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+--- Thread: Machine Learning A-Z: AI, Python & R + ChatGPT Prize (/Thread-Machine-Learning-A-Z-AI-Python-R-ChatGPT-Prize)
Machine Learning A-Z: AI, Python & R + ChatGPT Prize - Farid - 02-16-2025
![[Image: a9342f254ba770003e3b9888aa0e4e88.jpg]](https://i124.fastpic.org/big/2025/0212/88/a9342f254ba770003e3b9888aa0e4e88.jpg)
Year of release : 2025
Manufacturer : UDEMY
Manufacturer's site : Author : Kirill Eremenko Hadelin de Ponteves
Duration : 42 hours 30m
Type of material given : Video lesson
Language : En
Description: Authors
Kirill Eremenko Hadelin de Ponteves SuperDataScience Team Ligency Team
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Course in En. Added En subtitles using Speech to Text for Adobe Premier Pro.
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.
Content
01 - Welcome to the course! Here we will help you get started in the best conditions
02 - -------------------- Part 1 Data Preprocessing --------------------
03 - Data Preprocessing in Python
04 - Data Preprocessing in R
05 - -------------------- Part 2 Regression --------------------
06 - Simple Linear Regression
07 - Multiple Linear Regression
08 - Polynomial Regression
09 - Support Vector Regression (SVR)
10 - Decision Tree Regression
11 - Random Forest Regression
12 - Evaluating Regression Models Performance
13 - Regression Model Selection in Python
14 - Regression Model Selection in R
15 - -------------------- Part 3 Classification --------------------
16 - Logistic Regression
17 - K-Nearest Neighbors (K-NN)
18 - Support Vector Machine (SVM)
19 - Kernel SVM
20 - Naive Bayes
21 - Decision Tree Classification
22 - Random Forest Classification
23 - Classification Model Selection in Python
24 - Evaluating Classification Models Performance
25 - -------------------- Part 4 Clustering --------------------
26 - K-Means Clustering
27 - Hierarchical Clustering
28 - -------------------- Part 5 Association Rule Learning --------------------
29 - Apriori
30 - Eclat
31 - -------------------- Part 6 Reinforcement Learning --------------------
32 - Upper Confidence Bound (UCB)
33 - Thompson Sampling
34 - -------------------- Part 7 Natural Language Processing --------------------
35 - -------------------- Part 8 Deep Learning --------------------
36 - Artificial Neural Networks
37 - Convolutional Neural Networks
38 - -------------------- Part 9 Dimensionality Reduction --------------------
39 - Principal Component Analysis (PCA)
40 - Linear Discriminant Analysis (LDA)
41 - kernel pca
42 - -------------------- Part 10 Model Selection & Boosting --------------------
43 - Model Selection
44 - XGBoost
45 - Annex Logistic Regression (Long Explanation)
46 - Congratulations!! Don't forget your Prize )
Example files : present
Format Video : mp4
Video : H265 1920x1080 16: 9 30k / SEK 300 kbit / sec
Audio : AAC 48 kHz 128 kbps 2 channels
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📌 Machine Learning A-Z AI, Python & R + ChatGPT Prize (8.09 GB)
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