Machine Learning A-Z™: Python & R In Data Science [2023]

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

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

Requirements

Just some high school mathematics level.

Description

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 PreprocessingPart 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest RegressionPart 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest ClassificationPart 4 - Clustering: K-Means, Hierarchical ClusteringPart 5 - Association Rule Learning: Apriori, EclatPart 6 - Reinforcement Learning: Upper Confidence Bound, Thompson SamplingPart 7 - Natural Language Processing: Bag-of-words model and algorithms for NLPPart 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural NetworksPart 9 - Dimensionality Reduction: PCA, LDA, Kernel PCAPart 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoostEach 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.

Overview

Section 1: Welcome to the course! Here we will help you get started in the best conditions.

Lecture 1 Welcome Challenge!

Lecture 2 Machine Learning Demo - Get Excited!

Lecture 3 Get all the Datasets, Codes and Slides here

Lecture 4 How to use the ML A-Z folder & Google Colab

Lecture 5 Installing R and R Studio (Mac, Linux & Windows)

Lecture 6 Extra Resources

Section 2: –––––––––– Part 1: Data Preprocessing ––––––––––

Lecture 7 Welcome to Part 1 - Data Preprocessing

Lecture 8 The Machine Learning process

Lecture 9 Splitting the data into a Training and Test set

Lecture 10 Feature Scaling

Section 3: Data Preprocessing in Python

Lecture 11 Getting Started - Step 1

Lecture 12 Getting Started - Step 2

Lecture 13 Importing the Libraries

Lecture 14 Importing the Dataset - Step 1

Lecture 15 Importing the Dataset - Step 2

Lecture 16 Importing the Dataset - Step 3

Lecture 17 For Python learners, summary of Object-oriented programming: classes & objects

