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Getting Started with Machine Learning in Python

Video Description

A+ guide to using Machine Learning to classify objects, predict future prices, and automatically learn fixes to problems

About This Video

  • Learn about supervised learning: how to classify data points and predict future numbers
  • Practical exercises on unsupervised learning: how to segment clients and cluster documents
  • Intuition-driven practical tour through Machine Learning, packed with step-by-step instructions, working examples, and helpful advice

In Detail

Machine Learning is a hot topic. And you want to get involved! From developers to analysts, this course aims to bring Machine Learning to those with coding experience and numerical skills.

In this course, we introduce, via intuition rather than theory, the core of what makes Machine Learning work. Learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. Use unlabelled datasets to do segmentation and clustering, so that you can separate a large dataset into sensible groups.

If you want to move past Excel and if-then-else into automatically learned ML solutions, this course is for you!

All the code and the supporting files are available on GitHub at - https://github.com/PacktPublishing/Getting-Started-with-Machine-Learning-in-Python-

Table of Contents

  1. Chapter 1 : Launching a Python Environment to Create Machine Learning Models
    1. The Course Overview 00:02:03
    2. Machine Learning versus Rule-Based Programming 00:14:15
    3. Understanding What Machine Learning Can Do Using the Tasks Framework 00:05:46
    4. Creating Machine-Learned Models with Python and scikit-learn 00:05:58
    5. Supervised Versus Unsupervised Learning 00:08:43
  2. Chapter 2 : Prepare Your Datasets for Machine Learning with Data Cleaning
    1. In this video, we will fix your machine learning models by understanding your data source 00:08:28
    2. Dealing with Missing Values – An Example 00:09:22
    3. Standardization and Normalization to Deal with Variables with Different Scales 00:07:51
    4. Eliminating Duplicate Entries 00:05:22
  3. Chapter 3 : Put Data into Their Right Categories with Classification
    1. How Do We Learn Rules to Classify Objects? 00:10:14
    2. Understanding Logistic Regression – Your First Classifier 00:07:48
    3. Applying Logistic Regression to the Iris Classification Task 00:06:27
    4. Closing Our First Machine Learning Pipeline with a Simple Model Evaluator 00:05:51
  4. Chapter 4 : Predict Numbers in the Future with Regression
    1. Creating Formulas That Predict the Future – A House Price Example 00:08:06
    2. Understanding Linear Regression – Your First Regressor 00:05:58
    3. Applying Linear Regression to the Boston House Price Task 00:05:10
    4. Evaluating Numerical Predictions with Least Squares 00:05:11
  5. Chapter 5 : Unsupervised Learning: Segmenting Groups and Detecting Outliers
    1. Exploring Unsupervised Learning and Its Usefulness 00:07:24
    2. Finding Groups Automatically with K-means Clustering 00:05:17
    3. Reducing the Number of Variables in Your Data with PCA 00:05:08
    4. Smooth out Your Histograms with Kernel Density Estimation 00:03:49
  6. Chapter 6 : Modeling Complex Relationships with Nonlinear Models
    1. Create Explainable Models with Decision Trees 00:09:24
    2. Automatic Feature Engineering with Support Vector Machines 00:06:56
    3. Deal with Nonlinear Relationships with Polynomial Regression 00:06:24
    4. Reduce the Number of Learned Rules with Regularization 00:06:20