machine learning

The ‘Machine Learning A-Z™: Python & R in Data Science [2023] Course’ makes it easier for students to understand difficult theories, algorithms, and coding libraries. This course is trusted by more than 900,000 students worldwide. Students will learn new skills and deepen their understanding of this difficult yet lucrative area of data science with each course. While delving deeply into machine learning, this course is interesting and fun. Additionally, the course includes hands-on projects and exercises that are based on actual case studies.

Originally, this course is priced between INR 2,000 to INR 4,000. Students can Enroll Now and get an exclusive discount of up to 90% off the regular price by clicking on the link.

Course Highlights

Key HighlightsDetails
Registration LinkStudents can Join Now and get a discount of up to 90% off the regular price (INR 3,399).
Duration43 Hours
Rating4.5/5
Student Enrollment907,113 students
InstructorKirill Eremenko https://www.linkedin.com/in/kirilleremenko
Topics CoveredHow to use the ML A-Z folder & Google Colab, Split the data into a Training and Test set, R – Encoding Categorical Data
Course LevelIntermediate
Total Student Reviews163,790

Who all can opt for this course?

  • Everybody with an interest in machine learning
  • Students who wish to begin learning machine learning and who have at least a high school math background
  • Any intermediate-level individuals who are familiar with the fundamentals of machine learning, including the traditional techniques like logistic regression or regression with covariance, but who wish to learn more and explore all the various areas of machine learning
  • Anyone interested in machine learning who is not very comfortable with coding and wants to apply it quickly to datasets
  • Any college students that wish to begin a career in data science
  • Anyone looking to advance their machine-learning skills in data analysis
  • Anyone who wants to become a data scientist but is dissatisfied with their current position
  • Anyone who wants to use strong machine learning techniques to provide value to their business

Learning Outcomes

  • Master Python and R for Machine Learning
  • Possess excellent machine learning model intuition
  • Make reliable assumptions
  • Conduct insightful analysis
  • Construct reliable machine-learning models
  • Add significant value to your company
  • Utilize machine learning for private gain
  • Handle specialized subjects like Deep Learning, NLP, and Reinforcement Learning
  • Handle cutting-edge methods like dimension reduction
  • Identify the appropriate machine learning model for each kind of challenge
  • Develop a massive army of effective machine learning models and be able to mix them to address any issue

