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 Highlights | Details |
---|---|

Registration Link | Students can Join Now and get a discount of up to 90% off the regular price ( |

Duration | 43 Hours |

Rating | 4.5/5 |

Student Enrollment | 907,113 students |

Instructor | Kirill Eremenko https://www.linkedin.com/in/kirilleremenko |

Topics Covered | How to use the ML A-Z folder & Google Colab, Split the data into a Training and Test set, R – Encoding Categorical Data |

Course Level | Intermediate |

Total Student Reviews | 163,790 |

## 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**

Parameters | Machine Learning A-Z™: Python & R in Data Science [2023] | Data Science A-Z™: Real-Life Data Science Exercises Included | R Programming A-Z™: R For Data Science With Real Exercises! |
---|---|---|---|

Offers | Students can join the course now and get an exclusive discount of up to 90% off the regular price by clicking on the link. | ||

Registration Link | Apply Now! | Apply Now! | Apply Now! |

Duration | 43 hours | 21 hours | 10.5 hours |

Rating | 4.5 /5 | 4.5 /5 | 4.7 /5 |

Student Enrollments | 907,113 | 208,256 | 244,647 |

Instructors | Kirill Eremenko | Kirill Eremenko | Kirill Eremenko |

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