Machine Learning Foundations for Data-Driven Insights Course

Course Objectives

By the end of this course, participants will be able to:

• To understand the fundamentals of machine learning and its role in data analysis.
• To explore various machine learning algorithms and their applications in solving data problems.
• To develop the ability to pre-process data and prepare it for machine learning models.
• To gain proficiency in evaluating and tuning machine learning models for optimal performance.
• To learn to implement machine learning techniques using popular tools and libraries like Python and R.
• To apply machine learning models to real-world data sets and interpret the results.
• To understand the ethical considerations and limitations of machine learning in data analysis.
• To develop problem-solving skills by working on practical machine learning projects.
• To stay updated with the latest trends and advancements in machine learning.
• To build a foundation for advanced studies or a career in machine learning and data science.

Course Outline:

Module 1: Introduction to Machine Learning

• Definition and types of machine learning
• Supervised vs. unsupervised learning
• The machine learning process
• Python programming fundamentals for machine learning

Module 2: Data Exploration and Preprocessing

• Data loading and inspection
• Exploratory data analysis (EDA)
• Data cleaning and handling missing values
• Feature engineering and selection
• Data visualization techniques

Module 3: Linear Regression

• Simple and multiple linear regression
• Model evaluation metrics
• Overfitting and underfitting
• Regularization techniques

Module 4: Logistic Regression

• Logistic regression for classification
• Model evaluation metrics
• Odds and logit
• Decision boundaries

Module 5: Decision Trees and Random Forests

• Decision tree algorithm
• Random forest algorithm
• Feature importance
• Hyperparameter tuning

Module 6: Support Vector Machines (SVM)

• SVM for classification and regression
• Kernel trick
• Model selection and hyperparameter tuning

Module 7: Clustering

• K-means clustering
• Hierarchical clustering
• Evaluation of clustering results

Module 8: Model Evaluation and Selection

• Performance metrics for classification and regression
• Cross-validation
• Model comparison and selection
• Bias-variance trade-off

Module 9: Model Deployment and Interpretation

• Model deployment options
• Model interpretation techniques
• Explainable AI
• Ethical considerations in machine learning

Module 10: Advanced Topics

• Deep learning introduction
• Neural networks
• Natural language processing
• Time series analysis
• Model optimization and scalability