Course Content

S.No.Module (Duration)Topics
1.Welcome to the course! Here we will help you get started in the best conditions. (16 minutes)Machine Learning Demo – Get Excited!
Get all the Datasets, Codes, and Slides here
How to use the ML A-Z folder & Google Colab
Installing R and R Studio (Mac, Linux & Windows)
Extra Resources
2.——————– Part 1: Data Preprocessing ——————– (21 seconds)Welcome to Part 1 – Data Preprocessing
3.The Steps of Data Preprocessing (01 hour)The Machine Learning process
Splitting the data into a Training and Test set
Feature Scaling
Welcome to Data Preprocessing Hands-On
Python – Importing the libraries
Python – Importing the Dataset
For Python learners, a summary of Object-oriented programming: classes & objects
Python – Taking Care of Missing Data
Python – Encoding Categorical Data
Python – Splitting the dataset into a Training set and Test set
Python – Feature Scaling
R – Importing the Dataset
R – Taking Care of Missing Data
R – Encoding Categorical Data
R – Splitting the dataset into a Training set and Test set
R – Feature Scaling
Data Preprocessing Quiz
4.——————– Part 2: Regression ——————– (21 seconds)Welcome to Part 2 – Regression
5.Simple Linear Regression (01 hour 07 minutes)Simple Linear Regression Intuition
Ordinary Least Squares
Simple Linear Regression in Python – Step 1a
Simple Linear Regression in Python – Step 1b
Simple Linear Regression in Python – Step 2a
Simple Linear Regression in Python – Step 2b
Simple Linear Regression in Python – Step 3
Simple Linear Regression in Python – Step 4a
Simple Linear Regression in Python – Step 4b
Simple Linear Regression in Python – Additional Lecture
Simple Linear Regression in R – Step 1
Simple Linear Regression in R – Step 2
Simple Linear Regression in R – Step 3
Simple Linear Regression in R – Step 4a
Simple Linear Regression in R – Step 4b
Simple Linear Regression Quiz
6.Multiple Linear Regression (02 hours 16 minutes)Dataset + Business Problem Description
Multiple Linear Regression Intuition
Assumptions of Linear Regression
Multiple Linear Regression Intuition – Step 3
Multiple Linear Regression Intuition – Step 4
Understanding the P-Value
Multiple Linear Regression Intuition – Step 5
Multiple Linear Regression in Python – Step 1a
Multiple Linear Regression in Python – Step 1b
Multiple Linear Regression in Python – Step 2a
Multiple Linear Regression in Python – Step 2b
Multiple Linear Regression in Python – Step 3a
Multiple Linear Regression in Python – Step 3b
Multiple Linear Regression in Python – Step 4a
Multiple Linear Regression in Python – Step 4b
Multiple Linear Regression in Python – Backward Elimination
Multiple Linear Regression in Python – Extra Content
Multiple Linear Regression in R – Step 1a
Multiple Linear Regression in R – Step 1b
Multiple Linear Regression in R – Step 2a
Multiple Linear Regression in R – Step 2b
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 Quiz
7.Polynomial Regression (01 hours 39 minutes)Polynomial Regression Intuition
Polynomial Regression in Python – Step 1a
Polynomial Regression in Python – Step 1b
Polynomial Regression in Python – Step 2a
Polynomial Regression in Python – Step 2b
Polynomial Regression in Python – Step 3a
Polynomial Regression in Python – Step 3b
Polynomial Regression in Python – Step 4a
Polynomial Regression in Python – Step 4b
Polynomial Regression in R – Step 1a
Polynomial Regression in R – Step 1b
Polynomial Regression in R – Step 2a
Polynomial Regression in R – Step 2b
Polynomial Regression in R – Step 3a
Polynomial Regression in R – Step 3b
Polynomial Regression in R – Step 3c
Polynomial Regression in R – Step 4a
Polynomial Regression in R – Step 4b
R Regression Template – Step 1
R Regression Template – Step 2
Polynomial Regression Quiz
8.Support Vector Regression (SVR) (01 hour 03 minutes)SVR Intuition (Updated!)
Heads-up on non-linear SVR
SVR in Python – Step 1a
SVR in Python – Step 1b
SVR in Python – Step 2a
SVR in Python – Step 2b
SVR in Python – Step 2c
SVR in Python – Step 3
SVR in Python – Step 4
SVR in Python – Step 5a
SVR in Python – Step 5b
SVR in R – Step 1
SVR in R – Step 2
SVR Quiz
9.Decision Tree Regression (52 minutes)Decision Tree Regression Intuition
Decision Tree Regression in Python – Step 1a
Decision Tree Regression in Python – Step 1b
Decision Tree Regression in Python – Step 2
Decision Tree Regression in Python – Step 3
Decision Tree Regression in Python – Step 4
Decision Tree Regression in R – Step 1
Decision Tree Regression in R – Step 2
Decision Tree Regression in R – Step 3
Decision Tree Regression in R – Step 4
Decision Tree Regression Quiz
10.Random Forest Regression (35 minutes)Random Forest Regression Intuition
Random Forest Regression in Python – Step 1
Random Forest Regression in Python – Step 2
Random Forest Regression in R – Step 1
Random Forest Regression in R – Step 2
Random Forest Regression in R – Step 3
Random Forest Regression Quiz
11.Evaluating Regression Models Performance (10 minutes)R-Squared Intuition
Adjusted R-Squared Intuition
Evaluating Regression Models Performance Quiz
12.Regression Model Selection in Python (29 minutes)Make sure you have this Model Selection folder ready
