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Practical Data Science with Python, Machine Learning and AI

A broad, project-led path through Python, statistics, data preparation, visualization, SQL, machine learning, deep learning, and retrieval-based AI.

24Lesson topics · 24 published

24Course sections

24Lessons to explore

Beginner → AdvancedLearning path

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0 / 24 lessons completed

1. Introduction to Data Science 1

Introduction to Data Science

2. Understanding the Conda Environment 1

Understanding the Conda Environment

3. Python Refresher (For Data Science) 1

Python Refresher (For Data Science)

4. Claim your Free Developer Tools 1

Claim your Free Developer Tools

5. Project 1 - Coders of Delhi 1

Project 1 - Coders of Delhi

6. Project 2 - Coders of Bangalore 1

Project 2 - Coders of Bangalore

7. Data Analysis using Numpy 1

Data Analysis using Numpy

8. Data Analysis using Pandas 1

Data Analysis using Pandas

9. Data Visualization using Matplotlib and Seaborn 1

Data Visualization using Matplotlib and Seaborn

10. Data Collection, Requests, and Web Scraping 1

Data Collection, Requests, and Web Scraping

11. SQL for Data Science 1

SQL for Data Science

12. Probability 1

Probability

13. Probability Distributions and the Central Limit Theorem 1

Probability Distributions and the Central Limit Theorem

14. Machine Learning for Data Scientists 1

Machine Learning for Data Scientists

15. Types of Machine Learning Algorithms 1

Types of Machine Learning Algorithms

16. Practical ML using Scikit-learn 1

Practical ML using Scikit-learn

17. Deep Learning and Neural Networks 1

Deep Learning and Neural Networks

18. Web Development for Data Scientists 1

Web Development for Data Scientists

19. Large Language Models and Retrieval-Augmented Generation 1

Large Language Models and Retrieval-Augmented Generation

20. Leveraging AI as a Data Scientist 1

Leveraging AI as a Data Scientist

21. Git for Data Scientists 1

Git for Data Scientists

22. Using Google Colab for Data Science 1

Using Google Colab for Data Science

23. Project 3 - RAG based AI Teaching Assistant 1

Project 3 - RAG based AI Teaching Assistant

24. Conclusion 1

Conclusion

Welcome to the course

Start with the foundations

Build data-science foundations with Python, notebooks, NumPy, pandas, visualization, data collection, SQL, probability, and machine learning.

Continue into neural networks, web applications, language models, AI-assisted workflows, version control, cloud notebooks, and an original retrieval-based assistant project. The material is independently authored and emphasizes validation, privacy, and reproducibility.

Begin the first lesson