| در پلیر موجود در این صفحه، می‌توانید ویدئوی مقدمه و آشنایی با آموزش‌های این دوره را با زیرنویس انگلیسی به‌صورت آنلاین تماشا کنید |
Udemy - Machine Learning A-Z™: Hands-On Python & R In Data Science
Last Updated: 2022/2
توجه داشته باشید که این دورهٔ آموزشی ارائه‌شده یکی از برترین، پُرامتیازترین، پُرفروش‌ترین و تأثیرگذارترین دوره‌های کمپانی معتبر Udemy با موضوع «یادگیری ماشین با استفاده از زبان برنامه‌نویسی پایتون و R در علم داده‌ها» و سایر مهارت‌های مربوطه است در سایت Udemy تنها با پرداخت مبلغ بسیار زیادی قابل خریداری خواهد بود.
با استفاده از این دورهٔ آموزشی ویدئویی می‌توانید از صفر تا صدِ مهارت‌ها و تکنیک‌های مربوط به یادگیری ماشین براساس زبان‌های برنامه‌نویسی پایتون و R در علم داده‌ها را با جدیدترین متدها فرا بگیرید. سطح این دورهٴ آموزشی تصویری، از مقدماتی (در حد صفر) به‌سوی سطح پیشرفته است؛ یعنی شما برای استفاده از این دوره، به هیچ‌گونه دانش و مهارت قبلی در هیچ‌کدام از زمینه‌های مربوطه نیازی ندارید.
فایلهای زیرنویس انگلیسی این دورهٔ آموزشی نیز به‌طور کامل به همراه هر ویدئو ارائه شده‌اند و نیازی به دانلود جداگانهٔ آنها نیست.
– تولیدکننده: کمپانی معتبر Udemy
– مدرس/تیم آموزش‌دهنده: Kirill Eremenko, Hadelin de Ponteves, Ligency I Team, SuperDataScience Support, Ligency Team
– تاریخ آخرین آپدیت دوره: ۲۰۲۲/۲ (جدیدترین آپدیت/آپدیت نهایی)
– سطح: از مقدماتی تا پبشرفته
– مدت زمان آموزش: ۴۴ ساعت و ۲۹ دقیقه
– زبان آموزش: انگلیسی
– زیرنویس انگلیسی: دارد
– فرمت فایلهای ویدئویی: MP4
– کیفیت ویدئوها: HD 720p
فهرست سرفصل‌ها و عناوین آموزشی به همراه زمان دقیق آنها (سرفصل‌ها و عناوین اصلی این دوره، بدون بروزرسانی):
Course Content
I 45 Sections | 320 Lectures | 44h 29m Total Length
_____________________________________________
Welcome to the course! | 43:20
Applications of Machine Learning - 03:22
BONUS: Learning Paths - 00:51
BONUS #2 ML vs DL vs AI — What’s the Difference? - 00:13
BONUS #3 Regression Types - 00:12
Why Machine Learning is the Future - 06:37
Important notes, tips & tricks for this course - 02:01
This PDF resource will help you a lot! - 01:04
Updates on Udemy Reviews - 01:09
GET ALL THE CODES AND DATASETS HERE! - 01:07
Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder - 16:48
Installing R and R Studio (Mac, Linux & Windows) - 05:40
BONUS: Meet your instructors - 00:28
Some Additional Resources - 00:10
FAQBot! - 01:29
Your Shortcut To Becoming A Better Data Scientist! - 02:05
-------------------- Part 1: Data Preprocessing --------------------
Welcome to Part 1 - Data Preprocessing - 00:21
Data Preprocessing in Python | 01:32:52
Make sure you have your Machine Learning A-Z folder ready - 00:15
Getting Started - 10:50
Importing the Libraries - 03:34
Importing the Dataset - 15:42
For Python learners, summary of Object-oriented programming: classes & objects - 01:00
Taking care of Missing Data - 12:15
Encoding Categorical Data - 14:58
Splitting the dataset into the Training set and Test set - 13:47
Feature Scaling - 20:31
Data Preprocessing in R | 43:15
Welcome - 00:24
Getting Started - 01:35
Make sure you have your dataset ready - 00:08
Dataset Description - 01:57
Importing the Dataset - 02:44
Taking care of Missing Data - 06:22
Encoding Categorical Data - 06:02
Splitting the dataset into the Training set and Test set - 09:34
Feature Scaling - 09:14
Data Preprocessing Template - 05:15
-------------------- Part 2: Regression --------------------
Welcome to Part 2 - Regression - 00:22
Simple Linear Regression | 01:18:10
Simple Linear Regression Intuition - Step 1 - 05:45
Simple Linear Regression Intuition - Step 2 - 03:09
Make sure you have your Machine Learning A-Z folder ready - 00:20