Preparation of the Regression Code Templates – Step 1
Preparation of the Regression Code Templates – Step 2
Preparation of the Regression Code Templates – Step 3
Preparation of the Regression Code Templates – Step 4
THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! – STEP 1
THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! – STEP 2
Conclusion of Part 2 – Regression
13.Regression Model Selection in R (19 minutes)Evaluating Regression Models Performance – Homework’s Final Part
Interpreting Linear Regression Coefficients
Conclusion of Part 2 – Regression
14.——————– Part 3: Classification ——————– (21 seconds)Welcome to Part 3 – Classification
15.Logistic Regression (01 hour 57 minutes)What is Classification?
Logistic Regression Intuition
Maximum Likelihood
Logistic Regression in Python – Step 1a
Logistic Regression in Python – Step 1b
Logistic Regression in Python – Step 2a
Logistic Regression in Python – Step 2b
Logistic Regression in Python – Step 3a
Logistic Regression in Python – Step 3b
Logistic Regression in Python – Step 4a
Logistic Regression in Python – Step 4b
Logistic Regression in Python – Step 5
Logistic Regression in Python – Step 6a
Logistic Regression in Python – Step 6b
Logistic Regression in Python – Step 7a
Logistic Regression in Python – Step 7b
Logistic Regression in Python – Step 7c
Logistic Regression in Python – Step 7 (Colour-blind friendly image)
Logistic Regression in R – Step 1
Logistic Regression in R – Step 2
Logistic Regression in R – Step 3
Logistic Regression in R – Step 4
Warning – Update
Logistic Regression in R – Step 5a
Logistic Regression in R – Step 5b
Logistic Regression in R – Step 5c
Logistic Regression in R – Step 5 (Colour-blind friendly image)
R Classification Template
Machine Learning Regression and Classification BONUS
Logistic Regression Quiz
EXTRA CONTENT: Logistic Regression Practical Case Study
16.K-Nearest Neighbors (K-NN) (37 minutes)K-Nearest Neighbor Intuition
K-NN in Python – Step 1
K-NN in Python – Step 2
K-NN in Python – Step 3
K-NN in R – Step 1
K-NN in R – Step 2
K-NN in R – Step 3
K-Nearest Neighbor Quiz
17.Support Vector Machine (SVM) (35 minutes)SVM Intuition
SVM in Python – Step 1
SVM in Python – Step 2
SVM in Python – Step 3
SVM in R – Step 1
SVM in R – Step 2
SVM Quiz
18.Kernel SVM (01 hours 06 minutes)Kernel SVM Intuition
Mapping to a higher dimension
The Kernel Trick
Types of Kernel Functions
Non-Linear Kernel SVR (Advanced)
Kernel SVM in Python – Step 1
Kernel SVM in Python – Step 2
Kernel SVM in R – Step 1
Kernel SVM in R – Step 2
Kernel SVM in R – Step 3
Kernel SVM Quiz
19.Naive Bayes (01 hour 16 minutes)Bayes Theorem
Naive Bayes Intuition
Naive Bayes Intuition (Challenge Reveal)
Naive Bayes Intuition (Extras)
Naive Bayes in Python – Step 1
Naive Bayes in Python – Step 2
Naive Bayes in Python – Step 3
Naive Bayes in R – Step 1
Naive Bayes in R – Step 2
Naive Bayes in R – Step 3
Naive Bayes Quiz
20.Decision Tree Classification (37 minutes)Decision Tree Classification Intuition
Decision Tree Classification in Python – Step 1
Decision Tree Classification in Python – Step 2
Decision Tree Classification in R – Step 1
Decision Tree Classification in R – Step 2
Decision Tree Classification in R – Step 3
Decision Tree Classification Quiz
21.Random Forest Classification (33 minutes)Random Forest Classification Intuition
Random Forest Classification in Python – Step 1
Random Forest Classification in Python – Step 2
Random Forest Classification in R – Step 1
Random Forest Classification in R – Step 2
Random Forest Classification in R – Step 3
Random Forest Classification Quiz
22.Classification Model Selection in Python (25 minutes)Make sure you have this Model Selection folder ready
Confusion Matrix & Accuracy Ratios
ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION – STEP 1
ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION – STEP 2
ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION – STEP 3
ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION – STEP 4
23.Evaluating Classification Models Performance (29 minutes)False Positives & False Negatives
Accuracy Paradox
CAP Curve
CAP Curve Analysis
Conclusion of Part 3 – Classification
Evaluating Classification Model Performance Quiz
24.——————– Part 4: Clustering ——————– (21 seconds)Welcome to Part 4 – Clustering
25.K-Means Clustering (01 hour 24 minutes)What is Clustering? (Supervised vs Unsupervised Learning)
K-Means Clustering Intuition
The Elbow Method
K-Means++
K-Means Clustering in Python – Step 1a
K-Means Clustering in Python – Step 1b
K-Means Clustering in Python – Step 2a
K-Means Clustering in Python – Step 2b
K-Means Clustering in Python – Step 3a
K-Means Clustering in Python – Step 3b
K-Means Clustering in Python – Step 3c
K-Means Clustering in Python – Step 4
K-Means Clustering in Python – Step 5a
K-Means Clustering in Python – Step 5b
K-Means Clustering in Python – Step 5c
K-Means Clustering in R – Step 1
K-Means Clustering in R – Step 2
K-Means Clustering Quiz
26.Hierarchical Clustering (01 hour 21 minutes)Hierarchical Clustering Intuition
