Deep Learning for Beginners with Python Course Syllabus

Overview: This course offers a comprehensive introduction to deep learning using Python, designed for learners with basic programming and math skills. Through hands-on labs, real-world case studies, and guided projects, you'll build foundational knowledge in neural networks, data analysis, and AI frameworks like TensorFlow and PyTorch. The curriculum spans approximately 15–18 hours of content, combining theory with practical application to prepare you for real-world AI challenges.

Module 1: Data Exploration & Preprocessing

Estimated time: 3 hours

• Interactive lab: Building practical solutions
• Discussion of best practices and industry standards
• Guided project work with instructor feedback
• Case study analysis with real-world examples

Module 2: Statistical Analysis & Probability

Estimated time: 3-4 hours

• Introduction to key concepts in statistical analysis & probability
• Hands-on exercises applying statistical analysis & probability techniques
• Review of tools and frameworks commonly used in practice
• Interactive lab: Building practical solutions

Module 3: Machine Learning Fundamentals

Estimated time: 2 hours

• Introduction to key concepts in machine learning fundamentals
• Hands-on exercises applying machine learning fundamentals techniques
• Case study analysis with real-world examples
• Interactive lab: Building practical solutions

Module 4: Model Evaluation & Optimization

Estimated time: 2-3 hours

• Interactive lab: Building practical solutions
• Discussion of best practices and industry standards
• Assessment: Quiz and peer-reviewed assignment

Module 5: Data Visualization & Storytelling

Estimated time: 1-2 hours

• Interactive lab: Building practical solutions
• Discussion of best practices and industry standards
• Guided project work with instructor feedback
• Assessment: Quiz and peer-reviewed assignment

Module 6: Advanced Analytics & Feature Engineering

Estimated time: 4 hours

• Introduction to key concepts in advanced analytics & feature engineering
• Guided project work with instructor feedback
• Discussion of best practices and industry standards
• Assessment: Quiz and peer-reviewed assignment

Prerequisites

• Basic knowledge of Python programming
• Familiarity with fundamental mathematical concepts (linear algebra, calculus)
• Understanding of basic data structures and logic

What You'll Be Able to Do After

• Create data visualizations that communicate findings effectively
• Apply statistical methods to extract insights from complex data
• Master exploratory data analysis workflows and best practices
• Work with large-scale datasets using industry-standard tools
• Design end-to-end data science pipelines for production environments