Machine Learning Course Syllabus

Overview: This comprehensive Machine Learning Engineer Master’s Program guides beginners through a structured, industry-aligned curriculum spanning Python fundamentals to advanced AI techniques. With over 200 hours of content, the course blends theory with hands-on labs and real-world projects, preparing learners for roles in AI and data science. The 30-week recommended timeline ensures deep understanding and practical mastery across ten modules, culminating in a capstone project that showcases end-to-end ML pipeline development and deployment.

Module 1: Python & Statistics for Data Science

Estimated time: 20 hours

• Python essentials for data analysis
• NumPy and Pandas for data manipulation
• Descriptive statistics and data summarization
• Probability distributions and statistical inference

Module 2: Python Certification Training

Estimated time: 24 hours

• Advanced Python constructs and scripting
• Object-oriented programming in Python
• File I/O and exception handling
• Python modules and code organization

Module 3: Python Machine Learning Certification

Estimated time: 30 hours

• Scikit-learn APIs for model development
• Supervised learning: regression and classification
• Unsupervised learning: clustering algorithms
• Model evaluation metrics and validation techniques

Module 4: Advanced Artificial Intelligence

Estimated time: 35 hours

• Ensemble methods: random forests and gradient boosting
• Advanced feature engineering techniques
• Building recommendation systems
• Hands-on implementation of real-world AI solutions

Module 5: PySpark Certification Training

Estimated time: 24 hours

• RDD and DataFrame APIs in Spark
• Spark SQL for big data querying
• MLlib for scalable machine learning pipelines
• Performance tuning in distributed environments

Module 6: Final Project

Estimated time: 20 hours

• Design and implement an end-to-end machine learning pipeline
• Deploy models on cloud platforms using MLOps best practices
• Publish a portfolio-ready capstone project with API integration

Prerequisites

• Basic understanding of programming concepts
• Familiarity with high school-level mathematics
• Access to a computer with internet and Python environment setup

What You'll Be Able to Do After

• Implement end-to-end machine learning workflows using Python and Spark
• Design, train, and evaluate models for regression, classification, and clustering
• Build deep learning solutions for NLP and computer vision using TensorFlow
• Apply advanced AI techniques like ensemble learning and recommendation systems
• Deploy scalable ML pipelines on cloud platforms and showcase projects in a professional portfolio