Machine Learning A-Z™: AI, Python & R + ChatGPT Bonus [2023]

Machine Learning A-Z™: AI, Python & R + ChatGPT Bonus [2023]

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 382 lectures (42h 34m) | 10.48 GB

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

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

Over 900,000 students world-wide trust this course.

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 can be completed by either doing either the Python tutorials, or R tutorials, or both – Python & R. Pick the programming language that you need for your career.

This course is fun and exciting, and 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

Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.

Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of 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.

What you’ll learn

  • Master Machine Learning on Python & R
  • Have a great intuition of many Machine Learning models
  • Make accurate predictions
  • Make 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
Table of Contents

Welcome to the course! Here we will help you get started in the best conditions
1 Welcome Challenge!
2 Machine Learning Demo – Get Excited!
3 Get all the Datasets, Codes and Slides here
4 How to use the ML A-Z folder & Google Colab
5 Installing R and R Studio (Mac, Linux & Windows)
6 BONUS Use ChatGPT to Boost your ML Skills

Part 1 Data Preprocessing ——————–
7 Welcome to Part 1 – Data Preprocessing
8 The Machine Learning process
9 Splitting the data into a Training and Test set
10 Feature Scaling

Data Preprocessing in Python
11 Getting Started – Step 1
12 Getting Started – Step 2
13 Importing the Libraries
14 Importing the Dataset – Step 1
15 Importing the Dataset – Step 2
16 Importing the Dataset – Step 3
17 For Python learners, summary of Object-oriented programming classes & objects
18 Taking care of Missing Data – Step 1
19 Taking care of Missing Data – Step 2
20 Encoding Categorical Data – Step 1
21 Encoding Categorical Data – Step 2
22 Encoding Categorical Data – Step 3
23 Splitting the dataset into the Training set and Test set – Step 1
24 Splitting the dataset into the Training set and Test set – Step 2
25 Splitting the dataset into the Training set and Test set – Step 3
26 Feature Scaling – Step 1
27 Feature Scaling – Step 2
28 Feature Scaling – Step 3
29 Feature Scaling – Step 4

Data Preprocessing in R
30 Getting Started
31 Dataset Description
32 Importing the Dataset
33 Taking care of Missing Data
34 Encoding Categorical Data
35 Splitting the dataset into the Training set and Test set – Step 1
36 Splitting the dataset into the Training set and Test set – Step 2
37 Feature Scaling – Step 1
38 Feature Scaling – Step 2
39 Data Preprocessing Template

Part 2 Regression ——————–
40 Welcome to Part 2 – Regression

Simple Linear Regression
41 Simple Linear Regression Intuition
42 Ordinary Least Squares
43 Simple Linear Regression in Python – Step 1a
44 Simple Linear Regression in Python – Step 1b
45 Simple Linear Regression in Python – Step 2a
46 Simple Linear Regression in Python – Step 2b
47 Simple Linear Regression in Python – Step 3
48 Simple Linear Regression in Python – Step 4a
49 Simple Linear Regression in Python – Step 4b
50 Simple Linear Regression in Python – Additional Lecture
51 Simple Linear Regression in R – Step 1
52 Simple Linear Regression in R – Step 2
53 Simple Linear Regression in R – Step 3
54 Simple Linear Regression in R – Step 4a
55 Simple Linear Regression in R – Step 4b

Multiple Linear Regression
56 Dataset + Business Problem Description
57 Multiple Linear Regression Intuition
58 Assumptions of Linear Regression
59 Multiple Linear Regression Intuition – Step 3
60 Multiple Linear Regression Intuition – Step 4
61 Understanding the P-Value
62 Multiple Linear Regression Intuition – Step 5
63 Multiple Linear Regression in Python – Step 1a
64 Multiple Linear Regression in Python – Step 1b
65 Multiple Linear Regression in Python – Step 2a
66 Multiple Linear Regression in Python – Step 2b
67 Multiple Linear Regression in Python – Step 3a
68 Multiple Linear Regression in Python – Step 3b
69 Multiple Linear Regression in Python – Step 4a
70 Multiple Linear Regression in Python – Step 4b
71 Multiple Linear Regression in Python – Backward Elimination
72 Multiple Linear Regression in Python – EXTRA CONTENT
73 Multiple Linear Regression in R – Step 1a
74 Multiple Linear Regression in R – Step 1b
75 Multiple Linear Regression in R – Step 2a
76 Multiple Linear Regression in R – Step 2b
77 Multiple Linear Regression in R – Step 3
78 Multiple Linear Regression in R – Backward Elimination – HOMEWORK !
79 Multiple Linear Regression in R – Backward Elimination – Homework Solution
80 Multiple Linear Regression in R – Automatic Backward Elimination

