by Arish Ali | Released June 2018 | ISBN: 9781788999137

Build powerful Machine Learning models using Python with hands-on practical examples in just a weekAbout This VideoA good understanding of Machine learning to start creating practical solutions.Get an intuitive understanding of many machine learning algorithmsBuild many different Machine Learning models and learn to combine them to solve problemsIn DetailMachine learning is one of the most sought-after skills in the market. But have you ever wondered where to start or found the course not so easy to follow. With this hands-on and practical machine learning course, you can learn and start applying machine learning in less than a week without having to be an expert mathematician.In this course, you will be introduced to a new machine learning aspect in each section followed by a practical assignment as a homework to help you in efficiently implement the learnings in a practical manner. With the systematic and fast-paced approach to this course, learn machine learning using Python in the most practical and structured way to develop machine learning projects in Python in a week.This course is structured to unlock the potential of Python machine learning in the shortest amount of time. If you are looking to upgrade your machine learning skills using Python in the quickest possible time, then this course is for you!You can find the code files at https://github.com/PacktPublishing/Python-Machine-Learning-in-7-Days Show and hide more Publisher Resources Download Example Code

1. Chapter 1 : Enter the Machine Learning World!
   • The Course Overview 00:01:51
   • Setting Up Your Machine Learning Environment 00:04:09
   • Exploring Types of Machine Learning 00:03:50
   • Using Scikit-learn for Machine Learning 00:06:25
   • Assignment – Train Your First Pre-built Machine Learning Model 00:01:26
2. Chapter 2 : Build Your First Predicting Model
   • Supervised Learning Algorithm 00:02:24
   • Architecture of a Machine Learning System 00:03:40
   • Machine Learning Model and Its Components 00:04:14
   • Linear Regression 00:02:04
   • Predicting Weight Using Linear Regression 00:06:47
   • Assignment – Predicting Energy Output of a Power Plant 00:01:58
3. Chapter 3 : Image Classification Using Supervised Learning
   • Review of Predicting Energy Output of a Power Plant 00:07:11
   • Logistic Regression 00:05:51
   • Classifying Images Using Logistic Regression 00:05:16
   • Support Vector Machines 00:01:56
   • Kernels in a SVM 00:01:20
   • Classifying Images Using Support Vector Machines 00:03:01
   • Assignment – Start Image Classifying Using Support Vector Machines 00:03:25
4. Chapter 4 : Improving Model Accuracy
   • Review of Classifying Images Using Support Vector Machines 00:05:17
   • Model Evaluation 00:03:06
   • Better Measures than Accuracy 00:05:13
   • Understanding the Results 00:02:44
   • Improving the Models 00:02:57
   • Assignment – Getting Better Test Sample Results by Measuring Model Performance 00:01:51
5. Chapter 5 : Finding Patterns and Structures in Unlabeled Data
   • Review of Getting Better Test Sample Results by Measuring Model Performance 00:06:41
   • Unsupervised Learning 00:02:03
   • Clustering 00:02:49
   • K-means Clustering 00:02:27
   • Determining the Number of Clusters 00:01:31
   • Assignment – Write Your Own Clustering Implementation for Customer Segmentation 00:02:07
6. Chapter 6 : Sentiment Analysis Using Neural Networks
   • Review of Clustering Customers Together 00:05:25
   • Why Neural Network 00:03:15
   • Parts of a Neural Network 00:03:31
   • Working of a Neural Network 00:02:33
   • Improving the Network 00:01:49
   • Assignment – Build a Sentiment Analyzer Based on Social Network Using ANN 00:03:21
7. Chapter 7 : Mastering Kaggle Titanic Competition Using Random Forest
   • Review of Building a Sentiment Analyser ANN 00:05:24
   • Decision Trees 00:03:04
   • Working of a Decision Tree 00:03:08
   • Techniques to Further Improve a Model 00:01:48
   • Random Forest as an Improved Machine Learning Approach 00:01:44
   • Weekend Task – Solving Titanic Problem Using Random Forest 00:02:06
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