Course curriculum

    1. Curriculum

    1. Week 1 Learning Objectives

    2. Version Control

    3. Git

    4. GitHub

    5. Commands in Git

    6. GitHub Flow

    7. Terminologies

    8. Resources

    1. Week 2 Learning Objectives

    2. Introduction to Machine Learning

    3. What is Machine Learning?

    4. Why Use Machine Learning?

    5. Types of Machine Learning

    6. Main Challenges of Machine Learning

    7. What are machine learning algorithms?

    8. Examples of ML models

    9. How to use a model

    10. Testing and Validating

    11. How to Approach a ML problem?

    12. Further Readings

    1. Week 3 Learning Objectives

    2. Required Downloads

    3. Business & Data Understanding - Monday

    4. Data Preparation - Tuesday

    5. Modeling - Wednesday

    6. Fine-Tune Your Model - Thursday

    7. Week 3 Practice Quiz

    1. Project Instructions

    2. Task: Business Understanding & Data Understanding

    3. Data Loading

    4. Dataset Splitting

    5. EDA Principles & Tools

    6. Useful Resources

    7. Project Submission

    8. End of Classification Project Survey

    1. Task: Fundamentals of Streamlit (Home & Data Page)

    2. Fundamentals Of Streamlit

    3. Building a Basic Streamlit Interface

    4. Useful Resources

About this course

  • Free
  • 68 lessons
  • 6.5 hours of video content

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