Machine Learning
Machine Learning Course
Day 1-5: Introduction to Machine Learning
Day 1: Introduction to Machine Learning
- What is Machine Learning (ML)?
- Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
- Applications of Machine Learning in real life
- Overview of key ML algorithms
- Tools and libraries: Python, Scikit-learn, TensorFlow, PyTorch
Day 2: Python for Machine Learning
- Python basics for ML (libraries: NumPy, Pandas, Matplotlib)
- Data structures in Python (Lists, Tuples, DataFrames)
- Data visualization basics (Matplotlib, Seaborn)
- Data loading and preprocessing using Pandas
- Hands-on: Simple data analysis with Pandas and NumPy
Day 3: Introduction to Supervised Learning
- What is Supervised Learning?
- Training, Testing, and Validation sets
- Types of Supervised Learning: Regression and Classification
- Overview of Regression Algorithms (Linear, Polynomial, etc.)
- Overview of Classification Algorithms (Logistic Regression, KNN, SVM)
Day 4: Linear Regression
- Concept of Regression in ML
- Simple Linear Regression (SLR) and Multiple Linear Regression (MLR)
- Loss Function (MSE – Mean Squared Error)
- Gradient Descent Optimization
- Hands-on: Implementing Linear Regression in Scikit-learn
Day 5: Model Evaluation Metrics
- Overfitting vs Underfitting
- Bias-Variance Tradeoff
- Metrics: RMSE (Root Mean Squared Error), MAE (Mean Absolute Error)
- R-Squared, Adjusted R-Squared
- Cross-validation techniques (K-fold, Leave-one-out)
Day 6-10: Classification Algorithms
Day 6: Logistic Regression
- Introduction to Logistic Regression
- Sigmoid function and probability estimation
- Cost function and optimization (Gradient Descent)
- Binary and Multiclass classification
- Hands-on: Implementing Logistic Regression
Day 7: k-Nearest Neighbors (k-NN)
- Intuition behind k-NN algorithm
- Distance metrics: Euclidean, Manhattan, etc.
- Choosing the right value for ‘k’
- Pros and cons of k-NN
- Hands-on: Implementing k-NN in Python
Day 8: Support Vector Machines (SVM)
- Introduction to SVM
- Linear and Non-Linear SVMs (Kernel trick)
- Hyperplanes, Support Vectors, and Margin
- Regularization in SVM
- Hands-on: Implementing SVM for classification tasks
Day 9: Decision Trees and Random Forests
- Decision Tree basics: Gini Index, Entropy, and Information Gain
- Overfitting and pruning techniques
- Random Forests and Ensemble Learning
- Bagging, Boosting, and Voting classifiers
- Hands-on: Implementing Decision Tree and Random Forest
Day 10: Model Evaluation for Classification
- Confusion Matrix, Precision, Recall, F1-Score
- ROC Curve and AUC (Area Under Curve)
- Hyperparameter tuning for classification models
- Cross-validation for classification
- Hands-on: Model evaluation and comparison
Day 11-15: Unsupervised Learning
Day 11: Introduction to Unsupervised Learning
- What is Unsupervised Learning?
- Types of Unsupervised Learning: Clustering and Dimensionality Reduction
- Applications in anomaly detection and pattern recognition
- Overview of key algorithms (K-means, Hierarchical Clustering)
Day 12: K-means Clustering
- Introduction to K-means Clustering
- Objective function and convergence
- Choosing the number of clusters (Elbow method, Silhouette score)
- Pros and cons of K-means
- Hands-on: Implementing K-means Clustering
Day 13: Hierarchical Clustering
- Introduction to Hierarchical Clustering
- Agglomerative vs. Divisive Clustering
- Dendrogram and how to decide the number of clusters
- Pros and cons of Hierarchical Clustering
- Hands-on: Implementing Hierarchical Clustering
Day 14: Dimensionality Reduction
- What is Dimensionality Reduction and why is it important?