Hierarchical Clustering How Dendrograms Work
Hierarchical Clustering Using Dendrograms
Hierarchical Clustering in Python – Step 1
Hierarchical Clustering in Python – Step 2a
Hierarchical Clustering in Python – Step 2b
Hierarchical Clustering in Python – Step 2c
Hierarchical Clustering in Python – Step 3a
Hierarchical Clustering in Python – Step 3b
Hierarchical Clustering in R – Step 1
Hierarchical Clustering in R – Step 2
Hierarchical Clustering in R – Step 3
Hierarchical Clustering in R – Step 4
Hierarchical Clustering in R – Step 5
Hierarchical Clustering Quiz
Conclusion of Part 4 – Clustering
27.——————– Part 5: Association Rule Learning ——————– (11 seconds)Welcome to Part 5 – Association Rule Learning
28.Apriori (02 hours 10 minutes)Apriori Intuition
Apriori in Python – Step 1
Apriori in Python – Step 2
Apriori in Python – Step 3
Apriori in Python – Step 4
Apriori in R – Step 1
Apriori in R – Step 2
Apriori in R – Step 3
Apriori Quiz
29.Eclat (28 minutes)Eclat Intuition
Eclat in Python
Eclat in R
Eclat Quiz
30.——————– Part 6: Reinforcement Learning ——————– (41 seconds)Welcome to Part 6 – Reinforcement Learning
31.Upper Confidence Bound (UCB) (02 hours 22 minutes)The Multi-Armed Bandit Problem
Upper Confidence Bound (UCB) Intuition
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 Python – Step 5
Upper Confidence Bound in Python – Step 6
Upper Confidence Bound in Python – Step 7
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
Upper Confidence Bound Quiz
32.Thompson Sampling (01 hour 30 minutes)Thompson Sampling Intuition
Algorithm Comparison: UCB vs Thompson Sampling
Thompson Sampling in Python – Step 1
Thompson Sampling in Python – Step 2
Thompson Sampling in Python – Step 3
Thompson Sampling in Python – Step 4
Additional Resource for this Section
Thompson Sampling in R – Step 1
Thompson Sampling in R – Step 2
Thompson Sampling Quiz
33.——————– Part 7: Natural Language Processing ——————– (03 hours 05 minutes)Welcome to Part 7 – Natural Language Processing
NLP Intuition
Types of Natural Language Processing
Classical vs Deep Learning Models
Bag-Of-Words Model
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 – BONUS
Homework Challenge
Natural Language Processing in R – Step 1
Warning – Update
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
Natural Language Processing Quiz
34.——————– Part 8: Deep Learning ——————– (12 minutes)Welcome to Part 8 – Deep Learning
What is Deep Learning?
Deep Learning Quiz
35.Artificial Neural Networks (03 hours 25 minutes)Plan of attack
The Neuron
The Activation Function
How do Neural Networks work?
How do Neural Networks learn?
Gradient Descent
Stochastic Gradient Descent
Backpropagation
Business Problem Description
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 R – Step 1
ANN in R – Step 2
ANN in R – Step 3
ANN in R – Step 4 (Last step)
Deep Learning Additional Content
EXTRA CONTENT: ANN Case Study
ANN QUIZ
36.Convolutional Neural Networks (03 hours 14 minutes)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
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 – FINAL DEMO!
Deep Learning Additional Content #2
CNN Quiz
37.——————– Part 9: Dimensionality Reduction ——————– (33 seconds)Welcome to Part 9 – Dimensionality Reduction
38.Principal Component Analysis (PCA) (01 hour 03 minutes)Principal Component Analysis (PCA) Intuition
PCA in Python – Step 1
PCA in Python – Step 2
PCA in R – Step 1
PCA in R – Step 2
PCA in R – Step 3
PCA Quiz
39.Linear Discriminant Analysis (LDA) (38 minutes)Linear Discriminant Analysis (LDA) Intuition
LDA in Python
LDA in R
LDA Quiz
40.Kernel PCA (31 minutes)Kernel PCA in Python
Kernel PCA in R
41.——————– Part 10: Model Selection & Boosting ——————– (29 seconds)Welcome to Part 10 – Model Selection & Boosting
42.Model Selection (01 hour 13 minutes)k-Fold Cross-Validation in Python
Grid Search in Python
k-Fold Cross Validation in R
Grid Search in R
43.XGBoost (33 minutes)XGBoost in Python
Model Selection and Boosting Additional Content
XGBoost in R
44.Exclusive Offer (01 minute)***OUR SPECIAL OFFER***
45.ANNEX – Data Preprocessing in Python (01 hour 31 minutes)Getting Started – Step 1
Getting Started – Step 2
Importing the Libraries
Importing the Dataset – Step 1
Importing the Dataset – Step 2
Importing the Dataset – Step 3
Taking care of Missing Data – Step 1
Taking care of Missing Data – Step 2
Encoding Categorical Data – Step 1
Encoding Categorical Data – Step 2
Encoding Categorical Data – Step 3
Splitting the dataset into the Training set and Test set – Step 1
Splitting the dataset into the Training set and Test set – Step 2
Splitting the dataset into the Training set and Test set – Step 3
Feature Scaling – Step 1
Feature Scaling – Step 2
Feature Scaling – Step 3
Feature Scaling – Step 4
46.ANNEX – Data Preprocessing in R (42 minutes)Getting Started
Dataset Description
Importing the Dataset
Taking care of Missing Data
Encoding Categorical Data
Splitting the dataset into the Training set and Test set – Step 1
Splitting the dataset into the Training set and Test set – Step 2
Feature Scaling – Step 1
Feature Scaling – Step 2
Data Preprocessing Template