Polynomial Regression
81 Polynomial Regression Intuition
82 Polynomial Regression in Python – Step 1a
83 Polynomial Regression in Python – Step 1b
84 Polynomial Regression in Python – Step 2a
85 Polynomial Regression in Python – Step 2b
86 Polynomial Regression in Python – Step 3a
87 Polynomial Regression in Python – Step 3b
88 Polynomial Regression in Python – Step 4a
89 Polynomial Regression in Python – Step 4b
90 Polynomial Regression in R – Step 1a
91 Polynomial Regression in R – Step 1b
92 Polynomial Regression in R – Step 2a
93 Polynomial Regression in R – Step 2b
94 Polynomial Regression in R – Step 3a
95 Polynomial Regression in R – Step 3b
96 Polynomial Regression in R – Step 3c
97 Polynomial Regression in R – Step 4a
98 Polynomial Regression in R – Step 4b
99 R Regression Template – Step 1
100 R Regression Template – Step 2

Support Vector Regression (SVR)
101 SVR Intuition (Updated!)
102 Heads-up on non-linear SVR
103 SVR in Python – Step 1a
104 SVR in Python – Step 1b
105 SVR in Python – Step 2a
106 SVR in Python – Step 2b
107 SVR in Python – Step 2c
108 SVR in Python – Step 3
109 SVR in Python – Step 4
110 SVR in Python – Step 5a
111 SVR in Python – Step 5b
112 SVR in R – Step 1
113 SVR in R – Step 2

Decision Tree Regression
114 Decision Tree Regression Intuition
115 Decision Tree Regression in Python – Step 1a
116 Decision Tree Regression in Python – Step 1b
117 Decision Tree Regression in Python – Step 2
118 Decision Tree Regression in Python – Step 3
119 Decision Tree Regression in Python – Step 4
120 Decision Tree Regression in R – Step 1
121 Decision Tree Regression in R – Step 2
122 Decision Tree Regression in R – Step 3
123 Decision Tree Regression in R – Step 4

Random Forest Regression
124 Random Forest Regression Intuition
125 Random Forest Regression in Python – Step 1
126 Random Forest Regression in Python – Step 2
127 Random Forest Regression in R – Step 1
128 Random Forest Regression in R – Step 2
129 Random Forest Regression in R – Step 3

Evaluating Regression Models Performance
130 R-Squared Intuition
131 Adjusted R-Squared Intuition

Regression Model Selection in Python
132 Make sure you have this Model Selection folder ready
133 Preparation of the Regression Code Templates – Step 1
134 Preparation of the Regression Code Templates – Step 2
135 Preparation of the Regression Code Templates – Step 3
136 Preparation of the Regression Code Templates – Step 4
137 THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! – STEP 1
138 THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! – STEP 2
139 Conclusion of Part 2 – Regression

Regression Model Selection in R
140 Evaluating Regression Models Performance – Homework’s Final Part
141 Interpreting Linear Regression Coefficients
142 Conclusion of Part 2 – Regression

Part 3 Classification ——————–
143 Welcome to Part 3 – Classification

Logistic Regression
144 What is Classification
145 Logistic Regression Intuition
146 Maximum Likelihood
147 Logistic Regression in Python – Step 1a
148 Logistic Regression in Python – Step 1b
149 Logistic Regression in Python – Step 2a
150 Logistic Regression in Python – Step 2b
151 Logistic Regression in Python – Step 3a
152 Logistic Regression in Python – Step 3b
153 Logistic Regression in Python – Step 4a
154 Logistic Regression in Python – Step 4b
155 Logistic Regression in Python – Step 5
156 Logistic Regression in Python – Step 6a
157 Logistic Regression in Python – Step 6b
158 Logistic Regression in Python – Step 7a
159 Logistic Regression in Python – Step 7b
160 Logistic Regression in Python – Step 7c
161 Logistic Regression in Python – Step 7 (Colour-blind friendly image)
162 Logistic Regression in R – Step 1
163 Logistic Regression in R – Step 2
164 Logistic Regression in R – Step 3
165 Logistic Regression in R – Step 4
166 Warning – Update
167 Logistic Regression in R – Step 5a
168 Logistic Regression in R – Step 5b
169 Logistic Regression in R – Step 5c
170 Logistic Regression in R – Step 5 (Colour-blind friendly image)
171 R Classification Template
172 Machine Learning Regression and Classification BONUS
173 EXTRA CONTENT Logistic Regression Practical Case Study

K-Nearest Neighbors (K-NN)
174 K-Nearest Neighbor Intuition
175 K-NN in Python – Step 1
176 K-NN in Python – Step 2
177 K-NN in Python – Step 3
178 K-NN in R – Step 1
179 K-NN in R – Step 2
180 K-NN in R – Step 3

