Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]

August 3, 2024 Courses
Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]

English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 386 lectures (42h 48m) | 16.43 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 1 Million 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 last but not least, 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 AZ folder Google Colab
5 Installing R and R Studio Mac Linux Windows
6 EXTRA 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 Objectoriented 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
56 Simple Linear Regression in R Step 4c

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

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

Support Vector Regression SVR
102 SVR Intuition Updated
103 Headsup on nonlinear SVR
104 SVR in Python Step 1a
105 SVR in Python Step 1b
106 SVR in Python Step 2a
107 SVR in Python Step 2b
108 SVR in Python Step 2c
109 SVR in Python Step 3
110 SVR in Python Step 4
111 SVR in Python Step 5a
112 SVR in Python Step 5b
113 SVR in R Step 1
114 SVR in R Step 2

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

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

Evaluating Regression Models Performance
131 RSquared Intuition
132 Adjusted RSquared Intuition

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

Regression Model Selection in R
141 Evaluating Regression Models Performance Homeworks Final Part
142 Interpreting Linear Regression Coefficients
143 Conclusion of Part 2 Regression

Part 3 Classification
144 Welcome to Part 3 Classification
145 What is Classification

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

KNearest Neighbors KNN
175 KNearest Neighbor Intuition
176 KNN in Python Step 1
177 KNN in Python Step 2
178 KNN in Python Step 3
179 KNN in R Step 1
180 KNN in R Step 2
181 KNN in R Step 3

Support Vector Machine SVM
182 SVM Intuition
183 SVM in Python Step 1
184 SVM in Python Step 2
185 SVM in Python Step 3
186 SVM in R Step 1
187 SVM in R Step 2

Kernel SVM
188 Kernel SVM Intuition
189 Mapping to a higher dimension
190 The Kernel Trick
191 Types of Kernel Functions
192 NonLinear Kernel SVR Advanced
193 Kernel SVM in Python Step 1
194 Kernel SVM in Python Step 2
195 Kernel SVM in R Step 1
196 Kernel SVM in R Step 2
197 Kernel SVM in R Step 3

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

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

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

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

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

Part 4 Clustering
231 Welcome to Part 4 Clustering

KMeans Clustering
232 What is Clustering Supervised vs Unsupervised Learning
233 KMeans Clustering Intuition
234 The Elbow Method
235 KMeans
236 KMeans Clustering in Python Step 1a
237 KMeans Clustering in Python Step 1b
238 KMeans Clustering in Python Step 2a
239 KMeans Clustering in Python Step 2b
240 KMeans Clustering in Python Step 3a
241 KMeans Clustering in Python Step 3b
242 KMeans Clustering in Python Step 3c
243 KMeans Clustering in Python Step 4
244 KMeans Clustering in Python Step 5a
245 KMeans Clustering in Python Step 5b
246 KMeans Clustering in Python Step 5c
247 KMeans Clustering in R Step 1
248 KMeans Clustering in R Step 2

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

Part 5 Association Rule Learning
264 Welcome to Part 5 Association Rule Learning

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

Eclat
273 Eclat Intuition
274 Eclat in Python
275 Eclat in R

Part 6 Reinforcement Learning
276 Welcome to Part 6 Reinforcement Learning

Upper Confidence Bound UCB
277 The MultiArmed Bandit Problem
278 Upper Confidence Bound UCB Intuition
279 Upper Confidence Bound in Python Step 1
280 Upper Confidence Bound in Python Step 2
281 Upper Confidence Bound in Python Step 3
282 Upper Confidence Bound in Python Step 4
283 Upper Confidence Bound in Python Step 5
284 Upper Confidence Bound in Python Step 6
285 Upper Confidence Bound in Python Step 7
286 Upper Confidence Bound in R Step 1
287 Upper Confidence Bound in R Step 2
288 Upper Confidence Bound in R Step 3
289 Upper Confidence Bound in R Step 4

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

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

Part 8 Deep Learning
324 Welcome to Part 8 Deep Learning
325 What is Deep Learning

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

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

Part 9 Dimensionality Reduction
362 Welcome to Part 9 Dimensionality Reduction

Principal Component Analysis PCA
363 Principal Component Analysis PCA Intuition
364 PCA in Python Step 1
365 PCA in Python Step 2
366 PCA in R Step 1
367 PCA in R Step 2
368 PCA in R Step 3

Linear Discriminant Analysis LDA
369 Linear Discriminant Analysis LDA Intuition
370 LDA in Python
371 LDA in R

Kernel PCA
372 Kernel PCA in Python
373 Kernel PCA in R

Part 10 Model Selection Boosting
374 Welcome to Part 10 Model Selection Boosting

Model Selection
375 kFold CrossValidation Intuition
376 BiasVariance Tradeoff
377 kFold Cross Validation in Python
378 Grid Search in Python
379 kFold Cross Validation in R
380 Grid Search in R

XGBoost
381 XGBoost in Python
382 Model Selection and Boosting Additional Content
383 XGBoost in R

Annex Logistic Regression Long Explanation
384 Logistic Regression Intuition

Congratulations Dont forget your Prize
385 Huge Congrats for completing the challenge
386 Bonus How To UNLOCK Top Salaries Live Training

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