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 t
rain/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!
Requrirements
Requirements
Basic Python programming knowledge is necessary
Good understanding of linear algebra