Machine Learning

• ML Introduction
  • What is Machine Learning?
  • Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
  • Applications of ML
  • Key Terminologies (Model, Training, Testing, Features, Labels)

ML Get Started

• Setting Up Machine Learning Environment
  • Installing Python
  • Python Libraries for ML (NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, PyTorch)
  • IDE Setup (Jupyter, Google Colab, VS Code)
  • First Machine Learning Program (Basic Linear Regression)

ML Data Preprocessing

• Data Cleaning
  • Handling Missing Data
  • Handling Outliers
  • Data Normalization/Standardization
• Data Exploration
  • Descriptive Statistics
  • Data Visualization (Histograms, Boxplots, Scatter Plots)
  • Correlation Analysis
• Feature Engineering
  • Feature Selection and Extraction
  • One-Hot Encoding
  • Feature Scaling (Min-Max, Standardization)

Supervised Learning

• Introduction to Supervised Learning
  • Concept of Labels and Features
  • Training vs Testing Data Split
  • Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)
• Linear Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Evaluation of Regression Models (MSE, RMSE, MAE)
• Classification Algorithms
  • Logistic Regression
  • k-Nearest Neighbors (k-NN)
  • Support Vector Machines (SVM)
  • Decision Trees
  • Random Forests
  • Naive Bayes Classifier
• Model Evaluation
  • Cross-validation
  • Confusion Matrix
  • ROC Curve and AUC

Unsupervised Learning

• Introduction to Unsupervised Learning
  • Clustering vs. Dimensionality Reduction
• Clustering Algorithms
  • k-Means Clustering
  • Hierarchical Clustering
  • DBSCAN
• Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • t-SNE (t-Distributed Stochastic Neighbor Embedding)
  • Autoencoders

Reinforcement Learning

• Introduction to Reinforcement Learning
  • Concepts: Agent, Environment, Actions, Rewards
  • Markov Decision Processes (MDP)
• Q-Learning
  • Value Iteration
  • Q-Function and Bellman Equation
• Deep Q Networks (DQN)
  • Deep Learning in RL
  • Exploration vs Exploitation

Model Tuning & Optimization

• Hyperparameter Tuning
  • Grid Search
  • Random Search
  • Bayesian Optimization
• Regularization Techniques
  • L1/L2 Regularization (Ridge, Lasso)
  • Dropout
  • Early Stopping
• Ensemble Methods
  • Bagging (Random Forests)
  • Boosting (AdaBoost, Gradient Boosting, XGBoost)
  • Stacking

Deep Learning for ML

• Introduction to Deep Learning
  • Difference Between Machine Learning and Deep Learning
  • Neural Networks Basics
  • Feedforward Neural Networks
• Convolutional Neural Networks (CNNs)
  • Image Classification using CNN
  • Convolution and Pooling Layers
  • Transfer Learning with Pretrained Models
• Recurrent Neural Networks (RNNs)
  • Sequential Data (Text, Time Series)
  • Long Short-Term Memory (LSTM) Networks
  • GRU (Gated Recurrent Unit)

Natural Language Processing (NLP)

• Text Preprocessing
  • Tokenization, Lemmatization, Stop Words Removal
  • Bag of Words vs TF-IDF
• Text Classification
  • Sentiment Analysis
  • Spam Detection
  • Text Clustering
• Advanced NLP
  • Word Embeddings (Word2Vec, GloVe)
  • Transformers (BERT, GPT)
  • Named Entity Recognition (NER)

ML in Practice

• Deploying Machine Learning Models
  • Saving and Loading Models (Pickle, Joblib)
  • Creating REST APIs (Flask, FastAPI)
  • Cloud Deployment (AWS, Google Cloud, Azure)
• Model Monitoring and Maintenance
  • Tracking Model Performance Over Time
  • Updating Models with New Data
• ML in Real-World Applications
  • Predictive Analytics (Sales Forecasting)
  • Recommender Systems (Netflix, Amazon)
  • Computer Vision (Image Recognition)
  • Natural Language Processing (Chatbots, Voice Assistants)
  • Fraud Detection in Finance

Advanced Machine Learning Topics

• Anomaly Detection
  • Techniques for Outlier Detection
  • Applications in Fraud Detection, Network Security
• Transfer Learning
  • Using Pre-trained Models for New Tasks
  • Fine-tuning and Feature Extraction
• Self-Organizing Maps (SOM)
  • Unsupervised Learning for Data Visualization
• Meta Learning
  • Few-shot Learning
  • Learning to Learn

ML Tools & Libraries

• Scikit-learn
  • Overview of Functions (Classification, Regression, Clustering)
  • Model Evaluation Tools (Metrics, Cross-Validation)
• TensorFlow
  • TensorFlow Basics for Deep Learning
  • Building and Training Neural Networks
• PyTorch
  • Introduction to PyTorch Tensors
  • Building and Training Models in PyTorch
• Keras
  • Building Deep Learning Models with Keras
  • Using Keras for Transfer Learning

ML Challenges and Research

• Current Challenges in Machine Learning
  • Interpretability of Models
  • Bias and Fairness in ML Models
  • Model Generalization vs. Overfitting
• Future Trends in Machine Learning
  • AutoML (Automated Machine Learning)
  • Explainable AI (XAI)
  • ML for Edge Devices and IoT
  • Federated Learning