Support Vector Machine (SVM)
181 SVM Intuition
182 SVM in Python – Step 1
183 SVM in Python – Step 2
184 SVM in Python – Step 3
185 SVM in R – Step 1
186 SVM in R – Step 2

Kernel SVM
187 Kernel SVM Intuition
188 Mapping to a higher dimension
189 The Kernel Trick
190 Types of Kernel Functions
191 Non-Linear Kernel SVR (Advanced)
192 Kernel SVM in Python – Step 1
193 Kernel SVM in Python – Step 2
194 Kernel SVM in R – Step 1
195 Kernel SVM in R – Step 2
196 Kernel SVM in R – Step 3

Naive Bayes
197 Bayes Theorem
198 Naive Bayes Intuition
199 Naive Bayes Intuition (Challenge Reveal)
200 Naive Bayes Intuition (Extras)
201 Naive Bayes in Python – Step 1
202 Naive Bayes in Python – Step 2
203 Naive Bayes in Python – Step 3
204 Naive Bayes in R – Step 1
205 Naive Bayes in R – Step 2
206 Naive Bayes in R – Step 3

Decision Tree Classification
207 Decision Tree Classification Intuition
208 Decision Tree Classification in Python – Step 1
209 Decision Tree Classification in Python – Step 2
210 Decision Tree Classification in R – Step 1
211 Decision Tree Classification in R – Step 2
212 Decision Tree Classification in R – Step 3

Random Forest Classification
213 Random Forest Classification Intuition
214 Random Forest Classification in Python – Step 1
215 Random Forest Classification in Python – Step 2
216 Random Forest Classification in R – Step 1
217 Random Forest Classification in R – Step 2
218 Random Forest Classification in R – Step 3

Classification Model Selection in Python
219 Make sure you have this Model Selection folder ready
220 Confusion Matrix & Accuracy Ratios
221 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION – STEP 1
222 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION – STEP 2
223 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION – STEP 3
224 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION – STEP 4

Evaluating Classification Models Performance
225 False Positives & False Negatives
226 Accuracy Paradox
227 CAP Curve
228 CAP Curve Analysis
229 Conclusion of Part 3 – Classification

Part 4 Clustering ——————–
230 Welcome to Part 4 – Clustering

K-Means Clustering
231 What is Clustering (Supervised vs Unsupervised Learning)
232 K-Means Clustering Intuition
233 The Elbow Method
234 K-Means++
235 K-Means Clustering in Python – Step 1a
236 K-Means Clustering in Python – Step 1b
237 K-Means Clustering in Python – Step 2a
238 K-Means Clustering in Python – Step 2b
239 K-Means Clustering in Python – Step 3a
240 K-Means Clustering in Python – Step 3b
241 K-Means Clustering in Python – Step 3c
242 K-Means Clustering in Python – Step 4
243 K-Means Clustering in Python – Step 5a
244 K-Means Clustering in Python – Step 5b
245 K-Means Clustering in Python – Step 5c
246 K-Means Clustering in R – Step 1
247 K-Means Clustering in R – Step 2

Hierarchical Clustering
248 Hierarchical Clustering Intuition
249 Hierarchical Clustering How Dendrograms Work
250 Hierarchical Clustering Using Dendrograms
251 Hierarchical Clustering in Python – Step 1
252 Hierarchical Clustering in Python – Step 2a
253 Hierarchical Clustering in Python – Step 2b
254 Hierarchical Clustering in Python – Step 2c
255 Hierarchical Clustering in Python – Step 3a
256 Hierarchical Clustering in Python – Step 3b
257 Hierarchical Clustering in R – Step 1
258 Hierarchical Clustering in R – Step 2
259 Hierarchical Clustering in R – Step 3
260 Hierarchical Clustering in R – Step 4
261 Hierarchical Clustering in R – Step 5
262 Conclusion of Part 4 – Clustering

Part 5 Association Rule Learning ——————–
263 Welcome to Part 5 – Association Rule Learning

Apriori
264 Apriori Intuition
265 Apriori in Python – Step 1
266 Apriori in Python – Step 2
267 Apriori in Python – Step 3
268 Apriori in Python – Step 4
269 Apriori in R – Step 1
270 Apriori in R – Step 2
271 Apriori in R – Step 3

Eclat
272 Eclat Intuition
273 Eclat in Python
274 Eclat in R

Part 6 Reinforcement Learning ——————–
275 Welcome to Part 6 – Reinforcement Learning

