The Complete Machine Learning Course with Python
3.2 (2,285 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
18,468 students enrolled

The Complete Machine Learning Course with Python

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!
3.2 (2,285 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
18,468 students enrolled
Last updated 1/2020
English
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  • Spanish [Auto]
Current price: $119.99 Original price: $199.99 Discount: 40% off
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This course includes
  • 17.5 hours on-demand video
  • 3 articles
  • 2 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • 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
Course content
Expand all 111 lectures 17:18:45
+ Introduction
3 lectures 04:42
How to Succeed in This Course
01:09
Project Files and Resources
01:00
+ Getting Started with Anaconda
6 lectures 56:34
Iris Project 1: Working with Error Messages
12:32
Iris Project 2: Reading CSV Data into Memory
08:45
Iris Project 3: Loading data from Seaborn
08:43
Iris Project 4: Visualization
10:20
+ Regression
19 lectures 04:06:25
Scikit-Learn
09:11
EDA
19:11
Correlation Analysis and Feature Selection
08:47
Correlation Analysis and Feature Selection
13:03
Linear Regression with Scikit-Learn
13:44
Five Steps Machine Learning Process
08:53
Robust Regression
18:00
Evaluate Regression Model Performance
15:39
Multiple Regression 1
19:44
Multiple Regression 2
12:27
Regularized Regression
06:53
Polynomial Regression
18:03
Dealing with Non-linear Relationships
09:31
Feature Importance
05:13
Data Preprocessing
21:59
Variance-Bias Trade Off
11:43
Learning Curve
08:38
Cross Validation
08:02
CV Illustration
17:44
+ Classification
12 lectures 01:44:18
Logistic Regression
20:52
Introduction to Classification
05:04
Understanding MNIST
14:56
SGD
09:29
Performance Measure and Stratified k-Fold
07:26
Confusion Matrix
09:22
Precision
03:38
Recall
03:18
f1
02:04
Precision Recall Tradeoff
18:02
Altering the Precision Recall Tradeoff
03:07
ROC
07:00
+ Support Vector Machine (SVM)
5 lectures 39:18
Support Vector Machine (SVM) Concepts
06:57
Linear SVM Classification
10:57
Polynomial Kernel
05:03
Radial Basis Function
08:17
Support Vector Regression
08:04
+ Tree
7 lectures 01:05:16
Introduction to Decision Tree
06:27
Training and Visualizing a Decision Tree
06:38
Visualizing Boundary
08:06
Tree Regression, Regularization and Over Fitting
05:08
End to End Modeling
04:49
Project HR
24:01
Project HR with Google Colab
10:07
+ Ensemble Machine Learning
10 lectures 01:12:28
Ensemble Learning Methods Introduction
04:57
Bagging
20:58
Random Forests and Extra-Trees
10:13
AdaBoost
06:31
Gradient Boosting Machine
03:00
XGBoost Installation
02:45
XGBoost
04:40
Project HR - Human Resources Analytics
08:09
Ensemble of Ensembles Part 1
06:22
Ensemble of ensembles Part 2
04:53
+ Unsupervised Learning: Dimensionality Reduction
7 lectures 36:55
Dimensionality Reduction Concept
04:38
PCA Introduction
07:17
Project Wine
06:26
Kernel PCA
05:34
Kernel PCA Demo
03:30
LDA vs PCA
05:36
Project Abalone
03:54
+ Unsupervised Learning: Clustering
2 lectures 24:58
Clustering
15:55
k_Means Clustering
09:03
Requirements
  • Basic Python programming knowledge is necessary
  • Good understanding of linear algebra
Description

The Complete Machine Learning Course in Python has been FULLY UPDATED for November 2019!

With brand new sections as well as updated and improved content, you get everything you need to master Machine Learning in one course! The machine learning field is constantly evolving, and we want to make sure students have the most up-to-date information and practices available to them:

Brand new sections include:

  • Foundations of Deep Learning covering topics such as the difference between classical programming and machine learning, differentiate between machine and deep learning, the building blocks of neural networks, descriptions of tensor and tensor operations, categories of machine learning and advanced concepts such as over- and underfitting, regularization, dropout, validation and testing and much more.

  • Computer Vision in the form of Convolutional Neural Networks covering building the layers, understanding filters / kernels, to advanced topics such as transfer learning, and feature extractions.

And the following sections have all been improved and added to:

  • All the codes have been updated to work with Python 3.6 and 3.7

  • The codes have been refactored to work with Google Colab

  • Deep Learning and NLP

  • Binary and multi-class classifications with deep learning

Get the most up to date machine learning information possible, and get it in a single course! 


                                                                *         *         *


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 ratings, 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:

  • 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!

Who this course is for:
  • Anyone willing and interested to learn machine learning algorithm with Python
  • Any one who has a deep interest in the practical application of machine learning to real world problems
  • Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms
  • Any intermediate to advanced EXCEL users who is unable to work with large datasets
  • Anyone interested to present their findings in a professional and convincing manner
  • Anyone who wishes to start or transit into a career as a data scientist
  • Anyone who wants to apply machine learning to their domain