Machine Learning & Data Science Masterclass in Python and R

Machine learning with many practical examples. Regression, Classification and much more

What you'll learn
Create machine learning applications in Python as well as R
Apply Machine Learning to own data
You will learn Machine Learning clearly and concisely
Learn with real data: Many practical examples (spam filter, is fungus edible or poisonous etc. …)
No dry mathematics - everything explained vividly
Use popular tools like Sklearn, and Caret
You will know when to use which machine learning model

Requirements
You should have programmed a little before.
No knowledge of Python or R is required.
All necessary tools (R, RStudio, Anaconda, …) will be installed together in the course.

Description
This course contains over 200 lessons, quizzes, practical examples, … - the easiest way if you want to learn Machine Learning. Step by step I teach you machine learning. In each section you will learn a new topic - first the idea / intuition behind it, and then the code in both Python and R.Machine Learning is only really fun when you evaluate real data. That's why you analyze a lot of practical examples in this course:Estimate the value of used carsWrite a spam filterDiagnose breast cancerAll code examples are shown in both programming languages - so you can choose whether you want to see the course in Python, R, or in both languages!After the course you can apply Machine Learning to your own data and make informed decisions:You know when which models might come into question and how to compare them. You can analyze which columns are needed, whether additional data is needed, and know which data needs to be prepared in advance. This course covers the important topics:RegressionClassificationOn all these topics you will learn about different algorithms. The ideas behind them are simply explained - not dry mathematical formulas, but vivid graphical explanations.We use common tools (Sklearn, NLTK, caret, data.table, …), which are also used for real machine learning projects. What do you learn?Regression:Linear RegressionPolynomial RegressionClassification:Logistic RegressionNaive BayesDecision treesRandom ForestYou will also learn how to use Machine Learning:Read in data and prepare it for your modelWith complete practical example, explained step by stepFind the best hyper parameters for your model"Parameter Tuning"Compare models with each other:How the accuracy value of a model can mislead you and what you can do about itK-Fold Cross ValidationCoefficient of determinationMy goal with this course is to offer you the ideal entry into the world of machine learning.

Who this course is for:
Developers interested in Machine Learning