The Complete Machine Learning Course with Python
4.2 (1,160 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.
11,605 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!
4.2 (1,160 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.
11,605 students enrolled
Last updated 7/2018
English
English [Auto-generated], Portuguese [Auto-generated], 1 more
  • Spanish [Auto-generated]
Current price: $11.99 Original price: $199.99 Discount: 94% off
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This course includes
  • 18.5 hours on-demand video
  • 6 articles
  • 74 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
Requirements
  • Basic Python programming knowledge is necessary
  • Good understanding of linear algebra
Description

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!

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
Course content
Expand all 95 lectures 18:24:23
+ Introduction
3 lectures 03:59
How to Succeed in This Course
01:07
Project Files
00:30
+ Getting Started with Anaconda
8 lectures 01:16:09

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


Preview 20:16
[Mac OS] Intructions on Installing Anaconda and Managing Environment
00:03

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.

Practice Activity: Create a New Environment
00:11
Navigating the Spyder & Jupyter Notebook Interface
17:12
Downloading the IRIS Datasets
02:58
Data Exploration and Analysis
14:19
Presenting Your Data
15:04
Getting Started
10 questions
+ Regression
20 lectures 04:58:23
Categories of Machine Learning
12:24
Machine Learning Basic Concepts
7 questions
Working with Scikit-Learn
19:25
Boston Housing Data - EDA
20:11
Correlation Analysis and Feature Selection
08:47
Simple Linear Regression Modelling with Boston Housing Data
13:26
Robust Regression
14:22
Evaluate Model Performance
19:42
Multiple Regression with statsmodel
19:32
Multiple Regression and Feature Importance
14:11
Ordinary Least Square Regression and Gradient Descent
18:32
Regularised Method for Regression
19:09
Polynomial Regression
14:30
Dealing with Non-linear relationships
10:30
Feature Importance Revisited
07:40
Data Pre-Processing 1
13:06
Data Pre-Processing 2
19:10
Variance Bias Trade Off - Validation Curve
16:44
Variance Bias Trade Off - Learning Curve
15:04
Cross Validation
15:43
Section 3
9 questions
+ Classification
10 lectures 01:53:39
Introduction
04:14
Logistic Regression 1
12:00
Logistic Regression 2
16:33
MNIST Project 1 - Introduction
13:09
MNIST Project 2 - SGDClassifier
10:26
MNIST Project 3 - Performance Measures
12:08
MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score
18:43
MNIST Project 5 - Precision and Recall Tradeoff
16:43
MNIST Project 6 - The ROC Curve
09:26
MNIST Exercise
00:17
+ Support Vector Machine (SVM)
7 lectures 01:12:28
Introduction
02:17
Support Vector Machine (SVM) Concepts
19:58
Linear SVM Classification
11:09
Polynomial Kernel
15:24
Gaussian Radial Basis Function
12:30
Support Vector Regression
06:27
Advantages and Disadvantages of SVM
04:43
+ Tree
10 lectures 02:01:35
Introduction
02:51
What is Decision Tree
16:30
Training a Decision Tree
08:22
Visualising a Decision Trees
20:31
Decision Tree Learning Algorithm
13:42
Decision Tree Regression
11:22
Overfitting and Grid Search
17:39
Where to From Here
05:33
Project HR - Loading and preprocesing data
18:07
Project HR - Modelling
06:58
+ Ensemble Machine Learning
12 lectures 02:56:56
Introduction
02:37
Ensemble Learning Methods Introduction
13:41
Bagging Part 1
22:21
Bagging Part 2
12:13
Random Forests
13:51
Extra-Trees
07:05
AdaBoost
13:01
Gradient Boosting Machine
16:13
XGBoost
19:35
Project HR - Human Resources Analytics
23:16
Ensemble of ensembles Part 1
20:02
Ensemble of ensembles Part 2
13:01
+ k-Nearest Neighbours (kNN)
6 lectures 58:40
kNN Introduction
02:03
kNN Concepts
07:25
kNN and Iris Dataset Demo
08:35
Distance Metric
05:31
Project Cancer Detection Part 1
20:12
Project Cancer Detection Part 2
14:54
+ Unsupervised Learning: Dimensionality Reduction
10 lectures 01:33:58
Introduction
01:41
Dimensionality Reduction Concept
12:39
PCA Introduction
17:04
Dimensionality Reduction Demo
06:08
Project Wine 1: Dimensionality Reduction with PCA
18:19
Project Abalone
00:06
Project Wine 2: Choosing the Number of Components
07:14
Kernel PCA
16:17
Kernel PCA Demo
07:09
LDA & Comparison between LDA and PCA
07:21
+ Unsupervised Learning: Clustering
9 lectures 01:28:35
Introduction
01:58
Clustering Concepts
08:01
MLextend
06:15
Ward’s Agglomerative Hierarchical Clustering
16:14
Truncating Dendrogram
17:35
k-Means Clustering
13:00
Elbow Method
06:57
Silhouette Analysis
07:41
Mean Shift
10:54