PYTHON BUNDLE NOW AVAILABLE

TRUSTED BY

Feeling overwhelmed by

Image

The complexity of applying machine learning techniques

Image

Selecting the right algorithms for real-world problems

Image

Tuning hyperparameters and evaluating model performance effectively

Image

Building automated pipelines for data preprocessing and model deployment

Image

Understanding advanced concepts like neural networks, tree-based methods, and Bayesian approaches

What you’ll learn

In this course, you'll master essential techniques and concepts, including:

Introduction to Machine Learning

  • Understand the fundamentals of machine learning and its applications in sports analytics.

  • Explore various types of sports datasets and their unique challenges.

  • Get a comprehensive overview of the course structure and objectives.

Linear Regression and Model Pipelines

  • Master linear regression and its applications in sports, like predicting PGA Tour drive distances.

  • Learn normalization, regularization, and performance evaluation techniques.

  • Build complete machine learning pipelines for data preprocessing and modeling.

Exploratory Data Analysis & Feature Engineering

  • Understand the importance of exploratory data analysis for sports datasets.

  • earn advanced feature engineering techniques to improve model performance.

  • Visualize data trends to derive actionable insights.

Hyperparameter Tuning & Model Selection

  • Dive into cross-validation and hyperparameter tuning to optimize models.

  • Learn techniques for selecting the best-performing models.

  • Evaluate models using advanced performance metrics.

Advanced Machine Learning Techniques in Sports

  • Explore tree-based methods, neural networks, and generalized additive models.

  • Build applications like NBA in-game win probability and WNBA shot predictions.

  • Automate pipelines with Pycaret and monitor model performance effectively.

Course Overview

Step into the world of advanced sports analytics with our comprehensive Python MachineZ

course. Designed to take your machine learning skills to the next level, this course explores cutting-edge techniques like neural networks, tree-based models, and automated pipelines. From feature engineering to hyperparameter tuning, master the tools and techniques needed to tackle real-world sports data challenges. Join us on this immersive journey and redefine what's possible with machine learning in sports!

  1. Introduction to Advanced Machine Learning in Sports Analytics

1.1 Machine Learning in Sports

  •  Discover how machine learning is revolutionizing sports analytics by enabling smarter decisions through data-driven insights.

1.2 Overview of Sports Data

  • Learn about the structure and unique challenges of sports datasets, including player performance metrics, game statistics, and tracking data.

1.3 Overview of Machine Learning

  • Gain a high-level understanding of machine learning concepts, algorithms, and workflows, tailored for real-world applications.

1.4 Syllabus Review

  • Explore the roadmap for the course, highlighting key topics like feature engineering, neural networks, and advanced predictive modeling.

  1. Linear Regression and The Machine Learning Pipeline

2.1 Linear Regression

  • Understand the foundational principles of linear regression and its importance in predictive modeling.

2.2 Least Squares

  • Learn how the least squares method minimizes errors to optimize model performance in regression analysis.

2.3 Predicting PGA Tour Drive Distance

  • Apply linear regression techniques to predict PGA Tour drive distances, using real-world sports data.

2.4 Normalizing and Regularization

  • Explore how normalizing data and applying regularization techniques improve model accuracy and prevent overfitting.

2.5 Model Construction and Performance

  • Build linear regression models step-by-step.

  • Evaluate model performance with metrics like R² and mean squared error.

  • Identify areas for improvement using data insights.

  1. Exploratory Data Analysis and Feature Engineering

3.1 Motivating Data Visualization

  • Understand why effective data visualization is crucial for uncovering insights and communicating patterns in sports datasets.

3.2 Data Visualization Fundamentals

  • Learn the basics of creating impactful visualizations, including choosing the right chart types and customizing plots for clarity.

3.3 Motivating Feature Engineering

  • Discover how feature engineering improves model performance by capturing meaningful relationships in the data.

  • Explore real-world examples of feature creation in sports analytics.

  • Understand the role of domain knowledge in selecting relevant features.

3.4 Feature Engineering Techniques

  • Learn practical techniques for transforming raw data into predictive features, including one-hot encoding, scaling, interaction terms, and polynomial features.

