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

Education Şub 27

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

Instructor: Kirill Eremenko

Hello my name is Kirill Eremenko.

I’m a data scientist before 6 years of experience and the data centers coach with over 50 thousand students worldwide.

And today I’d like to welcome you into the machine learning A-Z that course brought to you by super data science

So what is this course all about.
Well machine learning is very broad I’m becoming an expert in this field can be very challenging just

to the lack of a clear pathway in all of the materials you can find online.
And that is why I have partnered up with machine learning expert on the part of his X Google and he has a mosses in machine learning.
And together we have brought you this course on machine learning.

And in this course you can actually study everything about this field.
We’ll talk about things like regression classification clustering Association rule learning reinforcement learning natural language processing and deep learning.
And moreover for every one of those braunches machine learning we look at between two and seven differentalgorithms.

And for every one of the algorithms we’ll show you how to create it and coded in are and in Python.

And not only will we show you how to code it but also will give you take takeaway templates which you can download and keep both in art and in Python.

So overall in this course you will learn the theory behind all of these algorithms and also you will learn how to create them into different programming languages so you can Shucky own course you can either take it in or or in Python or you can take it in both programming languages and truly boost your machine learning skills.

I hope this sounds very exciting to you because I’ve been a lot of effort into this course.
Our goal is to bring the world the best and the most powerful machine learning course that will be referenced by everyone so we’re constantly adding new materials and updating the scores.
And we can’t wait to see inside the closet. This is your opportunity to jump on board and a truly monster machine learning and we look forward to seeing you inside.

Until then enjoy machine learning.

What is this course teaching

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

Course Details

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.

Bu kurs kimler için uygun:
  • 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.

What's ın Course Content

Welcome to the course!

  • Applications of Machine Learning
  • Önizleme
  • Why Machine Learning is the Future
  • Important notes, tips & tricks for this course
  • This PDF resource will help you a lot
  • Updates on Udemy Reviews
  • Installing Python and Anaconda (Mac, Linux & Windows)
  • Update: Recommended Anaconda Version
  • Installing R and R Studio (Mac, Linux & Windows) (Free)
  • BONUS: Meet your instructors

Part 1: Data Preprocessing

  • Welcome to Part 1 – Data Preprocessing (Free)
  • Get the dataset
  • Importing the Libraries
  • Importing the Dataset
  • For Python learners, summary of Object-oriented programming: classes & objects
  • Missing Data
  • Categorical Data
  • WARNING – Update
  • Splitting the Dataset into the Training set and Test set
  • Feature Scaling
  • And here is our Data Preprocessing Template!
  • Data Preprocessing

Part 2: Regression

  • Welcome to Part 2 – Regression

Simple Linear Regression

  • How to get the dataset
  • Dataset + Business Problem Description
  • Simple Linear Regression Intuition – Step 1
  • Simple Linear Regression Intuition – Step 2
  • Simple Linear Regression in Python – Step 1
  • Simple Linear Regression in Python – Step 2
  • Simple Linear Regression in Python – Step 3
  • Simple Linear Regression in Python – Step 4
  • Simple Linear Regression in R – Step 1
  • Simple Linear Regression in R – Step 2 (Free)
  • Simple Linear Regression in R – Step 3
  • Simple Linear Regression in R – Step 4
  • Simple Linear Regression

Multiple Linear Regression

  • How to get the dataset
  • Dataset + Business Problem Description
  • Multiple Linear Regression Intuition – Step 1
  • Multiple Linear Regression Intuition – Step 2
  • Multiple Linear Regression Intuition – Step 3
  • Multiple Linear Regression Intuition – Step 4
  • Prerequisites: What is the P-Value?
  • Multiple Linear Regression Intuition – Step 5
  • Multiple Linear Regression in Python – Step 1
  • Multiple Linear Regression in Python – Step 2
  • Multiple Linear Regression in Python – Step 3
  • Multiple Linear Regression in Python – Backward Elimination – Preparation
  • Multiple Linear Regression in Python – Backward Elimination – HOMEWORK !
  • Multiple Linear Regression in Python – Backward Elimination – Homework Solution
  • Multiple Linear Regression in Python – Automatic Backward Elimination
  • Multiple Linear Regression in R – Step 1
  • Multiple Linear Regression in R – Step 2
  • Multiple Linear Regression in R – Step 3
  • Multiple Linear Regression in R – Backward Elimination – HOMEWORK !
  • Multiple Linear Regression in R – Backward Elimination – Homework Solution
  • Multiple Linear Regression in R – Automatic Backward Elimination
  • Multiple Linear Regression

