Probability / Statistics - The Foundations of Machine Learning
Real-world, code-oriented learning s to use probability/statistics in all of CS, data science, and machine learning
Created by Mohammad Nauman
Build a strong foundation in probability and statistics with a hands-on, code-first approach. You'll focus on the concepts that matter most for data science, machine learning, and computer science. By working through real coding examples, you'll see how these ideas apply directly to real-world problems.
Packt | Jun 2022 | 394 min
What You Will Learn
You'll learn by writing and running code that demonstrates each concept, moving quickly from theory to practical application. The focus stays on the most useful ideas for data science and machine learning, helping you build intuition and confidence as you progress. Complex math is kept to a minimum so you can concentrate on what matters.
Key Features
- Apply probability and statistics concepts directly through code
- Understand key ideas like distributions, entropy, and Bayesian inference
- Gain practical skills for data science and machine learning projects
Target Audience
Perfect for beginners in machine learning or data science who want a solid, practical foundation. If you're a developer curious about how probability powers modern analysis or looking to break into big data, you'll find clear explanations and actionable skills to help you move forward.