  1. Hyperparameter Tuning and Model Selection

4.1 Introduction to Hyperparameters

  • Learn what hyperparameters are and how they differ from model parameters, along with their critical role in controlling machine learning model behavior.

4.2 Cross Validation

  • Understand the concept of splitting data into training and testing sets.

  • Explore different cross-validation techniques, such as k-fold, leave-one-out, and stratified cross-validation.

  • Apply cross-validation to evaluate model performance and prevent overfitting.

4.3 Hyperparameter Tuning

  • Discover strategies for optimizing hyperparameters using techniques like grid search, random search, and automated tuning methods.

4.4 Model Selection

  • Learn how to choose the best-performing model based on evaluation metrics.

  • Compare models across different algorithms and parameter settings.

  • Develop an understanding of trade-offs between complexity and accuracy in machine learning models.

  1. Model Performance Evaluation

5.1 Performance Metrics

  • Understand key performance metrics such as accuracy, precision, recall, and F1-score.

  • Learn regression-specific metrics like R², RMSE, and MAE for evaluating continuous predictions.

  • Explore ROC curves and AUC for classification problems.

5.2 Choosing Evaluation Criteria

  • Learn how to select appropriate evaluation metrics based on the context and objectives of your sports analytics projects.

5.3 Exploring Model Behavior

  • Analyze residuals and errors to understand where the model performs well and where it struggles.

  • Examine feature importance and sensitivity to interpret how the model makes decisions.

5.4 Identifying Areas for Future Research

  • Identify limitations in current models and explore opportunities for improvement.

  • Discuss how to expand models with additional features or advanced techniques.

  • Plan next steps for iterating on your sports analytics models.

  1. Automating Machine Learning Pipelines

6.1 Motivating Machine Learning Pipelines

  • Understand why automating machine learning workflows is essential for scalability, efficiency, and repeatability in data analysis.

6.2 Sklearn Pipelines

  • Learn how to create end-to-end pipelines using Scikit-learn for data preprocessing, feature engineering, and model building.

6.3 Pycaret

  • Explore Pycaret, a low-code machine learning library, to streamline the process of model selection, tuning, and evaluation.

6.4 PGA Tour Drive Distance with Pycaret

  • Apply Pycaret to build and evaluate models for predicting PGA Tour drive distances using sports analytics data.

6.5 Model Monitoring

  • Discover best practices for monitoring model performance over time, ensuring reliability, and detecting issues like drift or performance degradation.

  1. Generalized Linear Models

7.1 Motivating Generalized Linear Models

  • Understand the limitations of traditional linear models and discover the flexibility and power of generalized linear models (GLMs) in addressing complex problems in sports analytics.

7.2 Outcome Distributions

  • Explore different types of outcome distributions (e.g., Gaussian, Poisson, Binomial) and their applications.

  • Learn how selecting the right distribution impacts the performance of your GLM.

7.3 Link Functions

  • Understand the role of link functions in connecting linear predictors to outcome distributions.

  • Dive into commonly used link functions like logit, log, and identity.

7.4 NFL Field Goal Probability

  • Analyze NFL field goal data to build a GLM that predicts the probability of successful field goals.

  • Explore features such as kick distance, weather conditions, and kicker statistics.

  • Apply the learned concepts of outcome distributions and link functions to evaluate model accuracy.

  1. Generalized Additive Models

8.1 Motivating Generalized Additive Models

  • Understand the flexibility of GAMs in modeling non-linear relationships by combining linear models with non-parametric smoothing functions.

8.2 Splines

  • Learn how splines are used to create smooth curves for non-linear data.

  • Explore different types of splines, such as cubic and B-splines.

  • Understand the role of knots in controlling spline smoothness and flexibility.

8.3 Basis Functions

  • Discover how basis functions transform features into new representations, enabling non-linear modeling in generalized additive models.

8.4 NBA In-Game Win Probability

  • Apply generalized additive models to predict NBA in-game win probability, accounting for dynamic factors such as score margin and time remaining.

  1. Tree-based Methods

9.1 Motivating Tree-based Methods

  • Understand the strengths of tree-based methods for handling complex data structures, non-linear relationships, and high-dimensional datasets in sports analytics.