Polynomial Regression

  • Polynomial Regression Intuition
  • How to get the dataset
  • Polynomial Regression in Python – Step 1
  • Polynomial Regression in Python – Step 2
  • Polynomial Regression in Python – Step 3
  • Polynomial Regression in Python – Step 4
  • Python Regression Template
  • Polynomial Regression in R – Step 1
  • Polynomial Regression in R – Step 2
  • Polynomial Regression in R – Step 3
  • Polynomial Regression in R – Step 4
  • R Regression Template

Support Vector Regression (SVR)

  • How to get the dataset
  • SVR Intuition
  • SVR in Python
  • SVR in R

Decision Tree Regression

  • Decision Tree Regression Intuition
  • How to get the dataset
  • Decision Tree Regression in Python
  • Decision Tree Regression in R

Random Forest Regression

  • Random Forest Regression Intuition
  • How to get the dataset
  • Random Forest Regression in Python
  • Random Forest Regression in R

Evaluating Regression Models Performance

  • R-Squared Intuition
  • Adjusted R-Squared Intuition
  • Evaluating Regression Models Performance – Homework’s Final Part
  • Interpreting Linear Regression Coefficients
  • Conclusion of Part 2 – Regression

Part 3: Classification

  • Welcome to Part 3 – Classification

Logistic Regression

  • Logistic Regression Intuition
  • How to get the dataset
  • Logistic Regression in Python – Step 1
  • Logistic Regression in Python – Step 2
  • Logistic Regression in Python – Step 3
  • Logistic Regression in Python – Step 4(Free)
  • Logistic Regression in Python – Step 5
  • Python Classification Template
  • Logistic Regression in R – Step 1
  • Logistic Regression in R – Step 2
  • Logistic Regression in R – Step 3
  • Logistic Regression in R – Step 4 (Free)
  • Logistic Regression in R – Step 5
  • R Classification Template
  • Logistic Regression

K-Nearest Neighbors (K-NN)

  • K-Nearest Neighbor Intuition
  • How to get the dataset
  • K-NN in Python
  • K-NN in R
  • K-Nearest Neighbor

Support Vector Machine (SVM)

  • SVM Intuition
  • How to get the dataset
  • SVM in Python
  • SVM in R

Kernel SVM

  • Kernel SVM Intuition
  • Mapping to a higher dimension (Free)
  • The Kernel Trick
  • Types of Kernel Functions
  • How to get the dataset
  • Kernel SVM in Python
  • Kernel SVM in R

Naive Bayes

  • Bayes Theorem (Free)
  • Naive Bayes Intuition (Free)
  • Naive Bayes Intuition (Challenge Reveal)
  • Naive Bayes Intuition (Extras)
  • How to get the dataset
  • Naive Bayes in Python
  • Naive Bayes in R

Decision Tree Classification

  • Decision Tree Classification Intuition
  • How to get the dataset
  • Decision Tree Classification in Python
  • Decision Tree Classification in R

Random Forest Classification

  • Random Forest Classification Intuition
  • How to get the dataset
  • Random Forest Classification in Python
  • Random Forest Classification in R

Evaluating Classification Models Performance

  • False Positives & False Negatives
  • Confusion Matrix
  • Accuracy Paradox
  • CAP Curve
  • CAP Curve Analysis
  • Conclusion of Part 3 – Classification

