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

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

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What you will learn

  • Master Machine Learning on Python & R
  • Have a great intuition of many Machine Learning models
  • Make accurate predictions
  • Make a powerful analysis
  • Make robust Machine Learning models
  • Create strong added value to your business
  • Use Machine Learning for personal purpose
  • Handle specific topics like Reinforcement Learning, NLP and Deep Learning
  • Handle advanced techniques like Dimensionality Reduction
  • Know which Machine Learning model to choose for each type of problem
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem

 

Curriculum

Section 1: Welcome to the course!

Section 2: -------------------- Part 1: Data Preprocessing --------------------

Section 3: Simple Linear Regression

Section 4: Multiple Linear Regression

Section 5: Polynomial Regression

Section 6: Support Vector Regression (SVR)

Section 7: Decision Tree Regression

Section 8: Random Forest Regression

Section 9: Evaluating Regression Models Performance

Section 10: Logistic Regression

Section 11: K-Nearest Neighbors (K-NN)

Section 12: Support Vector Machine (SVM)

Section 13: Kernel SVM

Section 14: Naive Bayes

Section 15: Decision Tree Classification

Section 16: Random Forest Classification

Section 17: Evaluating Classification Models Performance

Section 18: K-Means Clustering

Section 19: Hierarchical Clustering

Section 20: Apriori

Section 21: Eclat

Section 22: Upper Confidence Bound (UCB)

Section 23: Thompson Sampling

Section 24: -------------------- Part 7: Natural Language Processing --------------------

Section 25: -------------------- Part 8: Deep Learning --------------------

Section 26: Artificial Neural Networks

Section 27: Convolutional Neural Networks

Section 28: Principal Component Analysis (PCA)

Section 29: Linear Discriminant Analysis (LDA)

Section 30: Kernel PCA

Section 31: Model Selection

Section 32: XGBoost

Course Description

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

Requirements

  • Just some high school mathematics level.

Description

Interested in the field of 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 will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way:

  • Part 1 - Data Preprocessing
  • Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
  • Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
  • Part 4 - Clustering: K-Means, Hierarchical Clustering
  • Part 5 - Association Rule Learning: Apriori, Eclat
  • Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
  • Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP
  • Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
  • Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA
  • Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

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

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

Who this course is for:

  • Anyone interested in Machine Learning.
  • Students who have at least high school knowledge in math and who want to start learning Machine Learning.
  • Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
  • Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
  • Any students in college who want to start a career in Data Science.
  • Any data analysts who want to level up in Machine Learning.
  • Any people who are not satisfied with their job and who want to become a Data Scientist.
  • Any people who want to create added value to their business by using powerful Machine Learning tools.