Lecture 18 Taking care of Missing Data - Step 1

Lecture 19 Taking care of Missing Data - Step 2

Lecture 20 Encoding Categorical Data - Step 1

Lecture 21 Encoding Categorical Data - Step 2

Lecture 22 Encoding Categorical Data - Step 3

Lecture 23 Splitting the dataset into the Training set and Test set - Step 1

Lecture 24 Splitting the dataset into the Training set and Test set - Step 2

Lecture 25 Splitting the dataset into the Training set and Test set - Step 3

Lecture 26 Feature Scaling - Step 1

Lecture 27 Feature Scaling - Step 2

Lecture 28 Feature Scaling - Step 3

Lecture 29 Feature Scaling - Step 4

Section 4: Data Preprocessing in R

Lecture 30 Getting Started

Lecture 31 Dataset Description

Lecture 32 Importing the Dataset

Lecture 33 Taking care of Missing Data

Lecture 34 Encoding Categorical Data

Lecture 35 Splitting the dataset into the Training set and Test set - Step 1

Lecture 36 Splitting the dataset into the Training set and Test set - Step 2

Lecture 37 Feature Scaling - Step 1

Lecture 38 Feature Scaling - Step 2

Lecture 39 Data Preprocessing Template

Section 5: –––––––––– Part 2: Regression ––––––––––

Lecture 40 Welcome to Part 2 - Regression

Section 6: Simple Linear Regression

Lecture 41 Simple Linear Regression Intuition

Lecture 42 Ordinary Least Squares

Lecture 43 Simple Linear Regression in Python - Step 1a

Lecture 44 Simple Linear Regression in Python - Step 1b

Lecture 45 Simple Linear Regression in Python - Step 2a

Lecture 46 Simple Linear Regression in Python - Step 2b

Lecture 47 Simple Linear Regression in Python - Step 3

Lecture 48 Simple Linear Regression in Python - Step 4a

Lecture 49 Simple Linear Regression in Python - Step 4b

Lecture 50 Simple Linear Regression in Python - Additional Lecture

Lecture 51 Simple Linear Regression in R - Step 1

Lecture 52 Simple Linear Regression in R - Step 2

Lecture 53 Simple Linear Regression in R - Step 3

Lecture 54 Simple Linear Regression in R - Step 4a

Lecture 55 Simple Linear Regression in R - Step 4b

Section 7: Multiple Linear Regression

Lecture 56 Dataset + Business Problem Description

Lecture 57 Multiple Linear Regression Intuition

Lecture 58 Assumptions of Linear Regression

Lecture 59 Multiple Linear Regression Intuition - Step 3

Lecture 60 Multiple Linear Regression Intuition - Step 4

Lecture 61 Understanding the P-Value

Lecture 62 Multiple Linear Regression Intuition - Step 5

Lecture 63 Multiple Linear Regression in Python - Step 1a

Lecture 64 Multiple Linear Regression in Python - Step 1b

Lecture 65 Multiple Linear Regression in Python - Step 2a

Lecture 66 Multiple Linear Regression in Python - Step 2b

Lecture 67 Multiple Linear Regression in Python - Step 3a

Lecture 68 Multiple Linear Regression in Python - Step 3b

Lecture 69 Multiple Linear Regression in Python - Step 4a

Lecture 70 Multiple Linear Regression in Python - Step 4b

Lecture 71 Multiple Linear Regression in Python - Backward Elimination

Lecture 72 Multiple Linear Regression in Python - EXTRA CONTENT

Lecture 73 Multiple Linear Regression in R - Step 1a

Lecture 74 Multiple Linear Regression in R - Step 1b

Lecture 75 Multiple Linear Regression in R - Step 2a

Lecture 76 Multiple Linear Regression in R - Step 2b

Lecture 77 Multiple Linear Regression in R - Step 3

Lecture 78 Multiple Linear Regression in R - Backward Elimination - HOMEWORK !

Lecture 79 Multiple Linear Regression in R - Backward Elimination - Homework Solution

Lecture 80 Multiple Linear Regression in R - Automatic Backward Elimination

Section 8: Polynomial Regression

Lecture 81 Polynomial Regression Intuition

Lecture 82 Polynomial Regression in Python - Step 1a

Lecture 83 Polynomial Regression in Python - Step 1b

Lecture 84 Polynomial Regression in Python - Step 2a

Lecture 85 Polynomial Regression in Python - Step 2b

Lecture 86 Polynomial Regression in Python - Step 3a

Lecture 87 Polynomial Regression in Python - Step 3b

Lecture 88 Polynomial Regression in Python - Step 4a

Lecture 89 Polynomial Regression in Python - Step 4b

Lecture 90 Polynomial Regression in R - Step 1a

Lecture 91 Polynomial Regression in R - Step 1b

Lecture 92 Polynomial Regression in R - Step 2a

Lecture 93 Polynomial Regression in R - Step 2b

Lecture 94 Polynomial Regression in R - Step 3a

Lecture 95 Polynomial Regression in R - Step 3b

Lecture 96 Polynomial Regression in R - Step 3c

Lecture 97 Polynomial Regression in R - Step 4a

Lecture 98 Polynomial Regression in R - Step 4b

Lecture 99 R Regression Template - Step 1

Lecture 100 R Regression Template - Step 2

Section 9: Support Vector Regression (SVR)

Lecture 101 SVR Intuition (Updated!)

