Python Build Machine Learning Models In 6 Hours

Last updated 11/2018MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 2.26 GB | Duration: 5h 40m

A complete & comprehensive course in which you will create machine learning models with ease!

What you'll learn

Learn the core concepts of machine learning in Python

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

Measure and evaluate your Machine-Learning pipeline, so that you can improve your solution over

Read, explore, clean, and prepare your data using Pandas, the most popular library for analyzing data tables

Use the Scikit-Learn library to deploy ready-built models, train them, and see results in just a few lines of code

Use hyper-parameter optimization to get the best possible version of each model for your specific application

Requirements

Some knowledge of mathematics and Python is assumed.

Description

Given the constantly increasing amounts of data they're faced with, programmers and data scientists have to come up with better solutions to make machines smarter and reduce manual work along with finding solutions to the obstacles faced in between. Python comes to the rescue to craft better solutions and process them effectively.This comprehensive 2-in-1 course teaches you how to perform different machine learning tasks along with fixing common machine learning problems you face in your day-to-day tasks. You will learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. You will also use unlabelled datasets to do sntation and clustering, so that you can separate a large dataset into sensible groups. Further to get a complete hold on the technology, you will work with tools using which you can build predictive models in Python.This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.In the first course, Getting Started with Machine Learning in Python, you will learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. You will then use unlabelled datasets to do sntation and clustering, so that you can separate a large dataset into sensible groups. You will also learn to understand and estimate the value of your dataset. Next, you will learn how to clean data for your application, and how to recognize which machine learning task you are dealing with.The second course, Building Predictive Models with Machine Learning and Python, 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.By the end of this course, you will be able to take the Python machine learning toolkit and apply it to your own projects to build and deploy machine learning models in just a few lines of code. Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly chag and complex world of emeg 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 make better sense of its data, and process it in more intelligent ways. The company lives by its 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 feed-back into how our AI generates content. Prior to founding QuantCopy, Rudy ran HighDimension.IO, a Machine Learning consultancy, where he experienced firsthand 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 from which to learn about reinforcement learning in depth, 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.

Overview

Section 1: Getting Started with Machine Learning in Python

Lecture 1 The Course Overview

Lecture 2 Machine Learning versus Rule-Based Programming

Lecture 3 Understanding What Machine Learning Can Do Using the Tasks Framework

Lecture 4 Creating Machine-Learned Models with Python and scikit-learn

Lecture 5 Supervised Versus Unsupervised Learning

Lecture 6 Fix your machine learning models by understanding your data source

Lecture 7 Dealing with Missing Values – An Example

Lecture 8 Standardization and Normalization to Deal with Variables with Different Scales

Lecture 9 Eliminating Duplicate Entries

Lecture 10 How Do We Learn Rules to Classify Objects?

Lecture 11 Understanding Logistic Regression – Your First Classifier

Lecture 12 Applying Logistic Regression to the Iris Classification Task

Lecture 13 Closing Our First Machine Learning Pipeline with a Simple Model Evaluator

Lecture 14 Creating Formulas That Predict the Future – A House Price Example

Lecture 15 Understanding Linear Regression – Your First Regressor

Lecture 16 Applying Linear Regression to the Boston House Price Task

Lecture 17 Evaluating Numerical Predictions with Least Squares

Lecture 18 Exploring Unsupervised Learning and Its Usefulness

Lecture 19 Finding Groups Automatically with K-means Clustering

Lecture 20 Reducing the Number of Variables in Your Data with PCA

Lecture 21 Smooth out Your Histograms with Kernel Density Estimation

Lecture 22 Create Explainable Models with Decision Trees

Lecture 23 Automatic Feature Eeering with Support Vector Machines

Lecture 24 Deal with Nonlinear Relationships with Polynomial Regression

Lecture 25 Reduce the Number of Learned Rules with Regularization

Section 2: Building Predictive Models with Machine Learning and Python

Lecture 26 The Course Overview

Lecture 27 Introduction to Machine Learning

Lecture 28 Meet the Python Machine Learning Stack

Lecture 29 Making Sure It Works in Your Computer

Lecture 30 Exploring Your First Dataset

Lecture 31 Building Your First Model

Lecture 32 Assessing Your Model

Lecture 33 Finding Issues with Your Data

Lecture 34 Using Pandas to Get Your Data Ready for Modeling

Lecture 35 Building a Model to Assess Your Chances of Surviving the Titanic

Lecture 36 What Makes Models Truly Different?

Lecture 37 Understanding the Advantages and Shortcomings of the Most Popular Models

Lecture 38 Trying (and Failing) to Use an SVM, a Random Forest and a Linear Model

Lecture 39 Fixing Our Issues with Our SVM Model

Lecture 40 Fixing Our Issues with the Random Forest Model

Lecture 41 What Does it Mean to Tune a Model (Theory)?

Lecture 42 Grid Search – Just Try Everything!

Lecture 43 Tune a Linear Model to Predict House Prices

Lecture 44 Tune an SVM to Predict a Politician’s Party Based on Their Voting Record

Lecture 45 Advanced Libraries for Machine Learning

Lecture 46 Good Next Steps – Kaggle, Hackathons, YouTube Channels, and More

This course is aimed at novice data scientists and developers who want to get started with machine learning in Python. Developers who are curious about building and deploying machine learning-based models will find that this course will guide them to understand why some models are better than others at tackling certain challenges.

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