Simple Linear Regression in Python - Step 1 - 12:48
Simple Linear Regression in Python - Step 2 - 07:56
Simple Linear Regression in Python - Step 3 - 04:35
Simple Linear Regression in Python - Step 4 - 12:56
Simple Linear Regression in Python - BONUS - 00:30
Simple Linear Regression in R - Step 1 - 04:40
Simple Linear Regression in R - Step 2 - 05:58
Simple Linear Regression in R - Step 3 - 03:38
Simple Linear Regression in R - Step 4 - 15:55
Simple Linear Regression - 5 questions
Multiple Linear Regression | 02:14:18
Dataset + Business Problem Description - 03:44
Multiple Linear Regression Intuition - Step 1 - 01:02
Multiple Linear Regression Intuition - Step 2 - 01:00
Multiple Linear Regression Intuition - Step 3 - 07:21
Multiple Linear Regression Intuition - Step 4 - 02:10
Understanding the P-Value - 11:44
Multiple Linear Regression Intuition - Step 5 - 15:41
Make sure you have your Machine Learning A-Z folder ready - 00:20
Multiple Linear Regression in Python - Step 1 - 08:30
Multiple Linear Regression in Python - Step 2 - 09:11
Multiple Linear Regression in Python - Step 3 - 10:37
Multiple Linear Regression in Python - Step 4 - 12:31
Multiple Linear Regression in Python - Backward Elimination - 01:35
Multiple Linear Regression in Python - BONUS - 00:31
Multiple Linear Regression in R - Step 1 - 07:50
Multiple Linear Regression in R - Step 2 - 10:25
Multiple Linear Regression in R - Step 3 - 04:26
Multiple Linear Regression in R - Backward Elimination - HOMEWORK ! - 17:51
Multiple Linear Regression in R - Backward Elimination - Homework Solution - 07:33
Multiple Linear Regression in R - Automatic Backward Elimination - 00:15
Multiple Linear Regression - 5 questions
Polynomial Regression | 01:52:19
Polynomial Regression Intuition - 05:08
Make sure you have your Machine Learning A-Z folder ready - 00:20
Polynomial Regression in Python - Step 1 - 13:30
Polynomial Regression in Python - Step 2 - 11:40
Polynomial Regression in Python - Step 3 - 12:54
Polynomial Regression in Python - Step 4 - 08:10
Polynomial Regression in R - Step 1 - 09:12
Polynomial Regression in R - Step 2 - 09:58
Polynomial Regression in R - Step 3 - 19:54
Polynomial Regression in R - Step 4 - 09:35
R Regression Template - 11:58
Support Vector Regression (SVR) | 01:18:43
SVR Intuition (Updated!) - 08:09
Heads-up on non-linear SVR - 03:57
Make sure you have your Machine Learning A-Z folder ready - 00:20
SVR in Python - Step 1 - 09:15
SVR in Python - Step 2 - 15:10
SVR in Python - Step 3 - 06:27
SVR in Python - Step 4 - 08:01
SVR in Python - Step 5 - 15:40
SVR in R - 11:44
Decision Tree Regression | 58:04
Decision Tree Regression Intuition - 11:06
Make sure you have your Machine Learning A-Z folder ready - 00:20
Decision Tree Regression in Python - Step 1 - 08:38
Decision Tree Regression in Python - Step 2 - 05:00
Decision Tree Regression in Python - Step 3 - 03:16
Decision Tree Regression in Python - Step 4 - 09:50
Decision Tree Regression in R - 19:54
Random Forest Regression | 38:09
Random Forest Regression Intuition - 06:44
Make sure you have your Machine Learning A-Z folder ready - 00:20
Random Forest Regression in Python - 13:23
Random Forest Regression in R - 17:42
Evaluating Regression Models Performance | 15:07
R-Squared Intuition - 05:11
Adjusted R-Squared Intuition - 09:56
Regression Model Selection in Python | 30:03
Make sure you have this Model Selection folder ready - 00:31
Preparation of the Regression Code Templates - 19:26
THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! - 09:03
Conclusion of Part 2 - Regression - 01:03
Regression Model Selection in R | 19:13
Evaluating Regression Models Performance - Homework's Final Part - 08:54
Interpreting Linear Regression Coefficients - 09:16
Conclusion of Part 2 - Regression - 01:03
-------------------- Part 3: Classification --------------------
Welcome to Part 3 - Classification - 00:21
Logistic Regression | 02:09:51
Logistic Regression Intuition - 17:06
Make sure you have your Machine Learning A-Z folder ready - 00:20
Logistic Regression in Python - Step 1 - 09:43
Logistic Regression in Python - Step 2 - 13:38
Logistic Regression in Python - Step 3 - 07:40
Logistic Regression in Python - Step 4 - 07:49
Logistic Regression in Python - Step 5 - 06:15
Logistic Regression in Python - Step 6 - 09:26
Logistic Regression in Python - Step 7 - 16:06
Logistic Regression in R - Step 1 - 05:58
Logistic Regression in R - Step 2 - 02:58
Logistic Regression in R - Step 3 - 05:23
Logistic Regression in R - Step 4 - 02:48
Warning - Update - 00:27
Logistic Regression in R - Step 5 - 19:24
R Classification Template - 04:16
Machine Learning Regression and Classification BONUS - 00:17
Logistic Regression - 5 questions
BONUS: Logistic Regression Practical Case Study - 00:16
K-Nearest Neighbors (K-NN) | 40:56
K-Nearest Neighbor Intuition - 04:52
Make sure you have your Machine Learning A-Z folder ready - 00:20
K-NN in Python - 19:58
K-NN in R - 15:46
Support Vector Machine (SVM) | 37:10
K-Nearest Neighbor - 5 questions
SVM Intuition - 09:49
Make sure you have your Machine Learning A-Z folder ready - 00:20
SVM in Python - 14:52
SVM in R - 12:09
Kernel SVM | 01:08:06
Kernel SVM Intuition - 03:17
Mapping to a higher dimension - 07:50
The Kernel Trick - 12:20
Types of Kernel Functions - 03:47
Non-Linear Kernel SVR (Advanced) - 10:55
Make sure you have your Machine Learning A-Z folder ready - 00:20
Kernel SVM in Python - 13:03
Kernel SVM in R - 16:34
Naive Bayes | 01:19:45
Bayes Theorem - 20:25
Naive Bayes Intuition - 14:03
Naive Bayes Intuition (Challenge Reveal) - 06:04
Naive Bayes Intuition (Extras) - 09:41
Make sure you have your Machine Learning A-Z folder ready - 00:20
Naive Bayes in Python - 14:19
Naive Bayes in R - 14:53
Decision Tree Classification | 42:18
Decision Tree Classification Intuition - 08:08
Make sure you have your Machine Learning A-Z folder ready - 00:20
Decision Tree Classification in Python - 14:03
Decision Tree Classification in R - 19:47
Random Forest Classification | 38:12
Random Forest Classification Intuition - 04:28
Make sure you have your Machine Learning A-Z folder ready - 00:20
Random Forest Classification in Python - 13:28
Random Forest Classification in R - 19:56
Classification Model Selection in Python | 21:31
Make sure you have this Model Selection folder ready - 00:31
THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION! - 21:00
Evaluating Classification Models Performance | 34:50
False Positives & False Negatives - 07:57
Confusion Matrix - 04:57
Accuracy Paradox - 02:12
CAP Curve - 11:16
CAP Curve Analysis - 06:19
Conclusion of Part 3 - Classification - 02:09
-------------------- Part 4: Clustering --------------------
Welcome to Part 4 - Clustering - 00:21
K-Means Clustering | 01:48:21
K-Means Clustering Intuition - 14:17
K-Means Random Initialization Trap - 07:48
K-Means Selecting The Number Of Clusters - 11:51
Make sure you have your Machine Learning A-Z folder ready - 00:20
K-Means Clustering in Python - Step 1 - 08:25
