-Oriented Programming (OOP) in Python
Mathematics & Statistics for AI
- Linear Algebra (Vectors, Matrices, Operations)
- Calculus (Derivatives, Gradients)
- Probability & Statistics Basics
- Hypothesis Testing
- Descriptive & Inferential Statistics
- Correlation, Covariance, and Regression Basics
Introduction to Machine Learning
- What is Machine Learning?
- Types of ML:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Machine Learning Workflow
- Understanding Features, Labels, and Datasets
Supervised Learning Algorithms
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forests
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Naïve Bayes Classifier
- Model Evaluation Metrics (Accuracy, Precision, Recall, F1-score, ROC)
Unsupervised Learning Algorithms
- Clustering:
- K-Means
- Hierarchical Clustering
- Dimensionality Reduction:
- PCA (Principal Component Analysis)
- t-SNE
- Association Rule Learning (Apriori, Eclat)
Feature Engineering & Data Preprocessing
- Data Cleaning (Missing Values, Outliers)
- Encoding Categorical Data
- Feature Scaling (Normalization, Standardization)
- Feature Selection Techniques
- Handling Imbalanced Data (SMOTE, Undersampling)
Model Evaluation & Optimization
- Bias-Variance Tradeoff
- Cross-Validation
- Hyperparameter Tuning (GridSearchCV, RandomSearchCV)
- Regularization (L1, L2)
- Model Deployment Overview
Deep Learning Fundamentals
- What is Deep Learning?
- Neural Networks Basics
- Activation Functions
- Gradient Descent & Backpropagation
- Building Neural Networks using TensorFlow & Keras
- Model Training, Validation, and Evaluation
Advanced Deep Learning
- Convolutional Neural Networks (CNNs) – Image Processing
- Recurrent Neural Networks (RNNs) – Sequence Data
- LSTM & GRU Networks
- Transfer Learning (ResNet, VGG, Inception)
- Autoencoders & Generative Models
Natural Language Processing (NLP)
- Text Preprocessing (Tokenization, Lemmatization, Stopwords)
- Bag of Words & TF-IDF
- Word Embeddings (Word2Vec, GloVe)
- Sentiment Analysis
- Text Classification using RNN/LSTM
- Introduction to Transformers & BERT
Computer Vision (CV)
- Image Representation & Processing
- Image Classification with CNNs
- Object Detection (YOLO, SSD)
- Image Segmentation (U-Net)
Reinforcement Learning (Intro)
- Basics of RL (Agent, Environment, Reward)
- Q-Learning & Deep Q-Networks (DQN)
- Simple Game AI Implementation (e.g., Tic-Tac-Toe)
AI Deployment & Tools
- Model Serialization (Pickle, Joblib)
- Deploying Models with Flask / FastAPI
- Introduction to MLOps (CI/CD for ML)
- Using AWS / Google Cloud / Azure for AI Deployment
Projects & Case Studies
Mini Projects:
- House Price Prediction (Regression)
- Spam Email Classifier (NLP)
- Customer Segmentation (Clustering)
- Handwritten Digit Recognition (CNN)
Capstone Projects:
- Sentiment Analysis on Social Media Data
- Face Recognition System
- Medical Image Classification
- Chatbot using NLP + Deep Learning
Career Readiness & Interview Prep
- Building a Data Science Portfolio on GitHub
- Resume & LinkedIn Optimization for AI/ML Roles
- Common Interview Questions (Python, ML, DL)
- Mock Interviews & Coding Tests
- Certification Guidance (TensorFlow, AWS AI, etc.)
