AI Machine Learning Apply Build Solve Course Syllabus

Overview: This course offers a hands-on introduction to applied machine learning, designed to build practical skills in building, evaluating, and deploying models for real-world problems. Through a blend of guided projects, case studies, and interactive labs, learners will gain experience across the full data science workflow—from data preprocessing to storytelling with visualizations. The course spans approximately 15-20 hours, with flexible pacing suitable for part-time learners aiming to strengthen their implementation abilities in AI and machine learning.

Module 1: Data Exploration & Preprocessing

Estimated time: 4 hours

• Perform exploratory data analysis using industry-standard tools
• Apply data cleaning techniques to handle missing and inconsistent data
• Transform and normalize data for modeling readiness
• Build end-to-end data preprocessing pipelines

Module 2: Statistical Analysis & Probability

Estimated time: 2 hours

• Apply descriptive and inferential statistics to datasets
• Use probability distributions for data modeling
• Analyze correlations and significance in real-world data
• Interpret statistical results for decision-making

Module 3: Machine Learning Fundamentals

Estimated time: 2 hours

• Understand core concepts of supervised and unsupervised learning
• Train basic regression and classification models
• Apply ML algorithms to real-world datasets

Module 4: Model Evaluation & Optimization

Estimated time: 4 hours

• Evaluate model performance using appropriate metrics
• Apply cross-validation and hyperparameter tuning
• Optimize models for accuracy and generalization

Module 5: Data Visualization & Storytelling

Estimated time: 3 hours

• Create effective visualizations using best practices
• Communicate insights clearly to technical and non-technical audiences
• Tell compelling data-driven stories

Module 6: Advanced Analytics & Feature Engineering

Estimated time: 3 hours

• Engineer new features to improve model performance
• Apply advanced analytics techniques to complex datasets
• Review industry tools and best practices for scalable ML workflows

Prerequisites

• Familiarity with programming (preferably Python)
• Basic understanding of machine learning concepts
• Introductory knowledge of statistics and data analysis

What You'll Be Able to Do After

• Design and implement end-to-end data science pipelines
• Apply statistical methods to extract actionable insights from data
• Build, evaluate, and optimize machine learning models
• Create clear and impactful data visualizations
• Solve real-world problems using applied machine learning techniques