Machine Learning in Python
Course Duration: 8 weeks
General Course Plan
- Week 1: Introduction to Machine Learning and Python
- Introduction to Machine Learning
- Introduction to Python and its libraries (NumPy, Pandas, Matplotlib)
- Setting up the development environment
- Week 2: Data Preprocessing and Visualization
- Data cleaning and handling missing values
- Feature selection and feature engineering
- Data visualization using Matplotlib and Seaborn
- Week 3: Supervised Learning Algorithms
- Linear Regression
- Logistic Regression
- Decision Trees and Random Forests
- Week 4: Unsupervised Learning Algorithms
- K-means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Week 5: Evaluation and Model Selection
- Model evaluation metrics (accuracy, precision, recall, etc.)
- Cross-validation techniques
- Hyperparameter tuning
- Week 6: Neural Networks and Deep Learning
- Introduction to Neural Networks
- Building and training a basic Neural Network in Python
- Introduction to Deep Learning frameworks (TensorFlow, Keras)
- Week 7: Advanced Topics in Machine Learning
- Support Vector Machines (SVM)
- Ensemble Learning (Bagging, Boosting)
- Introduction to Natural Language Processing (NLP)
- Week 8: Final Project and Wrap-up
- Applying machine learning techniques to a real-world dataset
- Presenting and discussing the project results
- Reviewing key concepts and next steps
Additional Suggestions
- Assignments and quizzes to reinforce learning
- Guest lectures or industry case studies to provide real-world applications
- Discussion forums or study groups for collaborative learning
Detailed Course Plan
Now, let’s dive into the details of each lesson with a detailed course plan.
Week 1: Introduction to Machine Learning and Python
Overview of Machine Learning: What is ML, types of ML (supervised, unsupervised, reinforcement), applications.
Introduction to Python: Basics of Python programming, data types, variables, loops, and functions.
Introduction to Libraries: Overview of NumPy for numerical computations, Pandas for data manipulation, and Matplotlib for data visualization.
Setting up Development Environment: Installing Python, Anaconda, Jupyter Notebook, and required libraries.
Week 2: Data Preprocessing and Visualization
- Data Cleaning: Handling missing values (removal, imputation), dealing with outliers, data normalization, and scaling.
- Feature Selection and Engineering: Techniques for selecting relevant features, creating new features, and handling categorical variables.
- Data Visualization: Using Matplotlib and Seaborn for creating informative graphs, histograms, scatter plots, and more.
Week 3: Supervised Learning Algorithms
- Linear Regression: Understanding linear regression, simple and multiple regression, model evaluation.
- Logistic Regression: Introduction to logistic regression, binary and multiclass classification, sigmoid function.
- Decision Trees and Random Forests: Decision tree concepts, tree construction, random forests, overfitting, and model evaluation.
Week 4: Unsupervised Learning Algorithms
- K-means Clustering: Clustering concepts, K-means algorithm, elbow method for optimal K, evaluating clusters.
- Hierarchical Clustering: Agglomerative and divisive approaches, dendrogram visualization.
- Principal Component Analysis (PCA): Dimensionality reduction, eigenvalues, eigenvectors, PCA algorithm.
Week 5: Evaluation and Model Selection
- Model Evaluation Metrics: Accuracy, precision, recall, F1-score, ROC curve, AUC.
- Cross-validation Techniques: K-fold cross-validation, stratified sampling, advantages, and implementation.
- Hyperparameter Tuning: Grid search, random search, optimizing model performance.
Week 6: Neural Networks and Deep Learning
- Introduction to Neural Networks: Neurons, activation functions, feedforward networks, backpropagation.
- Building a Basic Neural Network: Implementing a simple neural network using NumPy.
- Introduction to Deep Learning Frameworks: Overview of TensorFlow and Keras, building and training neural networks.
Week 7: Advanced Topics in Machine Learning
- Support Vector Machines (SVM): SVM concepts, linear and nonlinear SVM, kernels, tuning parameters.
- Ensemble Learning: Bagging and Boosting techniques (Random Forest, AdaBoost, Gradient Boosting), advantages.
- Introduction to Natural Language Processing (NLP): Basics of text processing, tokenization, stemming, and introduction to NLP applications.
Week 8: Final Project and Wrap-up
- Final Project: Students apply machine learning techniques to a real-world dataset, working on data preprocessing, model selection, training, and evaluation.
- Project Presentation: Each student presents their project, discusses their approach, challenges, and results.
- Review and Next Steps: Recap of key concepts from the course, further resources for advanced learning in ML, AI, and next steps in the learning journey.
- Remember that this is just a suggested breakdown, and you can adjust the pacing and depth based on your target audience and the duration of the course. It’s also important to include hands-on exercises, assignments, and quizzes to reinforce the learning throughout the course.
Important Web Sites
- Scikit-learn: Official documentation and tutorials for machine learning in Python
- Kaggle: Platform for data science and machine learning competitions, provides datasets and notebooks for practice