9.2 Decision Trees

  • Learn the fundamentals of decision trees, including how they split data based on features to create interpretable models.

9.3 Bagging

  • Discover how bagging (Bootstrap Aggregating) improves model performance by reducing variance through ensemble methods.

9.4 Boosting

  • Explore boosting techniques to sequentially improve weak models by minimizing errors and achieving higher accuracy.

9.5 Random Forest

  • Understand how random forests aggregate multiple decision trees to enhance predictive performance and reduce overfitting.

9.6 Gradient Boosting Machines

  • Learn the mechanics of gradient boosting and its applications in optimizing complex datasets, balancing bias, and variance.

9.7 WNBA Shot Probability

  • Apply tree-based methods to predict WNBA shot probabilities based on game scenarios and player attributes.

  • Understand how feature importance analysis improves interpretability.

  • Compare model performance across different tree-based algorithms.

  1. Neural Networks

10.1 Motivating Neural Netowrks

  • Learn why neural networks are a powerful tool for modeling complex, non-linear relationships in sports analytics and beyond.

10.2 Neural Network Architecture

  • Understand the fundamental building blocks of neural networks, including layers, nodes, and activation functions.

10.3 Feedforward Neural Networks

  • Dive into feedforward neural networks and how they pass data through layers to make predictions or classifications.

10.4 Backpropagation

  • Explore the backpropagation algorithm for training neural networks by minimizing errors using gradient descent.

10.5 Neural Network Hyperparameters

  • Learn about key hyperparameters such as learning rate, batch size, and epochs, and their role in optimizing model performance.

10.6 Other Types of Neural Networks

  • Discover advanced architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and their applications.

10.7 Revisiting NBA In-Game Win Probability

  • Apply neural networks to model in-game win probabilities for NBA games.

  • Analyze how advanced neural network techniques improve predictions.

  • Understand how to interpret model outputs in a real-world context.

  1. Introduction to Applied Bayesian Statistics

11.1 Motivating Bayesian Statistics

  • Explore why Bayesian methods are uniquely suited for incorporating prior knowledge and handling uncertainty in sports analytics.

11.2 Bayes' Theorem

  • Understand the foundational principle of Bayesian statistics, combining prior beliefs with observed data to update probabilities.

11.3 Sampling

  • Learn about sampling techniques like Markov Chain Monte Carlo (MCMC) and how they are used in Bayesian inference.

11.4 Introduction to PyMC

  • Get introduced to PyMC, a powerful Python library for Bayesian modeling, and learn how to construct probabilistic models.

11.5 Revisiting WNBA Shot Probability

  • Apply Bayesian methods to model shot probabilities in the WNBA.

  • Use PyMC to implement and analyze Bayesian models.

  • Evaluate model predictions and interpret results in a real-world context.

meet Our Python Pro data science coach

Pat McFarlane

Image

Passionate about the intersection of data science and sports analytics.

As the Director of Predictive Modeling at the Philadelphia Phillies, I bring extensive experience in sports analytics and data science to our Python curriculum.

My background in aerospace engineering and passion for sports data will guide you in mastering Python for data analysis.

I hold a BS in Aerospace Engineering from the University of Notre Dame and a Master’s degree from MIT. This rigorous quantitative foundation has equipped me with the skills to develop predictive models for in-game strategy, player acquisition, and decision-making in baseball.

My career spans aviation safety, finance, consulting, and now sports analytics with the Phillies. This diverse experience allows me to blend my love for sports with data science expertise.

After jumpstarting your Python journey with Evan in FoundationZ of Data Science, get ready to join me in the "Python MachineZ in Sports" course to unlock take your machine learning skills to the next level. -- Coming soon (January 2025).

What Industry Experts Are saying About Athlyticz

Discover what our students have to say about their experience with AthlyticZ:

"Here's your chance to learn future sports data gurus!"

"AthlyticZ has completely transformed the learning approach to data science through the use of sports-based problems. The course structure is intuitive, the content is comprehensive, the instructors are the best of the best, and the practical projects have immediate impact to students."