Part 4: Clustering

  • Welcome to Part 4 – Clustering

K-Means Clustering

  • K-Means Clustering Intuition (Free)
  • K-Means Random Initialization Trap
  • K-Means Selecting The Number Of Clusters
  • How to get the dataset
  • K-Means Clustering in Python
  • K-Means Clustering in R
  • K-Means Clustering

Hierarchical Clustering

  • Hierarchical Clustering Intuition (Free)
  • Hierarchical Clustering How Dendrograms Work
  • Hierarchical Clustering Using Dendrograms
  • How to get the dataset
  • HC in Python – Step 1
  • HC in Python – Step 2
  • HC in Python – Step 3 (Free)
  • HC in Python – Step 4
  • HC in Python – Step 5
  • HC in R – Step 1
  • HC in R – Step 2
  • HC in R – Step 3 (Free)
  • HC in R – Step 4
  • HC in R – Step 5
  • Hierarchical Clustering
  • Conclusion of Part 4 – Clustering

Part 5: Association Rule Learning

  • Welcome to Part 5 – Association Rule Learning

Apriori

  • Apriori Intuition
  • How to get the dataset
  • Apriori in R – Step 1
  • Apriori in R – Step 2
  • Apriori in R – Step 3
  • Apriori in Python – Step 1
  • Apriori in Python – Step 2
  • Apriori in Python – Step 3

Eclat

  • Eclat Intuition
  • How to get the dataset
  • Eclat in R

Part 6: Reinforcement Learning

  • Welcome to Part 6 – Reinforcement Learning

Upper Confidence Bound (UCB)

  • The Multi-Armed Bandit Problem (Free)
  • Upper Confidence Bound (UCB) Intuition (Free)
  • How to get the dataset
  • Upper Confidence Bound in Python – Step 1
  • Upper Confidence Bound in Python – Step 2
  • Upper Confidence Bound in Python – Step 3
  • Upper Confidence Bound in Python – Step 4
  • Upper Confidence Bound in R – Step 1
  • Upper Confidence Bound in R – Step 2
  • Upper Confidence Bound in R – Step 3
  • Upper Confidence Bound in R – Step 4

Thompson Sampling

  • Thompson Sampling Intuition
  • Algorithm Comparison: UCB vs Thompson Sampling
  • How to get the dataset
  • Thompson Sampling in Python – Step 1
  • Thompson Sampling in Python – Step 2
  • Thompson Sampling in R – Step 1
  • Thompson Sampling in R – Step 2

Part 7: Natural Language Processing

  • Welcome to Part 7 – Natural Language Processing
  • Natural Language Processing Intuition
  • How to get the dataset
  • Natural Language Processing in Python – Step 1
  • Natural Language Processing in Python – Step 2
  • Natural Language Processing in Python – Step 3
  • Natural Language Processing in Python – Step 4
  • Natural Language Processing in Python – Step 5
  • Natural Language Processing in Python – Step 6
  • Natural Language Processing in Python – Step 7
  • Natural Language Processing in Python – Step 8
  • Natural Language Processing in Python – Step 9
  • Natural Language Processing in Python – Step 10
  • Homework Challenge
  • Natural Language Processing in R – Step 1
  • Natural Language Processing in R – Step 2
  • Natural Language Processing in R – Step 3
  • Natural Language Processing in R – Step 4
  • Natural Language Processing in R – Step 5
  • Natural Language Processing in R – Step 6
  • Natural Language Processing in R – Step 7
  • Natural Language Processing in R – Step 8
  • Natural Language Processing in R – Step 9
  • Natural Language Processing in R – Step 10
  • Homework Challenge

Part 8: Deep Learning

  • Welcome to Part 8 – Deep Learning
  • What is Deep Learning?

