Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!

Can$174.99

By Anthony Ng

Time 18hrs 22mins

Published in Oct 2018

Product Type Video

Edition 1st Edition

ISBN 9781789953725

What do you get with a video?

Download this video in MP4 format

DRM FREE - Read whenever, wherever and however you want

Key Benefits

Solve any problem in your business or job with powerful Machine Learning models

Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.

Who Is This Book For?

A newbie who wants to learn machine learning algorithm with Python. Anyone 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.

Book Description

Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you. You will train 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 toolsets 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 the 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 more! 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.

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