Machine Learning & Data Science Masterclass in Python and R
Machine learning with many practical examples. Regression, Classification and much more
Bullstrack
Machine Learning & Data Science Masterclass in Python and R udemy course
Machine learning with many practical examples. Regression, Classification and much more
What you'll learn:
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Learn how to solve real life problem using the Machine learning techniques
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Machine Learning models such as Linear Regression, Logistic Regression, KNN etc.
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Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc.
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Understanding of basics of statistics and concepts of Machine Learning
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How to do basic statistical operations and run ML models in Python
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Indepth knowledge of data collection and data preprocessing for Machine Learning problem
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How to convert business problem into a Machine learning problem
Requirements:
- Students will need to install Anaconda software but we have
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 cars
Write a spam filter
Diagnose breast cancer
All 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:
Regression
Classification
On 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 Regression
Polynomial Regression
Classification:
Logistic Regression
Naive Bayes
Decision trees
Random Forest
You will also learn how to use Machine Learning:
Read in data and prepare it for your model
With complete practical example, explained step by step
Find 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 it
K-Fold Cross Validation
Coefficient of determination
My goal with this course is to offer you the ideal entry into the world of machine learning.
Who this course is for:
Course Details:
- 17 hours on-demand video
- 13 articles
- 1 downloadable resource
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
Machine Learning & Data Science Masterclass in Python and R udemy free download
Machine learning with many practical examples. Regression, Classification and much more
Demo Link: https://www.udemy.com/course/machine-learning-data-science-masterclass/