K-Means Clustering in Python - Step 2 - 10:36
K-Means Clustering in Python - Step 3 - 16:58
K-Means Clustering in Python - Step 4 - 06:44
K-Means Clustering in Python - Step 5 - 19:35
K-Means Clustering in R - 11:47
Hierarchical Clustering | 01:23:39
K-Means Clustering - 5 questions
Hierarchical Clustering Intuition - 08:47
Hierarchical Clustering How Dendrograms Work - 08:47
Hierarchical Clustering Using Dendrograms - 11:21
Make sure you have your Machine Learning A-Z folder ready - 00:20
Hierarchical Clustering in Python - Step 1 - 06:56
Hierarchical Clustering in Python - Step 2 - 17:12
Hierarchical Clustering in Python - Step 3 - 12:19
Hierarchical Clustering in R - Step 1 - 03:45
Hierarchical Clustering in R - Step 2 - 05:23
Hierarchical Clustering in R - Step 3 - 03:18
Hierarchical Clustering in R - Step 4 - 02:45
Hierarchical Clustering in R - Step 5 - 02:33
Hierarchical Clustering - 5 questions
Conclusion of Part 4 - Clustering - 00:12
-------------------- Part 5: Association Rule Learning --------------------
Welcome to Part 5 - Association Rule Learning - 00:11
Apriori | 02:10:29
Apriori Intuition - 18:13
Make sure you have your Machine Learning A-Z folder ready - 00:20
Apriori in Python - Step 1 - 08:46
Apriori in Python - Step 2 - 17:07
Apriori in Python - Step 3 - 12:48
Apriori in Python - Step 4 - 19:41
Apriori in R - Step 1 - 19:53
Apriori in R - Step 2 - 14:24
Apriori in R - Step 3 - 19:17
Eclat | 28:34
Eclat Intuition - 06:05
Make sure you have your Machine Learning A-Z folder ready - 00:20
Eclat in Python - 12:00
Eclat in R - 10:09
-------------------- Part 6: Reinforcement Learning --------------------
Welcome to Part 6 - Reinforcement Learning - 00:35
Upper Confidence Bound (UCB) | 02:22:44
The Multi-Armed Bandit Problem - 15:36
Upper Confidence Bound (UCB) Intuition - 14:53
Make sure you have your Machine Learning A-Z folder ready - 00:20
Upper Confidence Bound in Python - Step 1 - 12:42
Upper Confidence Bound in Python - Step 2 - 03:51
Upper Confidence Bound in Python - Step 3 - 07:16
Upper Confidence Bound in Python - Step 4 - 15:45
Upper Confidence Bound in Python - Step 5 - 06:12
Upper Confidence Bound in Python - Step 6 - 07:28
Upper Confidence Bound in Python - Step 7 - 08:09
Upper Confidence Bound in R - Step 1 - 13:39
Upper Confidence Bound in R - Step 2 - 15:58
Upper Confidence Bound in R - Step 3 - 17:37
Upper Confidence Bound in R - Step 4 - 03:18
Thompson Sampling | 01:30:35
Thompson Sampling Intuition - 19:12
Algorithm Comparison: UCB vs Thompson Sampling - 08:12
Make sure you have your Machine Learning A-Z folder ready - 00:20
Thompson Sampling in Python - Step 1 - 05:47
Thompson Sampling in Python - Step 2 - 12:19
Thompson Sampling in Python - Step 3 - 14:03
Thompson Sampling in Python - Step 4 - 07:45
Additional Resource for this Section - 00:28
Thompson Sampling in R - Step 1 - 19:01
Thompson Sampling in R - Step 2 - 03:27
-------------------- Part 7: Natural Language Processing --------------------
Welcome to Part 7 - Natural Language Processing - 01:05
NLP Intuition - 03:02
Types of Natural Language Processing - 04:11
Classical vs Deep Learning Models - 11:22
Bag-Of-Words Model - 17:05
Make sure you have your Machine Learning A-Z folder ready - 00:20
Natural Language Processing in Python - Step 1 - 07:13
Natural Language Processing in Python - Step 2 - 06:45
Natural Language Processing in Python - Step 3 - 12:54
Natural Language Processing in Python - Step 4 - 11:00
Natural Language Processing in Python - Step 5 - 17:24
