[free download] Machine Learning A-Z™: Hands-On Python & R In Data Science

Learn how to create machine learning algorithms in Python and R from two data science experts. Code templates included.

What will you learn

• Master machine learning on Python & R.
• You have a great intuition for many machine learning models
• Make accurate predictions
• Do a powerful analysis
• Build powerful machine learning models
• Create strong added value for your business
• Use machine learning for personal purposes
• Dealing with specific topics such as learning reinforcement, NLP and deep learning
• Deal with advanced technologies like dimensional reduction
• Find out which machine learning model to choose for each type of problem
• Build an army of powerful machine learning models and learn how to combine them to solve any problem

Requirements

• Just some level of math in high school.

Description

Are you interested in machine learning? Then this course is for you!

This course has been designed by two professional data scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We'll guide you step-by-step into the world of machine learning. With each educational program, you will develop new skills and improve your understanding of this challenging and profitable sub-field of data science.

This course is fun and exciting, but at the same time, we go deeper into machine learning. It is organized in the following way:

Part 1 - Data pre-processing

Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Forest Stochastic Regression

Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Part 4 - The Clustering: K-Means, Hierarchical Clustering

Part 5 - Learn the rules of bonding: Apriori, Eclat

Part 6 - Augmented Learning: Supreme Confidence, Thomson Sampling

Part 7 - Natural Language Processing: The Word Bag Model and NLP Algorithms

Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

Part 9 - Dimensional Reduction: PCA, LDA, and Kernel PCA

Part 10 - Model selection and reinforcement: k-fold cross validation, parameter setting, network search, XGBoost

Moreover, the course is packed with practical exercises that are based on real-life examples. So you will not only learn the theory but you will also get some hands-on training in building your own models.

And as a bonus, this course includes both Python and R code templates that you can download and use on your own projects.

Important updates (June 2020):

All icons until today

Deep learning encoded in TENSORFLOW 2.0

Top gradient promotion models including XGBOOST and even CATBOOST!

Who is this course for:

• Anyone interested in machine learning.
• Students with at least high school math knowledge who would like to start learning machine learning.
• Any intermediate level folks who know the basics of machine learning, including classic algorithms such as linear regression or logistic regression, but would like to learn more about it and explore all the different areas of machine learning.
• Anyone who is not comfortable with coding but who is interested in machine learning and wants to apply it easily to datasets.
• Any college student wishing to start a career in data science.
• Any data analyst who wants to move to the next level in machine learning.
• Anyone who is dissatisfied with their job and wants to become a data scientist.
• Anyone who wants to create added value for their business using powerful machine learning tools.

Size: 6.32 GB 

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