Lecture 102 Heads-up on non-linear SVR

Lecture 103 SVR in Python - Step 1a

Lecture 104 SVR in Python - Step 1b

Lecture 105 SVR in Python - Step 2a

Lecture 106 SVR in Python - Step 2b

Lecture 107 SVR in Python - Step 2c

Lecture 108 SVR in Python - Step 3

Lecture 109 SVR in Python - Step 4

Lecture 110 SVR in Python - Step 5a

Lecture 111 SVR in Python - Step 5b

Lecture 112 SVR in R - Step 1

Lecture 113 SVR in R - Step 2

Section 10: Decision Tree Regression

Lecture 114 Decision Tree Regression Intuition

Lecture 115 Decision Tree Regression in Python - Step 1a

Lecture 116 Decision Tree Regression in Python - Step 1b

Lecture 117 Decision Tree Regression in Python - Step 2

Lecture 118 Decision Tree Regression in Python - Step 3

Lecture 119 Decision Tree Regression in Python - Step 4

Lecture 120 Decision Tree Regression in R - Step 1

Lecture 121 Decision Tree Regression in R - Step 2

Lecture 122 Decision Tree Regression in R - Step 3

Lecture 123 Decision Tree Regression in R - Step 4

Section 11: Random Forest Regression

Lecture 124 Random Forest Regression Intuition

Lecture 125 Random Forest Regression in Python - Step 1

Lecture 126 Random Forest Regression in Python - Step 2

Lecture 127 Random Forest Regression in R - Step 1

Lecture 128 Random Forest Regression in R - Step 2

Lecture 129 Random Forest Regression in R - Step 3

Section 12: Evaluating Regression Models Performance

Lecture 130 R-Squared Intuition

Lecture 131 Adjusted R-Squared Intuition

Section 13: Regression Model Selection in Python

Lecture 132 Make sure you have this Model Selection folder ready

Lecture 133 Preparation of the Regression Code Templates - Step 1

Lecture 134 Preparation of the Regression Code Templates - Step 2

Lecture 135 Preparation of the Regression Code Templates - Step 3

Lecture 136 Preparation of the Regression Code Templates - Step 4

Lecture 137 THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! - STEP 1

Lecture 138 THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! - STEP 2

Lecture 139 Conclusion of Part 2 - Regression

Section 14: Regression Model Selection in R

Lecture 140 Evaluating Regression Models Performance - Homework's Final Part

Lecture 141 Interpreting Linear Regression Coefficients

Lecture 142 Conclusion of Part 2 - Regression

Section 15: –––––––––– Part 3: Classification ––––––––––

Lecture 143 Welcome to Part 3 - Classification

Section 16: Logistic Regression

Lecture 144 What is Classification?

Lecture 145 Logistic Regression Intuition

Lecture 146 Maximum Likelihood

Lecture 147 Logistic Regression in Python - Step 1a

Lecture 148 Logistic Regression in Python - Step 1b

Lecture 149 Logistic Regression in Python - Step 2a

Lecture 150 Logistic Regression in Python - Step 2b

Lecture 151 Logistic Regression in Python - Step 3a

Lecture 152 Logistic Regression in Python - Step 3b

Lecture 153 Logistic Regression in Python - Step 4a

Lecture 154 Logistic Regression in Python - Step 4b

Lecture 155 Logistic Regression in Python - Step 5

Lecture 156 Logistic Regression in Python - Step 6a

Lecture 157 Logistic Regression in Python - Step 6b

Lecture 158 Logistic Regression in Python - Step 7a

Lecture 159 Logistic Regression in Python - Step 7b

Lecture 160 Logistic Regression in Python - Step 7c

Lecture 161 Logistic Regression in Python - Step 7 (Colour-blind friendly image)

Lecture 162 Logistic Regression in R - Step 1

Lecture 163 Logistic Regression in R - Step 2

Lecture 164 Logistic Regression in R - Step 3

Lecture 165 Logistic Regression in R - Step 4

Lecture 166 Warning - Update

Lecture 167 Logistic Regression in R - Step 5a

Lecture 168 Logistic Regression in R - Step 5b

Lecture 169 Logistic Regression in R - Step 5c

Lecture 170 Logistic Regression in R - Step 5 (Colour-blind friendly image)

Lecture 171 R Classification Template

Lecture 172 Machine Learning Regression and Classification BONUS

Lecture 173 EXTRA CONTENT: Logistic Regression Practical Case Study

Section 17: K-Nearest Neighbors (K-NN)

Lecture 174 K-Nearest Neighbor Intuition

Lecture 175 K-NN in Python - Step 1

Lecture 176 K-NN in Python - Step 2

Lecture 177 K-NN in Python - Step 3

Lecture 178 K-NN in R - Step 1

Lecture 179 K-NN in R - Step 2

Lecture 180 K-NN in R - Step 3

Section 18: Support Vector Machine (SVM)

