by Thomas Snell | Released March 2018 | ISBN: 9781788995580

Get hands-on with machine learning using Python.About This VideoGet to grips with supervised and unsupervised Machine Learning by working with hands-on examples.Implement Machine Learning solutions in Scikit-Learn and Python step by step.Overcome real-world drawbacks such as overfitting and produce stable, generalizable, and effective solutions.In DetailGiven the constantly increasing amounts of data they're faced with, programmers have to come up with better solutions to make machines smarter and reduce manual work. In this Machine Learning course, you'll use Python to craft better solutions and process them effectively.We start by focusing on key ML algorithms and how they can be trained for classification and regression. We will also work with Supervised and Unsupervised learning to help to get to grips with both types of algorithm. We will use the highly popular Scikit-learn library throughout the course while performing various ML tasks.By the end of the course, you will be adept at using the concepts and algorithms involved in Machine Learning. This is a highly practical course and will equip you with sufficient hands-on training to help you implement ML skills right after finishing the course.All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Learn-Machine-Learning-in-3-Hours Show and hide more Publisher Resources Download Example Code

1. Chapter 1 : Setting Up a Machine Learning Project in Scikit-Learn
   • The Course Overview 00:03:21
   • Operation of an Unsupervised Machine Learning Algorithm 00:04:14
   • Operation of a Supervised Machine Learning Algorithm 00:03:19
   • Avoid Overfitting and Splitting Data into Training and Testing Sets 00:07:39
   • Data Cleaning, Conversion, and Preprocessing 00:06:12
   • Using PCA to Easily Explore and Visualize Data 00:08:59
2. Chapter 2 : Unsupervised K-Means Clustering in Scikit-Learn
   • What Does the Unsupervised K-Means Clustering Algorithm Do? 00:02:13
   • Example Problem 00:01:10
   • Data Preparation and Processing 00:03:39
   • Implementing K-Means Clustering 00:05:22
   • Improving Performance and Hyperparameter Fitting 00:05:20
3. Chapter 3 : Supervised K-Nearest-Neighbor Classification in Scikit-Learn
   • Operation of the K-Nearest-Neighbor Classification Algorithm 00:02:29
   • Example Problem 00:01:09
   • Data Preparation and Processing 00:02:57
   • Implementing K-Nearest-Neighbor Classification 00:05:46
   • Improving Performance and Hyperparameter Fitting 00:08:36
4. Chapter 4 : Supervised Support Vector Machine Classification in Scikit-Learn
   • Operation of the Support Vector Machine Classification Algorithm 00:02:20
   • Example Problem 00:01:06
   • Data Preparation and Processing 00:04:30
   • Implementing Support Vector Machine Classification 00:06:21
   • Improving Performance and Hyperparameter Fitting 00:07:34
5. Chapter 5 : Support Vector Machine Regression in Scikit-Learn
   • Operation of the Support Vector Machine Regression Algorithm 00:01:53
   • Example Problem 00:01:00
   • Data Preparation and Processing 00:04:06
   • Implementing Support Vector Machine Regression 00:02:37
   • Improving Performance and Hyperparameter Fitting 00:06:31
6. Chapter 6 : Supervised Gradient Boosting in Scikit-Learn
   • Operation of the Gradient Boosting Algorithm 00:03:36
   • Example Problem 00:01:16
   • Data Preparation and Processing 00:05:37
   • Implementing Gradient Boosting Classification 00:05:40
   • Improving Performance and Hyperparameter Fitting 00:07:31
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