Machine Learning Syllabus

 Module 1: Introduction to Machine Learning

- Overview of Machine Learning

  - Definition and types of machine learning: supervised, unsupervised, reinforcement learning

  - Applications of machine learning in various fields

- Machine Learning Workflow

  - Problem definition and data collection

 Module 2: Data Preprocessing

- Data Cleaning

  - Handling missing values and outliers

  - Data transformation techniques (normalization, standardization)

- Feature Engineering

  - Feature selection and extraction methods

  - Creating new features from existing ones

- Data Splitting

  - Train-test split, cross-validation techniques

- NumPy Exercises

  - Create a NumPy array from a list and perform basic operations (addition, multiplication)

  - Implement array slicing and indexing techniques.

  - Use NumPy to compute the mean, median, and standard deviation of an array.

- pandas Exercises

  - Load a CSV file into a pandas DataFrame and perform data inspection (head, info, describe).

  - Clean the DataFrame by handling missing values (drop or fill).

  - Perform groupby operations and aggregate functions (sum, mean).

  - Filter DataFrame based on specific conditions.

- Matplotlib Exercises

  - Create basic line and scatter plots using Matplotlib.

  - Customize plots with titles, labels, legends, and grid.

  - Visualize data distributions using histograms and box plots.

  - Create subplots to visualize multiple graphs in one figure.

 Module 3: Supervised Learning

- Regression Techniques

  - Linear regression, polynomial regression

  - Regularization techniques (Lasso, Ridge, ElasticNet)

- Classification Techniques

  - Logistic regression, decision trees, and random forests

  - Support vector machines (SVM)

  - K-nearest neighbors (KNN)

  - Neural networks for classification

 Module 4: Unsupervised Learning

- Clustering Techniques

  - K-means clustering, hierarchical clustering, DBSCAN

  - Evaluation of clustering performance

- Dimensionality Reduction

  - Principal Component Analysis (PCA)

  - t-Distributed Stochastic Neighbor Embedding (t-SNE)

  - Feature extraction techniques

 Module 5: Model Evaluation and Selection

- Evaluation Metrics

  - Classification metrics: accuracy, precision, recall, F1 score, ROC-AUC

  - Regression metrics: mean absolute error (MAE), mean squared error (MSE), R-squared

- Model Selection

  - Hyperparameter tuning techniques (Grid Search, Random Search)

  - Ensemble methods: bagging and boosting (e.g., Random Forest, AdaBoost, XGBoost)

 Module 6: Python for Deep Learning

- Python Basics for Machine Learning

  - Data types, control structures, and functions

  - Object-oriented programming in Python

- Deep Learning Libraries

  - Overview of TensorFlow and Keras for building deep learning models

  - Implementing a simple neural network from scratch

  - Using pre-trained models for transfer learning

 Module 7: ML algorithms execution using  SciPy

- SciPy Exercises

  - Use SciPy for optimization and interpolation techniques.

  - Apply statistical functions from SciPy for data analysis (e.g., t-tests, ANOVA).

  - Implement algorithms from SciPy to solve linear algebra problems.

 Module 8: Practical Applications

- Real-World Machine Learning Projects

  - End-to-end project development

  - Case studies and industry applications

- Tools and Libraries

  - Introduction to libraries such as NumPy, pandas, Matplotlib, SciPy, scikit-learn, TensorFlow, and PyTorch

  - Best practices in using these tools

Recommended Resources

- Textbooks

  - "Pattern Recognition and Machine Learning" by Christopher Bishop

  - "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron

- Online Resources

  - Kaggle for practice datasets and competitions