Data Science: Bayesian Linear Regression in Python

Dive into Bayesian Linear Regression and unlock a powerful approach to machine learning! This course is a foundational step in Bayesian Machine Learning, building upon your existing knowledge of traditional linear regression.

This series, returning after years of development, starts with the core concepts of Bayesian statistics and applies them to a familiar model. You might be wondering, "Where's the 'machine learning' in this?". While the initial course focused on A/B Testing, this course directly tackles Bayesian Machine Learning using the well-understood framework of linear regression.

Why Bayesian Linear Regression?

• Easy Transition: A smooth progression from standard (non-Bayesian) linear regression.
• Closed-Form Solutions: Benefit from efficient mathematical solutions.
• Practical Applications: Linear Regression (and its Bayesian counterpart) is widely used in real-world scenarios. Learn to apply Bayesian Machine Learning concepts to solve practical problems.
• Gradual Mathematical Introduction: We'll introduce complex mathematical concepts incrementally, keeping the focus on real-world applications. Avoid feeling overwhelmed by the math!

What to Expect:

Be prepared for a mathematically rigorous course. This isn't a "plug-and-play" experience like scikit-learn. Expect significant algebraic manipulation. However, the elegance and satisfying results of Bayesian Machine Learning make the effort worthwhile.

Prerequisites:

• Python Coding: Familiarity with if/else, loops, lists, dictionaries, and sets.
• Numpy & Pandas: Comfortable with matrix and vector operations and loading data from CSV files.
• Basic Math: Solid understanding of calculus, linear algebra, and probability.
• Linear Regression: A working knowledge of standard linear regression techniques.
• Bayesian Machine Learning: Prior knowledge of A/B Testing with Bayesian concepts (conjugate priors).

This course is ideal for those looking to deepen their understanding of Bayesian Machine Learning and gain a powerful tool for data analysis and prediction.