Natural Language Processing in Python - Step 6 - 09:52
Natural Language Processing in Python - BONUS - 00:23
Homework Challenge - 00:43
Natural Language Processing in R - Step 1 - 16:35
Natural Language Processing in R - Step 2 - 08:39
Natural Language Processing in R - Step 3 - 06:27
Natural Language Processing in R - Step 4 - 02:57
Natural Language Processing in R - Step 5 - 02:05
Natural Language Processing in R - Step 6 - 05:49
Natural Language Processing in R - Step 7 - 03:26
Natural Language Processing in R - Step 8 - 05:20
Natural Language Processing in R - Step 9 - 12:50
Natural Language Processing in R - Step 10 - 17:31
Homework Challenge - 00:47
BONUS: NLP BERT - 00:23
-------------------- Part 8: Deep Learning --------------------
Welcome to Part 8 - Deep Learning - 00:23
What is Deep Learning? - 12:34
Artificial Neural Networks | 03:26:06
Plan of attack - 02:51
The Neuron - 16:24
The Activation Function - 08:29
How do Neural Networks work? - 12:47
How do Neural Networks learn? - 12:58
Gradient Descent - 10:12
Stochastic Gradient Descent - 08:44
Backpropagation - 05:21
Business Problem Description - 04:59
Make sure you have your Machine Learning A-Z folder ready - 00:20
ANN in Python - Step 1 - 10:21
Check out our free course on ANN for Regression - 00:11
ANN in Python - Step 2 - 18:36
ANN in Python - Step 3 - 14:28
ANN in Python - Step 4 - 11:58
ANN in Python - Step 5 - 16:25
ANN in R - Step 1 - 17:17
ANN in R - Step 2 - 06:30
ANN in R - Step 3 - 12:29
ANN in R - Step 4 (Last step) - 14:07
Deep Learning BONUS #1 - 00:24
BONUS: ANN Case Study - 00:14
Convolutional Neural Networks | 03:14:41
Plan of attack - 03:31
What are convolutional neural networks? - 15:49
Step 1 - Convolution Operation - 16:38
Step 1(b) - ReLU Layer - 06:41
Step 2 - Pooling - 14:13
Step 3 - Flattening - 01:52
Step 4 - Full Connection - 19:24
Summary - 04:19
Softmax & Cross-Entropy - 18:20
Make sure you have your dataset ready - 00:21
CNN in Python - Step 1 - 11:35
CNN in Python - Step 2 - 17:46
CNN in Python - Step 3 - 17:56
CNN in Python - Step 4 - 07:21
CNN in Python - Step 5 - 14:55
CNN in Python - FINAL DEMO! - 23:38
Deep Learning BONUS #2 - 00:21
-------------------- Part 9: Dimensionality Reduction --------------------
Welcome to Part 9 - Dimensionality Reduction - 00:33
Principal Component Analysis (PCA) | 01:03:43
Principal Component Analysis (PCA) Intuition - 03:49
Make sure you have your Machine Learning A-Z folder ready - 00:20
PCA in Python - Step 1 - 16:52
PCA in Python - Step 2 - 05:30
PCA in R - Step 1 - 12:08
PCA in R - Step 2 - 11:22
PCA in R - Step 3 - 13:42
Linear Discriminant Analysis (LDA) | 39:01
Linear Discriminant Analysis (LDA) Intuition - 03:50
Make sure you have your Machine Learning A-Z folder ready - 00:20
LDA in Python - 14:52
LDA in R - 19:59
Kernel PCA | 31:53
Make sure you have your Machine Learning A-Z folder ready - 00:20
Kernel PCA in Python - 11:03
Kernel PCA in R - 20:30
-------------------- Part 10: Model Selection & Boosting --------------------
Welcome to Part 10 - Model Selection & Boosting - 00:29
Model Selection | 01:13:39
Make sure you have your Machine Learning A-Z folder ready - 00:20
k-Fold Cross Validation in Python - 17:55
Grid Search in Python - 21:56
k-Fold Cross Validation in R - 19:29
Grid Search in R - 13:59
XGBoost | 36:34
Make sure you have your Machine Learning A-Z folder ready - 00:20
XGBoost in Python - 14:48
Model Selection and Boosting BONUS - 00:32
XGBoost in R - 18:14
THANK YOU bonus video - 02:40
Bonus Lectures | 01:47
YOUR SPECIAL BONUS - 01:47