Machine Learning For Sports Betting – Mlb Edition

What you'll learn
Implement Regressors, Classifiers, and Gradient Boosting Machines to Accurately Model MLB Prop Bets
Replicate Your Own Synthetic Sportsbook
Build and Maintain a Robust Data Infrastructure
Forward Test a Betting Methodology Before Real-World Deployment
Requirements
Having an intermediate knowledge of Python will be useful, but beginners will also be able to follow along!
Description
Welcome to Machine Learning for Sports Betting: The MLB EditionBrought to you by Quant Galore, this course aims to give you the tools needed to apply machine learning techniques to the fascinating world of sports betting algorithms. By the end of the course, you will be able to:Train Regressors and Classifiers for MLB Player Props: Learn to model the probabilities associated with specific events in Major League Baseball (MLB) player performances. For instance, you will develop models to predict the likelihood of a batter recording a hit, strikeout, home run, or walk.Automatically calculate theoretical odds to create your own synthetic sportsbook: Acquire the skills to automatically compute theoretical odds, a pivotal aspect in creating your own synthetic sportsbook. Understanding the underlying calculations will enable you to establish accurate odds for various MLB events and maintain an edge.Build and maintain the necessary data infrastructure: Explore the intricacies of constructing and managing the essential data infrastructure required for machine learning in sports betting. You will learn how to gather, clean, preprocess, and store the relevant data, preparing it for effective model training.Finally, you will be able to forward test a methodology before putting the models into production for real-world action!This course also serves as an introduction to machine learning in general. We will be mainly using the low-code PyCaret module to train and deploy our models, so, if this is your first machine learning or data science project, you will still be able to easily follow along!We look forward to having you join us on this exciting journey!
Overview
Section 1: Technical Setup
Lecture 1 Environment Setup
Lecture 2 Database Setup
Lecture 3 Database Configuration
Lecture 4 Database Connection
Section 2: Building Our Dataset
Lecture 5 Building Training Data
Lecture 6 Training and Analyzing Our Model
Lecture 7 Building the Daily Lineup
Lecture 8 Daily Predictions & Theoretical Odds
Section 3: Betting Time!
Lecture 9 Bankroll Management, Sportsbook Selection & Optimal Approach
Lecture 10 Forward Testing & Live Deployment
Section 4: Additional Models
Lecture 11 Bonus Models Overview
Quantitative Traders Aiming to Branch Out to Related Topics,Data Scientists Looking to Leverage Their Skills in an Applied Setting,Sports Bettors Seeking a Quantitative, Systematic Approach,Anyone Interested! :)

Homepage

https://www.udemy.com/course/machine-learning-for-sports-betting/

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