Resources Required

Just high school-level math

Featured Review

Joseph Guth (5/5): The course is extensive in length bringing plenty of details with the fundamentals and step-by-step approach. It might be possible to trim at least a few videos from the past and make them more compressed such as this video. Nevertheless, this series is among one the best courses I have seen in an introduction to machine learning!

Pros

  • Ezeh Sunday (5/5): The teaching method is top-notch in combining both theories and their practical implementation.
  • Sebastián Girón Arcila (5/5): What a course!!! the best course in machine learning and data science
  • Humberto Barrantes (5/5): This is the perfect course for all those who want to start with Machine Learning.
  • Francisco Panis Kaseker (5/5): I am very happy that I was introduced to the AI Python tools by this course and now I can look for a deeper AI course.

Cons

  • Eric S. (3/5): It’s pretty good for getting a grasp on machine learning models if you’ve never studied them before. But if you have no calculus, linear algebra and programming background, it would be pretty hard to understand what’s really going on since the theory here is very surface level.
  • Ritika G. (3/5): I wish they included the mathematics behind various ml concepts also the explanation could have been better however the coding part was amazing. Overall enjoyed taking up this course. Thank You:)
  • Birru L. (3/5): It was good, but what I felt was some parts are very much repeated. Like going into A-Z folder and selecting the data set and uploading data set. This shouldn’t have been repeated.

About the Author

The instructor of this course is Kirill Eremenko who is a Data Scientist with a 4.5 instructor rating and 599,095 reviews on Udemy. He offers 59 courses and has taught 2,251,523 students so far.

  • Professionally, Kirill Eremenko is a data science consultant with experience in the retail, transportation, retail, and financial sectors
  • At Deloitte Australia, Kirill Eremenko has received training from the top analytics mentors, and since he started teaching on Udemy, he has shared his experience with thousands of aspiring data scientists
  • Students will quickly see from his courses how he gave skilled step-by-step tutoring in the field of data science by fusing my real-world expertise and academic background in physics and mathematics

Comparison Table

ParametersMachine Learning A-Z™: Python & R in Data Science [2023]Data Science A-Z™: Real-Life Data Science Exercises IncludedR Programming A-Z™: R For Data Science With Real Exercises!
OffersStudents can join the course now and get an exclusive discount of up to 90% off the regular price by clicking on the link.
Registration LinkApply Now!Apply Now!Apply Now!
Duration43 hours21 hours10.5 hours
Rating4.5 /54.5 /54.7 /5
Student Enrollments907,113208,256244,647
InstructorsKirill EremenkoKirill EremenkoKirill Eremenko

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