- Principal Component Analysis (PCA)
- Singular Value Decomposition (SVD)
- t-SNE and LDA for visualization
- Hands-on: PCA for feature reduction
Day 15: Anomaly Detection and Gaussian Mixture Models
- Introduction to Anomaly Detection
- Gaussian Mixture Models (GMM)
- Expectation-Maximization (EM) algorithm
- Applications in fraud detection, network security
- Hands-on: Implementing GMM for anomaly detection
Day 16-20: Advanced Supervised Learning
Day 16: Naive Bayes Classification
- Introduction to Naive Bayes
- Bayes Theorem and Conditional Probability
- Types of Naive Bayes models (Gaussian, Multinomial, Bernoulli)
- Applications: Text Classification and Spam Detection
- Hands-on: Implementing Naive Bayes classifier
Day 17: Ensemble Methods
- Introduction to Ensemble Learning
- Bagging and Boosting techniques
- Random Forest, AdaBoost, Gradient Boosting
- XGBoost and LightGBM
- Hands-on: Implementing Gradient Boosting and XGBoost
Day 18: Neural Networks and Deep Learning
- Introduction to Neural Networks
- Perceptrons and Multi-Layer Perceptrons (MLP)
- Activation functions (Sigmoid, ReLU, Tanh)
- Forward and Backpropagation
- Hands-on: Simple neural network implementation using Keras
Day 19: Convolutional Neural Networks (CNNs)
- Introduction to CNNs and their components (Convolution, Pooling, Fully Connected Layers)
- Applications of CNNs in image recognition and classification
- CNN architectures (LeNet, AlexNet, VGG, ResNet)
- Hands-on: Building a CNN model in TensorFlow/Keras
Day 20: Recurrent Neural Networks (RNNs)
- Introduction to RNNs and use cases (time-series, sequential data)
- Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs)
- Sequence-to-sequence models and attention mechanisms
- Hands-on: Building a text generation model using RNN/LSTM
Day 21-25: Model Tuning and Advanced Topics
Day 21: Hyperparameter Tuning and Grid Search
- Importance of hyperparameter tuning
- Grid Search and Random Search
- Cross-validation with hyperparameter optimization
- Hands-on: Hyperparameter tuning with GridSearchCV
Day 22: Overfitting and Regularization
- Overfitting and Underfitting issues
- Techniques to prevent overfitting (L1, L2 regularization)
- Dropout in neural networks
- Early stopping and cross-validation strategies
- Hands-on: Implementing regularization techniques
Day 23: Transfer Learning
- What is Transfer Learning?
- Pre-trained models and fine-tuning
- Using transfer learning for image and text tasks
- Hands-on: Fine-tuning a pre-trained CNN for a custom dataset
Day 24: Model Deployment
- Model deployment concepts and challenges
- Saving and loading models with joblib and TensorFlow
- Deploying models with Flask/Django for web-based applications
- Introduction to model serving with TensorFlow Serving and AWS Sagemaker
Day 25: Introduction to Reinforcement Learning
- Overview of Reinforcement Learning (RL)
- Key concepts: Agent, Environment, Reward, Policy
- Q-learning and Deep Q Networks (DQNs)
- Applications of RL in games, robotics, and optimization
- Hands-on: Building a Q-learning agent
Day 26-30: Working with Text Data and NLP
Day 26: Natural Language Processing (NLP) Overview
- What is NLP and its challenges?
- Text preprocessing: Tokenization, Stopwords, Lemmatization, Stemming
- TF-IDF and Bag of Words
- Word embeddings: Word2Vec, GloVe
- Hands-on: Text preprocessing with NLTK
Day 27: Text Classification
- Introduction to text classification tasks (Spam detection, Sentiment analysis)
- Feature extraction techniques: Bag of Words, TF-IDF, Word Embeddings
- Building a classification model for text data
- Hands-on: Implementing a text classifier using Naive Bayes and Logistic Regression
Day 28: Sequence Models and LSTMs for NLP
- Introduction to Sequence Models
- Using LSTMs for text classification, language modeling
- Word Embeddings with LSTM-based models
- Hands-on: Implementing an LSTM for sentiment analysis
Day 29: Named Entity Recognition (NER)
- What is Named Entity Recognition?
