Python Machine Learning Bootcamp

Python Machine Learning Bootcamp, available at $44.99, has an average rating of 4.5, with 102 lectures, based on 9 reviews, and has 162 subscribers.

You will learn about How to take a machine learning idea and flush it out into a fully functioning project The different types of machine learning approaches and the models within each section Theoretical and intuitive understanding of how each model works Practical application and implementing each model we cover How to optimize models Common pitfalls and how to overcome them Technical skills to use machine learning on the job or for your own projects This course is ideal for individuals who are Beginner Python programers and data scientists who want to understand ML models in depth and be able to use them in practice It is particularly useful for Beginner Python programers and data scientists who want to understand ML models in depth and be able to use them in practice.

Enroll now: Python Machine Learning Bootcamp

Summary

Title: Python Machine Learning Bootcamp

Price: $44.99

Average Rating: 4.5

Number of Lectures: 102

Number of Published Lectures: 102

Number of Curriculum Items: 102

Number of Published Curriculum Objects: 102

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

How to take a machine learning idea and flush it out into a fully functioning project
The different types of machine learning approaches and the models within each section
Theoretical and intuitive understanding of how each model works
Practical application and implementing each model we cover
How to optimize models
Common pitfalls and how to overcome them
Technical skills to use machine learning on the job or for your own projects

Who Should Attend

Beginner Python programers and data scientists who want to understand ML models in depth and be able to use them in practice

Target Audiences

Beginner Python programers and data scientists who want to understand ML models in depth and be able to use them in practice

Machine learning is continuously growing in popularity, and for good reason. Companies that are able to make proper use of machine learning can solve complex problems that otherwise proved very difficult with standard software development.

However, building good machine learning models is not always easy, and it’s very important to have a solid foundation so that if/when you encounter problems with models on the job, you understand what steps to take to fix them.

That’s why this course focuses on always introducing every model that we cover first with the theoretical background of how the model works, so that you can build a proper intuition around its behaviour. Then we’ll have the practical component, where we’ll implement the machine learning model and use it on actual data. This way you gain both hands-on, as well as a solid theoretical foundation, of how the different machine learning models work, and you’ll be able to use this knowledge to better chose and fix models, depending on the situation.

In this course we’ll cover many different types of machine learning aspects.

We’ll start with going through a sample machine learning project from idea to developing a final working model. We’ll learn many important techniques around data preparation, cleaning, feature engineering, optimizaiton and learning techniques, and much more.

Once we’ve gone through the whole machine learning project we’ll then dive deeper into several different areas of machine learning, to better understand each task, and how each of the models we can use to solve these tasks work, and then also using each model and understanding how we can tune all the parameters we learned about in the theory components.

These different areas that we’ll dive deeper in to are:

– Classification

– Regression

– Ensembles

– Dimensionality Reduction

– Unsupervised Learning

At the end of this course you should have a solid foundation of machine learning knowledge. You’ll be able to build out machine learning solutions to different types of problems you’ll come across, and be ready to start applying machine learning on the job or in technical interviews.

Course Curriculum

Chapter 1: Pre-Machine Learning Steps

Lecture 1: Setup & Installation

Lecture 2: Loading Datasets

Lecture 3: Data Format

Lecture 4: Train Test Splitting

Lecture 5: Stratified Splitting

Lecture 6: Data Preparation and Exploration

Chapter 2: Machine Learning Workflow

Lecture 1: Supervised Learning Intro

Lecture 2: Classification Intro

Lecture 3: Logistic Regression Theory

Lecture 4: Gradient Descent

Lecture 5: Types of Classification Problems

Lecture 6: Creating and Training a Binary Classifier

Lecture 7: Creating and Training a Multiclass Classifier

Lecture 8: Evaluating Classifiers Theory

Lecture 9: Precision and Recall Theory

Lecture 10: ROC, Confusion Matrix, and Support Theory

Lecture 11: MNIST Data Set Intro

Lecture 12: Evaluating Classifiers Practical

Lecture 13: Validation Set

Lecture 14: Cross-Validation

Lecture 15: Hyperparameters

Lecture 16: Regularization Theory

Lecture 17: Generalization Error Sources

Lecture 18: Regularization Practical

Lecture 19: Grid and Randomized Search

Lecture 20: Handling Missing Values

Lecture 21: Feature Scaling Theory

Lecture 22: Feature Scaling Practical

Lecture 23: Text and Categorical Data

Lecture 24: Transformation Pipelines

Lecture 25: Custom Transformers

Lecture 26: Column Specific Pipelines

Lecture 27: Over and Undersampling

Lecture 28: Feature Importance

Lecture 29: Saving and Loading Models and Pipelines

Lecture 30: Post Prototyping

Chapter 3: Classification

Lecture 1: Multilabel Classification

Lecture 2: Polynomial Features

Lecture 3: SVM Theory

Lecture 4: SVM Classification Practical

Lecture 5: KNN Classification Theory

Lecture 6: KNN Classification Practical

Lecture 7: Decision Tree Classifier Theory

Lecture 8: Decision Tree Pruning

Lecture 9: Decision Tree Practical

Lecture 10: Random Forest Theory

Lecture 11: Random Forest Practical

Lecture 12: Naive Bayes Theory

Lecture 13: Naive Bayes Practical

Lecture 14: How to Choose a Model

Chapter 4: Regression

Lecture 1: Regression Intro

Lecture 2: Linear Regression Practical

Lecture 3: Regularized Linear Regression Practical

Lecture 4: Boston Housing Intro

Lecture 5: Polynomial Regression

Lecture 6: Regression Losses and Learning Rates

Lecture 7: SGD Regression

Lecture 8: KNN Regression Theory

Lecture 9: KNN Regression Practical

Lecture 10: SVM Regression Theory

Lecture 11: SVM Regression Practical

Lecture 12: Decision Tree Regression Theory

Lecture 13: Decision Tree and Random Forest Regression Practical

Lecture 14: Additional Regression Metrics

Chapter 5: Ensembles

Lecture 1: Ensembles Intro

Lecture 2: Voting Ensembles Theory

Lecture 3: Voting Classification Practical

Lecture 4: Voting Regression Practical

Lecture 5: Bagging and Pasting Theory

Lecture 6: Bagging and Pasting Classification Practical

Lecture 7: Bagging and Pasting Regression Practical

Lecture 8: AdaBoost Theory

Lecture 9: AdaBoost Classification Practical

Lecture 10: AdaBoost Regression Practical

Lecture 11: Gradient Boosting Theory

Lecture 12: Gradient Boosting Classification Pratical

Lecture 13: Gradient Boosting Regression Practical

Lecture 14: Stacking and Blending Theory

Lecture 15: Stacking Classifiers Practical

Lecture 16: Stacking Regression Practical

Chapter 6: Dimensionality Reduction

Lecture 1: Dimensionality Reduction Intro

Lecture 2: PCA Theory

Lecture 3: PCA Practical

Lecture 4: NNMF Theory

Lecture 5: NNMF Practical

Lecture 6: Isomap Theory

Lecture 7: Isomap Practical

Lecture 8: LLE Theory

Lecture 9: LLE Practical

Lecture 10: t-SNE Theory

Lecture 11: t-SNE Practical

Chapter 7: Unsupervised Learning

Lecture 1: Unsupervised Learning Intro

Lecture 2: KMeans Theory

Instructors

Max S
Data Engineer

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2 stars: 0 votes
3 stars: 1 votes
4 stars: 4 votes
5 stars: 4 votes

Frequently Asked Questions

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