by Taylor Smith | Released March 2018 | ISBN: 9781788991056

Understand and implement the best Machine Learning practices with the help of powerful features of Python and scikit-learnAbout This VideoDeep dive into Machine Learning using the most advanced tools and the Scikit library.Develop complex pipelines and process data through manipulation, extraction, and data-cleansing techniques.Clean coding techniques and best practices in Machine Learning which are applicable to any scalable Machine Learning projects.In DetailMachine learning and artificial intelligence are the new big data—at least as far as buzzwords in the workplace go. The scikit-learn library is one of the most popular platforms for everyday Machine Learning and data science because it is built upon Python, a fully featured programming language. This course will help you discover the magical black box that s Machine Learning by teaching a practical approach to modeling using Python along with the Scikit-Learn library.We begin our journey by observing the end result of a Machine Learning deployment before moving back to the fundamentals and into exploratory data analysis. Moving on, we learn to develop complex pipelines and techniques for building custom transformer objects for feature extraction, manipulation, and other effective data cleansing techniques. Finally, we discover how to select a model, apply optimal hyper-parameters, and deploy it.This video course highlights clean coding techniques, object-oriented transformer design and best practices in Machine Learning while using the Scikit-Learn library and also maintaining a focus on practicality and re-usability, ensuring these techniques can be applied to Machine Learning projects of any size.This course uses Python 3.6, and scikit-learn 0.20 while not the latest version available, it provides relevant and informative content for legacy users of Machine Learning and Python. Show and hide more

1. Chapter 1 : Getting Started with Machine Learning
   • The Course Overview 00:05:01
   • Demo Machine Learning Product 00:04:57
   • Setting Up Our Anaconda Environment 00:05:33
   • Launching an iPython Notebook 00:05:23
   • Loading and Manipulating Data with Pandas 00:11:40
   • ML Objective + Data Splitting and Common Pitfalls 00:15:41
   • Descriptive Analytics With Pandas 00:06:09
   • Planning Our Preprocessing Stages 00:09:07
2. Chapter 2 : Exploratory Data Analysis
   • Handling Categorical Data 00:08:01
   • Imputing Missing Values 00:04:32
   • Handling Outliers 00:03:33
   • Feature Extraction 00:04:06
   • Feature Selection 00:04:06
3. Chapter 3 : Building Your First Model
   • Pipelining Transformers 00:03:41
   • Bias/Variance Trade-Off, Overfitting, and Underfitting 00:05:35
   • Cross Validation 00:04:10
   • Scoring Metrics 00:08:12
   • Developing Model Baselines 00:05:12
4. Chapter 4 : Model Tuning and Selection Using Scikit-Learn
   • Hyper-Parameters and Strategic Search Ranges 00:07:50
   • The Importance of Cross Validation in Grid Searches 00:03:06
   • “Model Wars” Using Grid Searches 00:12:12
   • Final Model Selection and Exposure to Holdout Set 00:06:11
5. Chapter 5 : Model Deployment
   • Model Selection – Where Do We Go Now? 00:06:14
   • A Brief Note on Persistence and Version Perils 00:03:27
   • Deploying ML Applications Behind a RESTful Endpoint Using Flask 00:06:11
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