Course
Machine Learning
46 hours 35 minutes
Credits: Optional Learning
Description
This course teaches learners how to build and apply machine learning models using Python, starting from the mathematical foundations of linear algebra and differential calculus, through supervised and unsupervised learning, feature engineering, anomaly detection, and advanced model improvement techniques. Learners apply knowledge through hands-on labs across all chapters covering real-world datasets and practical ML scenarios.
What Students Will Learn
Chapter 1: Math for Machine Learning
- Introduction to Linear Algebra
- Linear Algebra with Python
- Linear Algebra with Python: Image Transformations
- Introduction to Differential Calculus
- Differential Calculus Lab
- Mathematics for Data Science: Final Problem Set
Chapter 2: Supervised Learning I: Regressors, Classifiers and Trees
- Linear Regression
- Linear Regression Lab: Honey Production
- Multiple Linear Regression with Python
- Multiple Linear Regression Lab: Tennis Ace
- Logistic Regression
- Logistic Regression Lab: Predicting Credit Card Fraud
- Evaluation Metrics for Classification Tasks
- Logistic Regression II
- Logistic Regression II Lab
Chapter 3: Supervised Learning II: SVMs, Recommender Systems, Naive Bayes
- Support Vector Machines
- Support Vector Machines Lab: Baseball
- K-Nearest Neighbors
- K-Nearest Neighbors: Classification Lab
- K-Nearest Neighbors Regression
- Decision Trees
- Decision Trees Lab: Flag Predictions
- Bayes Theorem
- Naive Bayes Classifier
- Naive Bayes Lab: Emails
Chapter 4: Feature Engineering
- What is Feature Engineering?
- Numerical Transformations
- Data Transformations Lab
- Introduction To Feature Selection Methods
- Filter Methods
- Wrapper Methods of Feature Selection
- Wrapper Methods: Lab
- An Introduction to Regularization in Machine Learning
- Feature Importance
Chapter 5: Unsupervised Learning
- Unsupervised Learning
- Clustering Techniques
- K Means Clustering
- K Means Clustering Lab: Handwriting Recognition using K-Means
- ML & Dimensionality Reduction: Performing Principal Component Analysis
- Principal Component Analysis (PCA)
- Principal Component Analysis (PCA) Lab: Telescope Data
Chapter 6: Anomaly Detection
- Understanding Anomalies and Their Detection
- Using Z-Scores and IQR for Anomaly Detection
- Using LOF, iForest, and One-Class SVMs for Anomaly Detection
Chapter 7: Improving Machine Learning Models
- Hyperparameter Tuning in Machine Learning
- Hyperparameter Tuning Lab: Raisin Classification
- Introduction To Ensembling Methods
- Random Forests
- Random Forests Lab
- Boosting Machine Learning Models
- Stacking Machine Learning Models
- Introduction To Recommender Systems
- Recommender Systems Lab: Book Recommenders
- Recommender Systems: Under the Hood of Recommendation Systems
Optional
- Linear Regression Cheatsheet
- Logistic Regression Cheatsheet
- K-Nearest Neighbors Cheatsheet
- K-Nearest Neighbors Regression Cheatsheet
- Bayes Theorem Cheatsheet
- Random Forests Cheatsheet
- Numerical Transformations Cheatsheet
Overall Learning Outcomes
- Apply linear algebra and differential calculus concepts as mathematical foundations for machine learning
- Implement linear and logistic regression models and evaluate classification performance metrics
- Build and apply support vector machines, K-nearest neighbors, decision trees, and Naive Bayes classifiers
- Engineer features through numerical transformations, filter methods, wrapper methods, and regularization techniques
- Apply unsupervised learning techniques including K-means clustering and principal component analysis (PCA)
- Detect anomalies using Z-scores, IQR, local outlier factor (LOF), isolation forests, and one-class SVMs
- Tune hyperparameters and improve model performance using ensembling methods including random forests and boosting
- Build and evaluate recommender systems using collaborative filtering and matrix factorization techniques