Upper Confidence Bound (UCB)
276 The Multi-Armed Bandit Problem
277 Upper Confidence Bound (UCB) Intuition
278 Upper Confidence Bound in Python – Step 1
279 Upper Confidence Bound in Python – Step 2
280 Upper Confidence Bound in Python – Step 3
281 Upper Confidence Bound in Python – Step 4
282 Upper Confidence Bound in Python – Step 5
283 Upper Confidence Bound in Python – Step 6
284 Upper Confidence Bound in Python – Step 7
285 Upper Confidence Bound in R – Step 1
286 Upper Confidence Bound in R – Step 2
287 Upper Confidence Bound in R – Step 3
288 Upper Confidence Bound in R – Step 4

Thompson Sampling
289 Thompson Sampling Intuition
290 Algorithm Comparison UCB vs Thompson Sampling
291 Thompson Sampling in Python – Step 1
292 Thompson Sampling in Python – Step 2
293 Thompson Sampling in Python – Step 3
294 Thompson Sampling in Python – Step 4
295 Additional Resource for this Section
296 Thompson Sampling in R – Step 1
297 Thompson Sampling in R – Step 2

Part 7 Natural Language Processing ——————–
298 Welcome to Part 7 – Natural Language Processing
299 NLP Intuition
300 Types of Natural Language Processing
301 Classical vs Deep Learning Models
302 Bag-Of-Words Model
303 Natural Language Processing in Python – Step 1
304 Natural Language Processing in Python – Step 2
305 Natural Language Processing in Python – Step 3
306 Natural Language Processing in Python – Step 4
307 Natural Language Processing in Python – Step 5
308 Natural Language Processing in Python – Step 6
309 Natural Language Processing in Python – BONUS
310 Homework Challenge
311 Natural Language Processing in R – Step 1
312 Warning – Update
313 Natural Language Processing in R – Step 2
314 Natural Language Processing in R – Step 3
315 Natural Language Processing in R – Step 4
316 Natural Language Processing in R – Step 5
317 Natural Language Processing in R – Step 6
318 Natural Language Processing in R – Step 7
319 Natural Language Processing in R – Step 8
320 Natural Language Processing in R – Step 9
321 Natural Language Processing in R – Step 10
322 Homework Challenge

Part 8 Deep Learning ——————–
323 Welcome to Part 8 – Deep Learning
324 What is Deep Learning

Artificial Neural Networks
325 Plan of attack
326 The Neuron
327 The Activation Function
328 How do Neural Networks work
329 How do Neural Networks learn
330 Gradient Descent
331 Stochastic Gradient Descent
332 Backpropagation
333 Business Problem Description
334 ANN in Python – Step 1
335 ANN in Python – Step 2
336 ANN in Python – Step 3
337 ANN in Python – Step 4
338 ANN in Python – Step 5
339 ANN in R – Step 1
340 ANN in R – Step 2
341 ANN in R – Step 3
342 ANN in R – Step 4 (Last step)
343 Deep Learning Additional Content
344 EXTRA CONTENT ANN Case Study

Convolutional Neural Networks
345 Plan of attack
346 What are convolutional neural networks
347 Step 1 – Convolution Operation
348 Step 1(b) – ReLU Layer
349 Step 2 – Pooling
350 Step 3 – Flattening
351 Step 4 – Full Connection
352 Summary
353 Softmax & Cross-Entropy
354 CNN in Python – Step 1
355 CNN in Python – Step 2
356 CNN in Python – Step 3
357 CNN in Python – Step 4
358 CNN in Python – Step 5
359 CNN in Python – FINAL DEMO!
360 Deep Learning Additional Content #2

Part 9 Dimensionality Reduction ——————–
361 Welcome to Part 9 – Dimensionality Reduction

Principal Component Analysis (PCA)
362 Principal Component Analysis (PCA) Intuition
363 PCA in Python – Step 1
364 PCA in Python – Step 2
365 PCA in R – Step 1
366 PCA in R – Step 2
367 PCA in R – Step 3

Linear Discriminant Analysis (LDA)
368 Linear Discriminant Analysis (LDA) Intuition
369 LDA in Python
370 LDA in R

Kernel PCA
371 Kernel PCA in Python
372 Kernel PCA in R

Part 10 Model Selection & Boosting ——————–
373 Welcome to Part 10 – Model Selection & Boosting

Model Selection
374 k-Fold Cross Validation in Python
375 Grid Search in Python
376 k-Fold Cross Validation in R
377 Grid Search in R

XGBoost
378 XGBoost in Python
379 Model Selection and Boosting Additional Content
380 XGBoost in R

Exclusive Offer
381 OUR SPECIAL OFFER

Annex Logistic Regression (Long Explanation)
382 Logistic Regression Intuition

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