Lecture 181 SVM Intuition

Lecture 182 SVM in Python - Step 1

Lecture 183 SVM in Python - Step 2

Lecture 184 SVM in Python - Step 3

Lecture 185 SVM in R - Step 1

Lecture 186 SVM in R - Step 2

Section 19: Kernel SVM

Lecture 187 Kernel SVM Intuition

Lecture 188 Mapping to a higher dimension

Lecture 189 The Kernel Trick

Lecture 190 Types of Kernel Functions

Lecture 191 Non-Linear Kernel SVR (Advanced)

Lecture 192 Kernel SVM in Python - Step 1

Lecture 193 Kernel SVM in Python - Step 2

Lecture 194 Kernel SVM in R - Step 1

Lecture 195 Kernel SVM in R - Step 2

Lecture 196 Kernel SVM in R - Step 3

Section 20: Naive Bayes

Lecture 197 Bayes Theorem

Lecture 198 Naive Bayes Intuition

Lecture 199 Naive Bayes Intuition (Challenge Reveal)

Lecture 200 Naive Bayes Intuition (Extras)

Lecture 201 Naive Bayes in Python - Step 1

Lecture 202 Naive Bayes in Python - Step 2

Lecture 203 Naive Bayes in Python - Step 3

Lecture 204 Naive Bayes in R - Step 1

Lecture 205 Naive Bayes in R - Step 2

Lecture 206 Naive Bayes in R - Step 3

Section 21: Decision Tree Classification

Lecture 207 Decision Tree Classification Intuition

Lecture 208 Decision Tree Classification in Python - Step 1

Lecture 209 Decision Tree Classification in Python - Step 2

Lecture 210 Decision Tree Classification in R - Step 1

Lecture 211 Decision Tree Classification in R - Step 2

Lecture 212 Decision Tree Classification in R - Step 3

Section 22: Random Forest Classification

Lecture 213 Random Forest Classification Intuition

Lecture 214 Random Forest Classification in Python - Step 1

Lecture 215 Random Forest Classification in Python - Step 2

Lecture 216 Random Forest Classification in R - Step 1

Lecture 217 Random Forest Classification in R - Step 2

Lecture 218 Random Forest Classification in R - Step 3

Section 23: Classification Model Selection in Python

Lecture 219 Make sure you have this Model Selection folder ready

Lecture 220 Confusion Matrix & Accuracy Ratios

Lecture 221 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 1

Lecture 222 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 2

Lecture 223 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 3

Lecture 224 ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION - STEP 4

Section 24: Evaluating Classification Models Performance

Lecture 225 False Positives & False Negatives

Lecture 226 Accuracy Paradox

Lecture 227 CAP Curve

Lecture 228 CAP Curve Analysis

Lecture 229 Conclusion of Part 3 - Classification

Section 25: –––––––––– Part 4: Clustering ––––––––––

Lecture 230 Welcome to Part 4 - Clustering

Section 26: K-Means Clustering

Lecture 231 What is Clustering? (Supervised vs Unsupervised Learning)

