4.32 out of 5
4.32
1246 reviews on Udemy

The Complete Machine Learning Course with Python

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!
Instructor:
Codestars by Rob Percival
12,121 students enrolled
English [Auto-generated] More
Machine Learning Engineers earn on average $166,000 - become an ideal candidate with this course!
Solve any problem in your business, job or personal life with powerful Machine Learning models
Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more
Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, unsupervised Machine Learning etc

 

The average salary of a Machine Learning Engineer in the US is $166,000! 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.

Come learn Machine Learning with Python this exciting course with Anthony NG, a Senior Lecturer in Singapore who has followed Rob Percival’s “project based” teaching style to bring you this hands-on course.

With over 18 hours of content and more than fifty 5 star rating, it’s already the longest and best rated Machine Learning course on Udemy!

Build Powerful Machine Learning Models to Solve Any Problem

You’ll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.

By the end of the course, you will have trained 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 tool sets 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 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 much more!

No Machine Learning required. Although having some basic Python experience would be helpful, no prior Python knowledge is necessary as all the codes will be provided and the instructor will be going through them line-by-line and you get friendly support in the Q&A area.

Make This Investment in Yourself

If you want to ride the machine learning wave and enjoy the salaries that data scientists make, then this is the course for you!

Take this course and become a machine learning engineer!

Introduction

1
What Does the Course Cover?
2
How to Succeed in This Course
3
Project Files

Getting Started with Anaconda

1
[Windows OS] Downloading & Installing Anaconda

The instructions provided are for installation of Anaconda on a Windows OS.

https://conda.io/docs/user-guide/install/windows.html


For instruction on how to install Anaconda on a Mac OS, follow this link

https://conda.io/docs/user-guide/install/index.html#regular-installation

and

https://conda.io/docs/user-guide/install/macos.html


2
[Windows OS] Managing Environment
3
[Mac OS] Intructions on Installing Anaconda and Managing Environment
4
Practice Activity: Create a New Environment

In this practice activity / quiz, please:

  • Create a new virtual environment
  • Calling it "irisproject"
  • Ensure that Anaconda, Jupyter Notebook, and Spyder has been successfully installed

Do make sure you have successfully completed the above before you proceed to the next portion. We will need it for the next lecture.

5
Navigating the Spyder & Jupyter Notebook Interface
6
Downloading the IRIS Datasets
7
Data Exploration and Analysis
8
Presenting Your Data
9
Getting Started

Regression

1
Introduction
2
Categories of Machine Learning
3
Machine Learning Basic Concepts
4
Working with Scikit-Learn
5
Boston Housing Data - EDA
6
Correlation Analysis and Feature Selection
7
Simple Linear Regression Modelling with Boston Housing Data
8
Robust Regression
9
Evaluate Model Performance
10
Multiple Regression with statsmodel
11
Multiple Regression and Feature Importance
12
Ordinary Least Square Regression and Gradient Descent
13
Regularised Method for Regression
14
Polynomial Regression
15
Dealing with Non-linear relationships
16
Feature Importance Revisited
17
Data Pre-Processing 1
18
Data Pre-Processing 2
19
Variance Bias Trade Off - Validation Curve
20
Variance Bias Trade Off - Learning Curve
21
Cross Validation
22
Section 3

Classification

1
Introduction
2
Logistic Regression 1
3
Logistic Regression 2
4
MNIST Project 1 - Introduction
5
MNIST Project 2 - SGDClassifier
6
MNIST Project 3 - Performance Measures
7
MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score
8
MNIST Project 5 - Precision and Recall Tradeoff
9
MNIST Project 6 - The ROC Curve
10
MNIST Exercise

Support Vector Machine (SVM)

1
Introduction
2
Support Vector Machine (SVM) Concepts
3
Linear SVM Classification
4
Polynomial Kernel
5
Gaussian Radial Basis Function
6
Support Vector Regression
7
Advantages and Disadvantages of SVM

Tree

1
Introduction
2
What is Decision Tree
3
Training a Decision Tree
4
Visualising a Decision Trees
5
Decision Tree Learning Algorithm
6
Decision Tree Regression
7
Overfitting and Grid Search
8
Where to From Here
9
Project HR - Loading and preprocesing data
10
Project HR - Modelling

Ensemble Machine Learning

1
Introduction
2
Ensemble Learning Methods Introduction
3
Bagging Part 1
4
Bagging Part 2
5
Random Forests
6
Extra-Trees
7
AdaBoost
8
Gradient Boosting Machine
9
XGBoost
10
Project HR - Human Resources Analytics
11
Ensemble of ensembles Part 1
12
Ensemble of ensembles Part 2

k-Nearest Neighbours (kNN)

1
kNN Introduction
2
kNN Concepts
3
kNN and Iris Dataset Demo
4
Distance Metric
5
Project Cancer Detection Part 1
6
Project Cancer Detection Part 2

Unsupervised Learning: Dimensionality Reduction

1
Introduction
2
Dimensionality Reduction Concept
3
PCA Introduction
4
Dimensionality Reduction Demo
5
Project Wine 1: Dimensionality Reduction with PCA
6
Project Abalone
7
Project Wine 2: Choosing the Number of Components
8
Kernel PCA
9
Kernel PCA Demo
10
LDA & Comparison between LDA and PCA

Unsupervised Learning: Clustering

1
Introduction
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Includes

18 hours on-demand video
6 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion