With our full and interesting video course, “**Machine Learning** A-ZTM: AI, **Python & R + ChatGPT Bonus** [2023],” explore the cutting-edge world of machine learning. This course, which was created to give students in-depth knowledge and useful abilities, is aimed to be your go-to manual for understanding AI, Python, R, and making the most of ChatGPT.

You’ll set out on a trip that explains the complex subject of AI and gives you the tools you need to realize its full potential with more than 30 hours of riveting video content. Each module has been carefully designed, covering everything from the basics to more complex ideas, to guarantee a seamless learning process.

Our training equips you with concrete abilities that enable you to tap into AI’s full potential. It doesn’t merely focus on theory. Our course is suitable for students at all levels of ability, whether they are beginners exploring the world of AI for the first time or seasoned professionals wishing to advance their knowledge.

## Table of Contents

The course “Machine Learning A-ZTM: AI, Python & R + ChatGPT Bonus [2023]” goes beyond the confines of traditional schooling. It is more than just taking in information; it involves adopting a comprehensive educational experience. You will be equipped to use your knowledge in real-world situations thanks to an entertaining blend of thorough information, practical exercises, and informative examples.

## What You Will Learn:

- Python and R skills for machine learning mastery.
- building a solid intuition for different machine learning models.
- ability to forecast events accurately.
- the capacity to do insightful analysis.
- ability to build reliable machine learning models.
- generating significant value for your company with machine learning.
- use of machine learning for independent initiatives.
- handling of technical subjects including Deep Learning, Natural Language Processing (NLP), and Reinforcement Learning.
- effective use of cutting-edge techniques like dimension reduction.
- knowledge of the best machine learning model to use for each type of challenge.
- developing the skills to integrate powerful Machine Learning models in a variety of effective ways for problem-solving.
- Please get in touch if you require any additional help!

## Course content

### 1. Welcome to the course! Here we will help you get started in the

- best conditions.
- Welcome Challenge!
- 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)
- BONUS: Use ChatGPT to Boost your ML Skills

### 2. Part 1: Data Preprocessing

- Welcome to Part 1 – Data Preprocessing
- The Machine Learning_process
- Splitting the data into a Training and Test set
- Feature Scaling

### 3. Data Preprocessing in Python

- 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
- For Python learners, summary of Object-oriented programming:
- classes & objects
- Coding Exercise 1: Importing and Preprocessing a Dataset for
- Machine Learning
- Taking care of Missing Data – Step 1
- Taking care of Missing Data – Step 2
- Coding Exercise 2: Handling Missing Data in a Dataset for
- Machine Learning
- Encoding Categorical Data – Step
- Encoding Categorical Data – Step 2
- Encoding Categorical Data – Step 3
- Coding Exercise 3: Encoding Categorical Data for Machine Learning
- 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
- Coding Exercise 4: Dataset Splitting and Feature Scaling
- Feature Scaling – Step 1
- Feature Scaling – Step 2
- Feature Scaling – Step 3
- Feature Scaling – Step 4
- Coding exercise 5: Feature scaling for Machine Learning

### 4. Data Preprocessing in R

- 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
- Data Preprocessing Quiz

### 5. Part 2: Regression

- Welcome to Regression

### 6. Simple Linear Regression

- Simple Linear Regression Intuition
- Ordinary Least Squares v
- Simple Linear Regression in Python – Step la
- 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 – Stepl
- Simple Linear Regression in R – Step 2
- Simple Linear Regression in R – Step 3
- Simple Linear Regression in R – Step4a
- Simple Linear Regression in R – Step 4b
- Simple Linear Regression Quiz

### 7. Multiple Linear Regression

- Dataset + Business Problem Description
- Multiple Linear Regression Intuition
- Assumptions-of-LineaLRegcessicn
- 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 la
- 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 la
- 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

### 8. Polynomial Regression

- Polynomial Regression Intuition
- Polynomial Regression in Python – Step ia
- 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 la
- 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 – Stepl
- R Regression Template – Step 2
- Polynomial Regression Quiz

### 9. Support Vector Regression (SVR)

- SVR Intuition (Updated!) v
- Heads-up on non-linear SVR
- SVR in Python – Step la
- 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- Stepl
- SVR in R- step 2
- SVR Quiz

### 10. Decision Tree Regression

- Decision Tree Regression Intuition
- Decision Tree Regression in Python – Step la
- 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 – Stepl
- 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

### 11. Random Forest Regression

- 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

### 12. Evaluating Regression Models Performance

- R-Squared Intuition
- Adjusted R-Squared Intuition
- Evaluating Regression Models Performance Quiz

### 13. Regression Model Selection in Python

- 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

### 14. Regression Model Selection in R

- Evaluating Regression Models Performance – Homework’s Final
- part
- Interpreting Linear Regression Coefficients
- Conclusion of Part 2 – Regression

### 15. Part 3: Classification

- Welcome to Pan 3 – Classification

### 16. Logistic Regression

- What is Classification?
- Logistic Regression Intuition
- Maximum Likelihood
- Logistic Regression in Python – Step la
- 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 – Stepl v
- 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 v
- Machine Learning Regression and Classification BONUS
- Logistic Regression Quiz
- EXTRA CONTENT: Logistic Regression Practical Case study

### 17. K-Nearest Neighbors (K-NN)

- 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 stepl
- K-NN in R- step 2
- K-NN in R – step 3
- K-Nearest Neighbor Quiz

### 18. Support Vector Machine (SVM)

- Intuition
- SVM in Python – Step 1
- SVM in Python – Step 2
- SVM in Python – Step 3
- SVM in R – Stepl
- SVM in R- step 2
- SVM Quiz

### 19. Kernel SVM

- 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 – Stepl v
- Kernel SVM in R – Step 2
- Kernel SVM in R – Step 3
- Kernel SVM Quiz

### 20. Naive Bayes

- 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 v
- Naive Bayes in R – Step 2
- Naive Bayes in R – Step 3
- Naive Bayes Quiz

### 21. Decision Tree Classification

- Decision Tree Classification Intuition
- Decision Tree Classification in Python – Step 1
- Decision Tree Classification in Python – Step 2
- Decision Tree Classification in R – Stepl v
- Decision Tree Classification in R – Step 2
- Decision Tree Classification in R – Step 3
- Decision Tree Classification Quiz

### 22. Random Forest Classification

- Random Forest Classification Intuition
- Random Forest Classification in Python – Step 1
- Random Forest Classification in Python – Step 2
- Random Forest Classification in R – Stepl
- Random Forest Classification in R – Step 2
- Random Forest Classification in R – Step 3
- Random Forest Classification Quiz

### 23. Classification Model Selection in Python

- 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

### 24. Evaluating Classification Models Performance

- False Positives & False Negatives
- Accuracy Paradox
- CAP Curve
- CAP Curve Analysis
- Conclusion of Part 3 – Classification
- Evaluating Classiification Model Performance Quiz

### 25. Part 4: Clustering

- Welcome to Part 4 – Clustering

### 26. K-Means Clustering

- What is vs Unsupervised Learning)
- K-Means Clustering Intuition v
- The Elbow Method v
- K-Means+
- K-Means Clustering in Python – Step la
- 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 – Stepl
- K-Means Clustering in R- step 2
- K-Means Clustering Quiz

### 27. Hierarchical Clustering

- 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

### 28. — Part 5: Association Rule Learning

- Welcome to Pan 5 – Association Rule Learning

### 29. Part 5: Association Rule Learning —

### 30. Apriori

- 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

### 31. Eclat

- Eclat Intuition
- Eclat in Python
- Eclat in R
- Eclat Quiz

### 32. Part 6: Reinforcement Learning

- Welcome to Part 6 – Reinforcement Learning

### 33. Upper Confidence Bound (UCB)

- Ihe Multi-Armed Bandit Problem
- UppeLCQnfidence-RQu—cL
- n (IOCB) 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

### 34. Thompson Sampling

- Thompson Sampling Intuition
- Algorithm Comparison: IJCB 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

### 35. Part 7: Natural Language Processing

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

### 36. Part 8: Deep Learning

- Welcome to Part 8 – Deep Learning
- What is Deep Learning?
- Deep Learning Quiz

### 37. 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
- 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-Step1
- NN in R – step 2
- NN in R – step 3
- NN in R – Step 4 (Last step)
- Deep Learning Additional Content
- EXTRA CONTENT: ANN Case study
- NN QUIZ

### 38. Convolutional Neural Networks

- Plan of attack
- What are convolutional neural networks?
- Step 1 – Convolution Operation
- Step I(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

### 39 Part 9: Dimensionality Reduction

### 40. Principal Component Analysis (PCA)

- Principal Component Analysis (PCA) Intuition
- PCA in Python – Step 1
- PCA in Python – Step 2
- PCAin R – step 1
- PCAin R – step 2
- PCAin R – step 3
- PCA Quiz

### 41. Linear Discriminant Analysis (LDA)

- Linear Discriminant Analysis (LDA) Intuition
- LDA in Python
- LDAinR
- LDA Quiz

### 42. Kernel PCA

- Kernel PCA in Python
- Kernel PCA in

### 43. Part 10: Model Selection & Boosting

- Welcome to Pan 10 – Model Selection & Boosting

### 44. Model Selection

- k-Fold Cross Validation in Python
- Grid Search in Python
- k-Fold Cross Validation in
- Grid Search in

### 45. XGBoost

- XGBoost in Python
- Model Selection and Boosting Additional Content
- XGBoost in R

### 46. Exclusive Offer

- “*OUR SPECIAL

### 47. Annex: Logistic Regression (Long Explanation)

- Logistic Regression Intuition

## Requirements

- Please provide your request or sentence for the next rewrite.

## Target Audience for This Course:

- Individuals with an interest in Machine Learning.
- Students possessing at least a high school level of math knowledge and seeking to initiate their Machine Learning journey.
- Intermediate learners familiar with the fundamentals of machine learning, such as classical algorithms like linear regression or logistic regression, but aiming to delve deeper into the subject and explore its various domains.
- Individuals who may not be very comfortable with coding, yet hold an interest in Machine Learning and its practical application on datasets.
- College students aspiring to embark on a career in Data Science.
- Data analysts aiming to enhance their proficiency in Machine Learning.
- Those dissatisfied with their current occupation and harboring ambitions of becoming a Data Scientist.
- Individuals looking to add value to their business by harnessing the capabilities of robust Machine Learning tools.
- Please let me know if there’s anything else you need assistance with!

### Is previous programming knowledge required?

While having a fundamental understanding of programming can be helpful, our course is structured to suit students of all skill levels. We start with the basics and work our way up to more complex subjects.

### How much time will I have access to the course?

After joining, you will have free access to the course content. You are free to review the content any time necessary and to study at your own speed.

### Will I receive a certificate upon completion?

Absolutely! Upon successfully finishing the course, you’ll receive a prestigious completion certificate, showcasing your newfound expertise in AI, Python, R, and ChatGPT.

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