Lecture 232 K-Means Clustering Intuition

Lecture 233 The Elbow Method

Lecture 234 K-Means++

Lecture 235 K-Means Clustering in Python - Step 1a

Lecture 236 K-Means Clustering in Python - Step 1b

Lecture 237 K-Means Clustering in Python - Step 2a

Lecture 238 K-Means Clustering in Python - Step 2b

Lecture 239 K-Means Clustering in Python - Step 3a

Lecture 240 K-Means Clustering in Python - Step 3b

Lecture 241 K-Means Clustering in Python - Step 3c

Lecture 242 K-Means Clustering in Python - Step 4

Lecture 243 K-Means Clustering in Python - Step 5a

Lecture 244 K-Means Clustering in Python - Step 5b

Lecture 245 K-Means Clustering in Python - Step 5c

Lecture 246 K-Means Clustering in R - Step 1

Lecture 247 K-Means Clustering in R - Step 2

Section 27: Hierarchical Clustering

Lecture 248 Hierarchical Clustering Intuition

Lecture 249 Hierarchical Clustering How Dendrograms Work

Lecture 250 Hierarchical Clustering Using Dendrograms

Lecture 251 Hierarchical Clustering in Python - Step 1

Lecture 252 Hierarchical Clustering in Python - Step 2a

Lecture 253 Hierarchical Clustering in Python - Step 2b

Lecture 254 Hierarchical Clustering in Python - Step 2c

Lecture 255 Hierarchical Clustering in Python - Step 3a

Lecture 256 Hierarchical Clustering in Python - Step 3b

Lecture 257 Hierarchical Clustering in R - Step 1

Lecture 258 Hierarchical Clustering in R - Step 2

Lecture 259 Hierarchical Clustering in R - Step 3

Lecture 260 Hierarchical Clustering in R - Step 4

Lecture 261 Hierarchical Clustering in R - Step 5

Lecture 262 Conclusion of Part 4 - Clustering

Section 28: –––––––––– Part 5: Association Rule Learning ––––––––––

Lecture 263 Welcome to Part 5 - Association Rule Learning

Section 29: Apriori

Lecture 264 Apriori Intuition

Lecture 265 Apriori in Python - Step 1

Lecture 266 Apriori in Python - Step 2

Lecture 267 Apriori in Python - Step 3

Lecture 268 Apriori in Python - Step 4

Lecture 269 Apriori in R - Step 1

Lecture 270 Apriori in R - Step 2

Lecture 271 Apriori in R - Step 3

Section 30: Eclat

Lecture 272 Eclat Intuition

Lecture 273 Eclat in Python

Lecture 274 Eclat in R

Section 31: –––––––––– Part 6: Reinforcement Learning ––––––––––

Lecture 275 Welcome to Part 6 - Reinforcement Learning

Section 32: Upper Confidence Bound (UCB)

Lecture 276 The Multi-Armed Bandit Problem

Lecture 277 Upper Confidence Bound (UCB) Intuition

Lecture 278 Upper Confidence Bound in Python - Step 1

Lecture 279 Upper Confidence Bound in Python - Step 2

Lecture 280 Upper Confidence Bound in Python - Step 3

Lecture 281 Upper Confidence Bound in Python - Step 4

Lecture 282 Upper Confidence Bound in Python - Step 5

Lecture 283 Upper Confidence Bound in Python - Step 6

Lecture 284 Upper Confidence Bound in Python - Step 7

Lecture 285 Upper Confidence Bound in R - Step 1

Lecture 286 Upper Confidence Bound in R - Step 2

Lecture 287 Upper Confidence Bound in R - Step 3

Lecture 288 Upper Confidence Bound in R - Step 4

Section 33: Thompson Sampling

Lecture 289 Thompson Sampling Intuition

Lecture 290 Algorithm Comparison: UCB vs Thompson Sampling

Lecture 291 Thompson Sampling in Python - Step 1

Lecture 292 Thompson Sampling in Python - Step 2

Lecture 293 Thompson Sampling in Python - Step 3

Lecture 294 Thompson Sampling in Python - Step 4

Lecture 295 Additional Resource for this Section

Lecture 296 Thompson Sampling in R - Step 1

Lecture 297 Thompson Sampling in R - Step 2

Section 34: –––––––––– Part 7: Natural Language Processing ––––––––––

Lecture 298 Welcome to Part 7 - Natural Language Processing

Lecture 299 NLP Intuition

Lecture 300 Types of Natural Language Processing

Lecture 301 Classical vs Deep Learning Models

Lecture 302 Bag-Of-Words Model

Lecture 303 Natural Language Processing in Python - Step 1

Lecture 304 Natural Language Processing in Python - Step 2

Lecture 305 Natural Language Processing in Python - Step 3

Lecture 306 Natural Language Processing in Python - Step 4

Lecture 307 Natural Language Processing in Python - Step 5

Lecture 308 Natural Language Processing in Python - Step 6

Lecture 309 Natural Language Processing in Python - BONUS

Lecture 310 Homework Challenge

Lecture 311 Natural Language Processing in R - Step 1

Lecture 312 Warning - Update

Lecture 313 Natural Language Processing in R - Step 2

Lecture 314 Natural Language Processing in R - Step 3

Lecture 315 Natural Language Processing in R - Step 4

Lecture 316 Natural Language Processing in R - Step 5

Lecture 317 Natural Language Processing in R - Step 6

Lecture 318 Natural Language Processing in R - Step 7

Lecture 319 Natural Language Processing in R - Step 8

Lecture 320 Natural Language Processing in R - Step 9

Lecture 321 Natural Language Processing in R - Step 10

Lecture 322 Homework Challenge

Section 35: –––––––––– Part 8: Deep Learning ––––––––––

Lecture 323 Welcome to Part 8 - Deep Learning

Lecture 324 What is Deep Learning?

