Getting Started with Machine Learning in Python [Video]

Rudy Lai

Saturday, September 29, 2018

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

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Video Details

ISBN 13 9781788477437

Course Length 2 hours 53 minutes

Launching a Python Environment to Create Machine Learning Models

The Course Overview

Machine Learning versus Rule-Based Programming

Understanding What Machine Learning Can Do Using the Tasks Framework

Creating Machine-Learned Models with Python and scikit-learn

Supervised Versus Unsupervised Learning

Prepare Your Datasets for Machine Learning with Data Cleaning

Fix Your Machine Learning Models by Understanding Your Data Source

Dealing with Missing Values – An Example

Standardization and Normalization to Deal with Variables with Different Scales

Eliminating Duplicate Entries

Put Data into Their Right Categories with Classification

How Do We Learn Rules to Classify Objects?

Understanding Logistic Regression – Your First Classifier

Applying Logistic Regression to the Iris Classification Task

Closing Our First Machine Learning Pipeline with a Simple Model Evaluator

Predict Numbers in the Future with Regression

Creating Formulas That Predict the Future – A House Price Example

Understanding Linear Regression – Your First Regressor

Applying Linear Regression to the Boston House Price Task

Evaluating Numerical Predictions with Least Squares

Unsupervised Learning: Segmenting Groups and Detecting Outliers

Exploring Unsupervised Learning and Its Usefulness

Finding Groups Automatically with K-means Clustering

Reducing the Number of Variables in Your Data with PCA

Smooth out Your Histograms with Kernel Density Estimation

Modeling Complex Relationships with Nonlinear Models

Create Explainable Models with Decision Trees

Deal with Nonlinear Relationships with Polynomial Regression

Reduce the Number of Learned Rules with Regularization

Automatic Feature Engineering with Support Vector Machines

Video Description

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.

You will learn to understand and estimate the value of your dataset. We guide you through creating the best performance metric for your task at hand, and how that takes you to the correct model to solve your problem. Understand how to clean data for your application, and how to recognize which Machine Learning task you are dealing with.

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-

Style and Approach

This extensive course is divided into clear bite-size chunks so you can learn at your own pace and focus on the areas of most interest to you. A practical, hands-on course packed with step-by-step instructions, working examples, and helpful advice. You will learn how Machine Learning can be used to create artificial intelligence.

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What You Will Learn

Core concepts of Machine Learning so you can understand fellow data scientists
Clean your data to optimize how it feeds into your Machine-Learning models.
Perform regression in a supervised learning setting, so that you can predict numbers, prices, and conversion rates.
Perform classification in a supervised-learning setting, teaching the model to distinguish between different plants, discussion topics, and objects.
Use decision tree models and random forests, creating models that are explainable but powerful.
Go past linear models with SVMs and polynomial regression, tackling relationships that are non-linear.
Measure and evaluate your Machine-Learning pipeline, so that you can improve your solution over time.

Authors

Rudy Lai

Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help their clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as Big Data, Data Science, Machine Learning, and Cloud Computing. Over the past few years, they have worked with some of the world's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the world's most popular soft drinks companies, helping each of them to better make sense of their data, and process it in more intelligent ways.
The company lives by their motto: Data -> Intelligence -> Action.

Rudy Lai is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, QuantCopy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance - key analytics that all feedback into how our AI generates content.

Prior to founding QuantCopy, Rudy ran HighDimension.IO, a machine learning consultancy, where he experienced first hand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with HighDimension.IO’s Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye.

In the first part of his career, Rudy spent 5+ years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and machine learning. Quantitative trading was also a great platform to learn deeply about reinforcement learning and supervised learning topics in a commercial setting.
Rudy holds a Computer Science degree from Imperial College London, where he was part of the Dean’s List, and received awards such as the Deutsche Bank Artificial Intelligence prize.

Launching a Python Environment to Create Machine Learning Models

The Course Overview

Machine Learning versus Rule-Based Programming

Understanding What Machine Learning Can Do Using the Tasks Framework

Creating Machine-Learned Models with Python and scikit-learn

Supervised Versus Unsupervised Learning

Prepare Your Datasets for Machine Learning with Data Cleaning

Fix Your Machine Learning Models by Understanding Your Data Source

Dealing with Missing Values – An Example

Standardization and Normalization to Deal with Variables with Different Scales

Eliminating Duplicate Entries

Put Data into Their Right Categories with Classification