- Applications in information extraction
- Using CRF and LSTM for NER
- Hands-on: Building an NER model using spaCy or Hugging Face Transformers
Day 30: Transformers and BERT
- Introduction to the Transformer architecture
- Self-attention mechanism
- BERT and fine-tuning pre-trained models
- Hands-on: Implementing a BERT model for text classification
Day 31-40: Advanced Topics and Techniques
Day 31: Anomaly Detection
- What is Anomaly Detection?
- Techniques: Statistical Methods, Clustering-based, One-Class SVM
- Applications in fraud detection, network security
- Hands-on: Anomaly detection using Isolation Forest
Day 32: Time Series Forecasting
- Introduction to Time Series Analysis
- Autoregressive Models: ARIMA, SARIMA
- LSTM-based models for time series forecasting
- Hands-on: Building a stock price prediction model
Day 33: Autoencoders and Deep Learning for Dimensionality Reduction
- What are Autoencoders?
- Applications in anomaly detection, feature extraction
- Variational Autoencoders (VAEs)
- Hands-on: Implementing an autoencoder in TensorFlow/Keras
Day 34: GANs (Generative Adversarial Networks)
- Introduction to GANs and their components
- Training the Generator and Discriminator
- Applications: Image generation, style transfer
- Hands-on: Implementing a simple GAN for image generation
Day 35: Model Interpretability
- Why is model interpretability important?
- Techniques: LIME, SHAP, Feature Importance
- Interpreting complex models like neural networks
- Hands-on: Model interpretation using SHAP and LIME
Day 36-45: Special Topics in Machine Learning
Day 36: Multi-Class and Multi-Label Classification
- Handling multi-class and multi-label problems
- One-vs-Rest and One-vs-One strategies
- Evaluating multi-class models (Confusion Matrix, Macro F1-score)
- Hands-on: Implementing multi-class classification using SVM and Logistic Regression
Day 37: Graph-Based Learning
- Introduction to Graph Theory in ML
- Graph Neural Networks (GNNs)
- Applications in social networks, drug discovery
- Hands-on: Implementing a basic Graph Neural Network
Day 38: Transfer Learning for NLP
- Transfer learning in NLP tasks
- Fine-tuning BERT and GPT for specific tasks
- Applications of transfer learning in text classification, QA, summarization
- Hands-on: Fine-tuning BERT for sentiment analysis
Day 39: Reinforcement Learning – Advanced
- Deep Q-Networks (DQN) and Policy Gradient methods
- Exploration vs. Exploitation dilemma
- Applications in games and robotics
- Hands-on: Implementing DQN in OpenAI Gym
Day 40: Hyperparameter Optimization and Advanced Model Selection
- Techniques for hyperparameter tuning: GridSearch, RandomSearch, Bayesian Optimization
- Hyperparameter selection for deep learning models
- Hands-on: Hyperparameter tuning with Optuna
Day 41-60: Final Project and Advanced Practice
Day 41-50: Project Work
- Choose an area for the final project (Classification, Regression, NLP, Time-Series, etc.)
- Data collection and preprocessing
- Model selection and training
- Hyperparameter tuning and evaluation
Day 51-55: Model Deployment and Scaling
- Preparing the final model for deployment
- Introduction to Cloud services (AWS, Google Cloud, Heroku)
- Model serving and scaling for real-time prediction
- Hands-on: Deploying the ML model as a REST API
Day 56-60: Final Project Presentation and Review
- Final project presentation
- Peer reviews and feedback
- Optimizing and refining models based on feedback
- Next steps in ML research and career advice
