The Complete Machine Learning Course with Python [Video]

Rob Percival, Anthony NG

October 2018

18 hours 22 minutes

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!

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Learn	Learn to Build Powerful Machine Learning Models to Solve Any Problem Learn to Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more
About	Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you. You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more! Inside the course, you'll learn how to: Set up a Python development environment correctly Gain complete machine learning toolsets to tackle most real-world problems Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them. Combine multiple models with by bagging, boosting or stacking Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data Develop in Jupyter (IPython) notebook, Spyder and various IDE Communicate visually and effectively with Matplotlib and Seaborn Engineer new features to improve algorithm predictions Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data Use SVM for handwriting recognition, and classification problems in general Use decision trees to predict staff attrition Apply the association rule to retail shopping datasets And much more! By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms. Style and Approach You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.
Features	Solve any problem in your business or job with powerful Machine Learning models Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.
Course Length	18 hours 22 minutes
ISBN	9781789953725
Date Of Publication	29 Oct 2018

Table of contents

1 Introduction

2 Getting Started with Anaconda

[Windows OS] Managing Environment
Navigating the Spyder & Jupyter Notebook Interface
Downloading the IRIS Datasets
Data Exploration and Analysis
Presenting Your Data

3 Regression

Categories of Machine Learning
Working with Scikit-Learn
Boston Housing Data - EDA
Correlation Analysis and Feature Selection
Simple Linear Regression Modelling with Boston Housing Data
Robust Regression
Evaluate Model Performance
Multiple Regression with statsmodel
Multiple Regression and Feature Importance
Ordinary Least Square Regression and Gradient Descent
Regularised Method for Regression
Polynomial Regression
Dealing with Non-linear relationships
Feature Importance Revisited
Data Pre-Processing 1
Data Pre-Processing 2
Variance Bias Trade Off - Validation Curve
Variance Bias Trade Off - Learning Curve
Cross Validation

4 Classification

Logistic Regression 1
Logistic Regression 2
MNIST Project 1 - Introduction
MNIST Project 2 - SGDClassifiers
MNIST Project 3 - Performance Measures
MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score
MNIST Project 5 - Precision and Recall Tradeoff
MNIST Project 6 - The ROC Curve

5 Support Vector Machine (SVM)

Support Vector Machine (SVM) Concepts
Linear SVM Classification
Polynomial Kernel
Gaussian Radial Basis Function
Support Vector Regression
Advantages and Disadvantages of SVM

6 Tree

What is Decision Tree
Training a Decision Tree
Visualising a Decision Trees
Decision Tree Learning Algorithm
Decision Tree Regression
Overfitting and Grid Search
Where to From Here
Project HR - Loading and preprocesing data
Project HR - Modelling

7 Ensemble Machine Learning

Ensemble Learning Methods Introduction
Bagging Part 1
Bagging Part 2
Random Forests
Extra-Trees
AdaBoost
Gradient Boosting Machine
XGBoost
Project HR - Human Resources Analytics
Ensemble of ensembles Part 1
Ensemble of ensembles Part 2

8 k-Nearest Neighbours (kNN)

kNN Concepts
kNN and Iris Dataset Demo
Distance Metric
Project Cancer Detection Part 1
Project Cancer Detection Part 2

9 Dimensionality Reduction

Dimensionality Reduction Concept
PCA Introduction
Dimensionality Reduction Demo
Project Wine 1: Dimensionality Reduction with PCA
Project Wine 2: Choosing the Number of Components
Kernel PCA
Kernel PCA Demo
LDA & Comparison between LDA and PCA

10 Unsupervised Learning: Clustering

Clustering Concepts
MLextend
Ward’s Agglomerative Hierarchical Clustering
Truncating Dendrogram
k-Means Clustering
Elbow Method
Silhouette Analysis
Mean Shift

Authors

Rob Percival

Codestars by Rob Percival

Rob Percival wants to revolutionize the way people learn to code by making it simple, logical, fun and, above all, accessible. But as just one man, Rob couldn't create all the courses his students, more than half a million of them, wanted. That's why Rob created Codestars. Together, the instructors that make up the Codestars team create courses on all the topics that students want to learn in the way that students want to learn them: courses that are well-structured, super interactive, and easy to understand. Codestars wants to make it as easy as possible for learners of all ages and levels to build functional websites and apps.

Rob Percival – Web Developer And Teacher

Rob has a degree in Mathematics from Cambridge University and you might call him a bit of a coding geek. After building websites for friends and family for fun, he soon learned that web development was a very lucrative career choice. He gave up his successful (and sometimes stressful) job as a teacher to work part-time and today he is passionate about teaching kids to code; so every summer he runs Code School in the beautiful city of Cambridge. He also runs the popular web hosting and design service, Eco Web Hosting, which leaves him free to share his secrets with people like you.

Anthony NG

Anthony NG – Algorithmic Trading Workshop Researcher and Conductor

Anthony Ng has spent the last seven years as a Senior Lecturer teaching algorithmic trading, financial data analysis, banking, finance, investment and portfolio management. He assists Quantopian, a Boston-based Hedge Fund, to conduct Algorithmic Trading Workshops in Singapore and has presented in the recent QuantCon Singapore 2016. You can find his Algorithmic Trading tutorials on his YouTube channel. Just click the YouTube icon to visit his channel. Passionate with finance, data science and python, Anthony enjoyed researching, teaching and sharing on these topics. Anthony studied Masters of Science in Financial Engineering at NUS Singapore.

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