Artificial Neural Networks

  • Plan of attack
  • The Neuron
  • The Activation Function
  • How do Neural Networks work?
  • How do Neural Networks learn?
  • Gradient Descent
  • Stochastic Gradient Descent
  • Backpropagation
  • How to get the dataset
  • Business Problem Description
  • Installing Keras
  • ANN in Python – Step 1
  • ANN in Python – Step 2
  • ANN in Python – Step 3
  • ANN in Python – Step 4
  • ANN in Python – Step 5
  • ANN in Python – Step 6
  • ANN in Python – Step 7
  • ANN in Python – Step 8
  • ANN in Python – Step 9
  • ANN in Python – Step 10
  • ANN in R – Step 1
  • ANN in R – Step 2
  • ANN in R – Step 3
  • ANN in R – Step 4 (Last step)

Convolutional Neural Networks

  • Plan of attack
  • What are convolutional neural networks?
  • Step 1 – Convolution Operation
  • Step 1(b) – ReLU Layer
  • Step 2 – Pooling
  • Step 3 – Flattening
  • Step 4 – Full Connection
  • Summary
  • Softmax & Cross-Entropy
  • How to get the dataset
  • Installing Keras
  • CNN in Python – Step 1
  • CNN in Python – Step 2
  • CNN in Python – Step 3
  • CNN in Python – Step 4
  • CNN in Python – Step 5
  • CNN in Python – Step 6
  • CNN in Python – Step 7
  • CNN in Python – Step 8
  • CNN in Python – Step 9
  • CNN in Python – Step 10
  • CNN in R

Part 9: Dimensionality Reduction

  • Welcome to Part 9 – Dimensionality Reduction

Principal Component Analysis (PCA)

  • Principal Component Analysis (PCA) Intuition
  • How to get the dataset
  • PCA in Python – Step 1
  • PCA in Python – Step 2
  • PCA in Python – Step 3
  • PCA in R – Step 1
  • PCA in R – Step 2
  • PCA in R – Step 3

Linear Discriminant Analysis (LDA)

  • Linear Discriminant Analysis (LDA) Intuition
  • How to get the dataset
  • LDA in Python
  • LDA in R

Kernel PCA

  • How to get the dataset
  • Kernel PCA in Python
  • Kernel PCA in R

Part 10: Model Selection & Boosting

  • Welcome to Part 10 – Model Selection & Boosting

Model Selection

  • How to get the dataset
  • k-Fold Cross Validation in Python
  • k-Fold Cross Validation in R
  • Grid Search in Python – Step 1
  • Grid Search in Python – Step 2
  • Grid Search in R

XGBoost

  • How to get the dataset
  • XGBoost in Python – Step 1
  • XGBoost in Python – Step 2
  • XGBoost in R

Bonus Lectures

  • YOUR SPECIAL BONUS

Student Reviews

Great experience and great knowledge. It covers pretty much every topic in Machine Learning. After finishing up the course I can say I have a lot of experience, insights on Machine Learning Model. I am looking forward to taking other courses!
Christian Orquera
⭐️⭐️⭐️⭐️⭐️

Kirill and hadelin made the Machine learning A-Z very seamless. This course was very well organized,planned and communicated. Highly recommended for beginners. It kept me engaged and was straight to the point.
Nehali Tolia
⭐️⭐️⭐️⭐️⭐️

Overall: Wonderful course for a beginner.

Pros:

1. Covered all kinds of machine algorithm types and their models.

2. Concise and well explained models & algorithms.

3. Clear explanation of the models starting with the intuition leading up to the practical usage of the model.

Cons:

1. Would have liked to see more real-life examples discussed in the exercises. Some of the exercises were made too simplified for the sake of easy explanation. Need to keep them at real-life level to bring the students up to the expectation of what they will encounter in real scenarios and also come across the difficulties faced while solving them.

2. Also there were few models for which the intuition topic was delivered by instructor other than Kirill. Please redesign those lectures again and have Kirilli explain those topics. The other lecturers were nowhere near the level of Kirill as far as explaining the concepts and delivering the insights and deep understanding that Kirill brings.

However overall, it is a super course ought to the taken by any student foraying his entry into the world of Data Science.
Saurabh Kumar
⭐️⭐️⭐️⭐️⭐️

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