The Complete Machine Learning Course with Python [Video]

More Information
Learn
  • Learn to Build Powerful Machine Learning Models to Solve Any Problem
  • Learn to Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more
About

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.

Style and Approach

You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.

Features
  • 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.
Course Length 18 hours 22 minutes
ISBN 9781789953725
Date Of Publication 29 Oct 2018

Authors

Rob Percival

Codestars by Rob Percival

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Rob Percival wants to revolutionize the way people learn to code by making it simple, logical, fun and, above all, accessible. But as just one man, Rob couldn't create all the courses his students, more than half a million of them, wanted. That's why Rob created Codestars. Together, the instructors that make up the Codestars team create courses on all the topics that students want to learn in the way that students want to learn them: courses that are well-structured, super interactive, and easy to understand. Codestars wants to make it as easy as possible for learners of all ages and levels to build functional websites and apps.

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Dee Aliyu Odumosu

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Dee has developed over 120 apps for clients, including both individuals and start-ups, from around the world.Dee’s passion for computer programming began in 2006 with his first contact with Java programming language. He continued to learn different languages, including Microsoft ASP.NET, Ruby on Rails, C#, some PHP and HTML and CSS. Dee briefly pursued a MCSE (Microsoft Certified System Engineer) certification before he decided he wanted to become a full-time programmer. He achieved a Microsoft Certified Professional status. For the last 4 years, Dee has been programming the iPhone using Objective-C and Swift language. In 2013, Dee decided to further his education in Software Development by earning a distinction in his Masters of Software Engineering degree from Queen Mary University

Anthony Ng

Anthony Ng has spent almost 10 years in the education sector covering topics such as algorithmic trading, financial data analytics, investment, and portfolio management and more. He has worked in various financial institutions and has assisted Quantopian to conduct Algorithmic Trading Workshops in Singapore since 2016. He has also presented in QuantCon Singapore 2016 and 2017. He is passionate about finance, data science and Python and enjoys researching, teaching and sharing knowledge. He holds a Master of Science in Financial Engineering from NUS Singapore and MBA and Bcom from Otago University.