BILL GEIVETT M.ED.

PRESIDENT @ IMA TEAM

AUTHOR: DO YOU WANT TO WORK IN BASEBALL?

SR. VP: COLORADO ROCKIES 2001-2014

ASST. GM: LOS ANGELES DODGERS 1998-2000

"Empowering and Engaging"

"I've always been intrigued by the intersection of sports analytics and data science. AthlyticZ provides students with the perfect platform to explore this passion. The hands-on projects are particularly empowering, allowing them to apply theoretical concepts to real-world scenarios."

BRAD SMITH PHD

SENIOR PERFORMANCE ANALYST @ UNIVERSITY OF NEBRASKA

SPORTS ANALYTICS PROFESSOR, DATA SCIENCE PROGRAM @ NORTHWESTERN

PLAYER DEVELOPMENT ANALYST @ NEW YORK YANKEES 2018-2021

How We Conduct Our Course

At AthlyticZ, we've designed our course structure to empower your learning journey at your own pace, ensuring flexibility and accessibility.

Prerecorded Video Lessons

Delve into our comprehensive course content with prerecorded video lessons, allowing you to learn at your convenience and revisit concepts as needed.

Assignments and Exercises

Engage with assignments and exercises that reinforce your understanding of key concepts and help you build practical skills.

Continuous Assessment

Benefit from quizzes and assessments integrated throughout the course, providing you with ongoing feedback on your progress and understanding.

Hands-On Projects

Apply your knowledge to real-world projects, including both minor and major assignments, where you'll solve sports analytics problems using the skills you've acquired.

Supplementary Resources

Learn from additional resources, including readings and videos provided directly from the instructor, to complement the primary lessons and enhance the learning experience.

Interactive Features

Utilize interactive features within each lesson, including note-taking capabilities and custom-built IDEs with preloaded code, ensuring an immersive and engaging learning environment.

Python Machinez

Course Information

  • 50+ Lessons of engaging content

  • Asynchronous, self-paced learning

  • Pre-recorded video lessons for flexible scheduling

  • Interactive Features: Quizzes throughout the course to reinforce learning

  • Supplementary Resources: Access to downloadable slides, readings, and additional materials

  • Hands-On Projects: Dive into major case study projects tailored to your interests in sports.

  • Virtual Machines: Immerse yourself in our customized IDE solutions for real world programming experience.

$899 usd

Frequently Asked Questions

1. Can I expense this course with my employer?

Discover the transformative power of mastering data science with our "Foundationz of Data Science" course.

Here's how your employer can leverage our course:

  • Streamline Data Analysis

  • Equip your team with essential Python skills to streamline data analysis processes, leading to faster insights and informed decision-making.

  • Optimize Workflows

  • Create customized data workflows tailored to your company's needs, optimizing efficiency and reducing manual data manipulation tasks.

  • Cost-Efficient SolutionsSave on commercial software expenses by utilizing open-source Python packages and tools for comprehensive data analysis and visualization.

    • Empower your team to excel in data analysis and drive business growth with our comprehensive "Foundationz of Data Science" course.

2. Do I need access to any commercial software?

No, you won't need any commercial software for this course.

3. Does the course assume extensive knowledge of Python?

Somewhat- this is an intermediate to advanced course in Python. We recommend having a strong grasp of materials from our FoundationZ of Data Science Course.

4. Is the course graded? What defines successful completion?

Evaluation is based on the completion of course work rather than "correct answers." Successful completion entails:

• 80% or higher on all quizzes

• 100% of modules completed

Upon meeting these criteria, you'll receive a certificate of completion.

5. What if I need to drop out? Is there a refund policy?

At AthlyticZ, we're committed to your success and satisfaction throughout your learning journey.Our refund policy is as follows:You can drop the course within 3 days of commencement for a full refund. After this date, refunds are not provided.

No refunds will be granted if you have completed 25% or more of the course material, regardless of the time elapsed since the course began.

Thank you for choosing AthlyticZ for your educational needs.

© 2024 AthlyticZ

Designed With ❤️ By Jackson Yew

Privacy Policy · Terms of Use · Terms of Sale ·  Email questions to : [email protected]