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Building Predictive Models with Machine Learning and Python

Video Description

Master the most popular Machine Learning tools by building your own models to tackle real-world problems

About This Video

  • Learn the tools that make each stage in building a Machine Learning-based model easy and fast.
  • Master how to get models working and make predictions by building models over and over again in a project-based teaching style.
  • Use Jupyter Notebooks to write and test your code in an interactive way.

In Detail

Machine Learning is no longer the inaccessible domain it used to be. There are over 100,000 Python libraries you can download in one line of code!

This course will introduce you to tools with which you can build predictive models with Python, the core of a Data Scientist's toolkit. Through some really interesting examples, the course will take you through a variety of challenges: predicting the value of a house in Boston, the batting average of a baseball player, their survival chances had they been on the Titanic, or any other number of other interesting problems.

Once you master the content of the course, you can level-up your knowledge of the Python Data Analytics and Machine Learning stack by exploring these recommended libraries.

This course will guide you through the tools in the Python ecosystem that Data Scientists use to get results in a matter of hours - and with practice - in a matter of minutes. The best way to learn is through examples, and this course will guide you through all the steps needed to train and test your models by tackling several classifications and regression challenges.

By the end of the course, you will be able to take the Python Machine Learning toolkit we cover and apply it to your own projects to deploy models in just a few lines of code.

All the code and supporting files are available on GitHub at: https://github.com/PacktPublishing/Building-Predictive-Models-with-Machine-Learning-and-Python-

Table of Contents

  1. Chapter 1 : Getting Started with Python Machine Learning Stack
    1. The Course Overview 00:02:14
    2. Introduction to Machine Learning 00:07:46
    3. Meet the Python Machine Learning Stack 00:09:08
    4. Making Sure It Works in Your Computer 00:12:41
  2. Chapter 2 : Your First Model – Classifying Iris Flowers by Petal Length and Width
    1. Exploring Your First Dataset 00:10:20
    2. Building Your First Model 00:10:14
    3. Assessing Your Model 00:08:00
  3. Chapter 3 : Solving Your First Challenge – Tackling Bad Data with Pandas
    1. Finding Issues with Your Data 00:10:48
    2. Using Pandas to Get Your Data Ready for Modeling 00:11:44
    3. Building a Model to Assess Your Chances of Surviving the Titanic 00:07:46
  4. Chapter 4 : Exploring Various Model Types – SVM’s, Linear Models, Random Forests
    1. What Makes Models Truly Different? 00:08:08
    2. Understanding the Advantages and Shortcomings of the Most Popular Models 00:04:10
    3. Trying (and Failing) to Use an SVM, a Random Forest and a Linear Model 00:06:37
    4. Fixing Our Issues with Our SVM Model 00:04:30
    5. Fixing Our Issues with the Random Forest Model 00:04:33
  5. Chapter 5 : Grid Search – Tuning Your Model for Best Results
    1. What Does it Mean to Tune a Model (Theory)? 00:04:33
    2. Grid Search – Just Try Everything! 00:02:26
    3. Tune a Linear Model to Predict House Prices 00:06:05
    4. Tune an SVM to Predict a Politician’s Party Based on Their Voting Record 00:07:49
  6. Chapter 6 : Keep Challenging Yourself
    1. Advanced Libraries for Machine Learning 00:14:16
    2. Good Next Steps – Kaggle, Hackathons, YouTube Channels, and More 00:13:36