1. Introduction to Machine Learning
• What is Machine Learning?
• Types of Machine Learning (Supervised, Unsupervised, Reinforcement).
• Applications and Tools.
2. Python for Machine Learning
• Setting up ML environments (Anaconda, Jupyter, etc.).
• Essential Python libraries (NumPy, Pandas, Matplotlib, Scikit-learn).
3. Data Preprocessing
• Data cleaning and handling missing values.
• Feature scaling and normalization.
• Encoding categorical data.
4. Exploratory Data Analysis (EDA)
• Data visualization techniques.
• Statistical analysis and correlation.
5. Supervised Learning Algorithms
• Regression (Linear, Logistic).
• Classification (SVM, Decision Trees, k-NN).
6. Unsupervised Learning Algorithms
• Clustering (K-means, Hierarchical).
• Dimensionality reduction (PCA, t-SNE).
7. Model Evaluation and Tuning
• Train-test split, cross-validation.
• Metrics (accuracy, precision, recall, F1-score, AUC-ROC).
• Hyperparameter tuning (GridSearch, RandomSearch).
8. Feature Engineering
• Feature selection techniques.
• Feature extraction and transformation.
9. Ensemble Learning
• Bagging (Random Forest).
• Boosting (Gradient Boosting, XGBoost).
10. Neural Networks and Deep Learning
• Introduction to neural networks.
• Basics of TensorFlow and Keras.
• Building and training deep learning models.
11. Natural Language Processing (NLP)
• Text preprocessing and tokenization.
• Sentiment analysis and text classification.
12. Time Series Analysis
• Understanding time series data.
• ARIMA and LSTM models.
13. Real-World Applications
• Recommender systems.
• Image recognition and computer vision basics.
14. Capstone Project
• End-to-end ML project.
• Deployment and final presentation.
Duration: 60 Days