Section 36: Artificial Neural Networks

Lecture 325 Plan of attack

Lecture 326 The Neuron

Lecture 327 The Activation Function

Lecture 328 How do Neural Networks work?

Lecture 329 How do Neural Networks learn?

Lecture 330 Gradient Descent

Lecture 331 Stochastic Gradient Descent

Lecture 332 Backpropagation

Lecture 333 Business Problem Description

Lecture 334 ANN in Python - Step 1

Lecture 335 ANN in Python - Step 2

Lecture 336 ANN in Python - Step 3

Lecture 337 ANN in Python - Step 4

Lecture 338 ANN in Python - Step 5

Lecture 339 ANN in R - Step 1

Lecture 340 ANN in R - Step 2

Lecture 341 ANN in R - Step 3

Lecture 342 ANN in R - Step 4 (Last step)

Lecture 343 Deep Learning Additional Content

Lecture 344 EXTRA CONTENT: ANN Case Study

Section 37: Convolutional Neural Networks

Lecture 345 Plan of attack

Lecture 346 What are convolutional neural networks?

Lecture 347 Step 1 - Convolution Operation

Lecture 348 Step 1(b) - ReLU Layer

Lecture 349 Step 2 - Pooling

Lecture 350 Step 3 - Flattening

Lecture 351 Step 4 - Full Connection

Lecture 352 Summary

Lecture 353 Softmax & Cross-Entropy

Lecture 354 CNN in Python - Step 1

Lecture 355 CNN in Python - Step 2

Lecture 356 CNN in Python - Step 3

Lecture 357 CNN in Python - Step 4

Lecture 358 CNN in Python - Step 5

Lecture 359 CNN in Python - FINAL DEMO!

Lecture 360 Deep Learning Additional Content #2

Section 38: –––––––––– Part 9: Dimensionality Reduction ––––––––––

Lecture 361 Welcome to Part 9 - Dimensionality Reduction

Section 39: Principal Component Analysis (PCA)

Lecture 362 Principal Component Analysis (PCA) Intuition

Lecture 363 PCA in Python - Step 1

Lecture 364 PCA in Python - Step 2

Lecture 365 PCA in R - Step 1

Lecture 366 PCA in R - Step 2

Lecture 367 PCA in R - Step 3

Section 40: Linear Discriminant Analysis (LDA)

Lecture 368 Linear Discriminant Analysis (LDA) Intuition

Lecture 369 LDA in Python

Lecture 370 LDA in R

Section 41: Kernel PCA

Lecture 371 Kernel PCA in Python

Lecture 372 Kernel PCA in R

Section 42: –––––––––– Part 10: Model Selection & Boosting ––––––––––

Lecture 373 Welcome to Part 10 - Model Selection & Boosting

Section 43: Model Selection

Lecture 374 k-Fold Cross Validation in Python

Lecture 375 Grid Search in Python

Lecture 376 k-Fold Cross Validation in R

Lecture 377 Grid Search in R

Section 44: XGBoost

Lecture 378 XGBoost in Python

Lecture 379 Model Selection and Boosting Additional Content

Lecture 380 XGBoost in R

Section 45: Exclusive Offer

Lecture 381 ***OUR SPECIAL OFFER***

Section 46: Annex: Logistic Regression (Long Explanation)

Lecture 382 Logistic Regression Intuition

Anyone interested in Machine Learning.,Students who have at least high school knowledge in math and who want to start learning Machine Learning.,Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.,Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.,Any students in college who want to start a career in Data Science.,Any data analysts who want to level up in Machine Learning.,Any people who are not satisfied with their job and who want to become a Data Scientist.,Any people who want to create added value to their business by using powerful Machine Learning tools.