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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
24,263 students enrolled
English [Auto] 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 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!

Introduction

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

Getting Started with Anaconda

1
Installing Applications and Creating Environment
2
Hello World
3
Iris Project 1: Working with Error Messages
4
Iris Project 2: Reading CSV Data into Memory
5
Iris Project 3: Loading data from Seaborn
6
Iris Project 4: Visualization

Regression

1
Scikit-Learn
2
EDA
3
Correlation Analysis and Feature Selection
4
Correlation Analysis and Feature Selection
5
Linear Regression with Scikit-Learn
6
Five Steps Machine Learning Process
7
Robust Regression
8
Evaluate Regression Model Performance
9
Multiple Regression 1
10
Multiple Regression 2
11
Regularized Regression
12
Polynomial Regression
13
Dealing with Non-linear Relationships
14
Feature Importance
15
Data Preprocessing
16
Variance-Bias Trade Off
17
Learning Curve
18
Cross Validation
19
CV Illustration

Classification

1
Logistic Regression
2
Introduction to Classification
3
Understanding MNIST
4
SGD
5
Performance Measure and Stratified k-Fold
6
Confusion Matrix
7
Precision
8
Recall
9
f1
10
Precision Recall Tradeoff
11
Altering the Precision Recall Tradeoff
12
ROC

Support Vector Machine (SVM)

1
Support Vector Machine (SVM) Concepts
2
Linear SVM Classification
3
Polynomial Kernel
4
Radial Basis Function
5
Support Vector Regression

Tree

1
Introduction to Decision Tree
2
Training and Visualizing a Decision Tree
3
Visualizing Boundary
4
Tree Regression, Regularization and Over Fitting
5
End to End Modeling
6
Project HR
7
Project HR with Google Colab

Ensemble Machine Learning

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

k-Nearest Neighbours (kNN)

1
kNN Introduction
2
Project Cancer Detection
3
Addition Materials
4
Project Cancer Detection Part 1

Unsupervised Learning: Dimensionality Reduction

1
Dimensionality Reduction Concept
2
PCA Introduction
3
Project Wine
4
Kernel PCA
5
Kernel PCA Demo
6
LDA vs PCA
7
Project Abalone

Unsupervised Learning: Clustering

1
Clustering
2
k_Means Clustering

Deep Learning

1
Estimating Simple Function with Neural Networks
2
Neural Network Architecture
3
Motivational Example - Project MNIST
4
Binary Classification Problem
5
Natural Language Processing - Binary Classification

Appendix A1: Foundations of Deep Learning

1
Introduction to Neural Networks
2
Differences between Classical Programming and Machine Learning
3
Learning Representations
4
What is Deep Learning
5
Learning Neural Networks
6
Why Now?
7
Building Block Introduction
8
Tensors
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Certificate of Completion
The Complete Machine Learning Course with Python
Category:
Data Science
Teacher
Daniel W
Price:
$218.98 $169
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