Machine Learning A-Z: Hands-On Python & R In Data Science is the name of a set of video tutorials on machine learning from a basic to advanced level. The course you are preparing for the Machine Learning Learning enthusiast, and you can become a specialist in the field of machine learning by seeing this course and having only the average knowledge of mathematical science. In this tutorial, you will learn how to use machine learning algorithms in Python and R programming languages.
You will also learn about this course by incorporating concepts of accurate predictions, powerful analysis, smart modeling, and strong value creation for your business and other concepts. You will also learn new and highly functional skills by viewing each session of this video course.
Features of the Machine Learning A-Z: Hands-On Python & R In Data Science
Dear students, you will become a full-fledged expert by observing and learning the concepts and techniques of this course from a person in Machine Learning. It should be noted that you will see 289 video tutorial sessions for your students to complete the course.
Course specifications:
- Publisher: Udemy
- Teacher: Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, SuperDataScience Support
- Level: Advanced to advanced
- Duration: 41:13:58
- Number of lessons: 289 lessons
- English language
- Machine Learning A-Z Headings: Hands-On Python & R In Data Science
- Course content: 289 lectures 41:13:58
Contents
- Welcome to the course!: 9 lectures 29:40
------- Part 1: Data Preprocessing ------
11 lectures 01:43:18
------- Part 2: Regression ------
- 1 lecture 00:22
- Simple Linear Regression: 12 lectures 01:25:06
- Multiple Linear Regression: 21 lectures 02:20:00
- Polynomial Regression: 12 lectures 02:09:06
- Support Vector Regression (SVR): 4 lectures 43:28
- Decision Tree Regression: 4 lectures 49:03
- Random Forest Regression: 4 lectures 44:28
- Evaluating Regression Models Performance: 5 lectures 35:04
------- Part 3: Classification ------
- 1 lecture 00:21
- Logistic Regression: 14 lectures 01:41:02
- K-Nearest Neighbors (K-NN): 4 lectures 38:06
- Support Vector Machine (SVM): 4 lectures 37:40
- Kernel SVM: 7 lectures 01:04:58
- Naive Bayes: 7 lectures 01:17:38
- Decision Tree Classification: 4 lectures 43:47
- Random Forest Classification: 4 lectures 47:36
- Evaluating Classification Models Performance: 6 lectures 34:52
------- Part 4: Clustering ------
- 1 lecture 00:21
- K-Means Clustering: 6 lectures 01:06:56
- Hierarchical Clustering: 15 lectures 01:15:43
------- Part 5: Association Rule Learning ------
- 1 lecture 00:11
- Apriori: 8 lectures 01:59:47
- Eclat: 3 lectures 19:32
------- Part 6: Reinforcement Learning ------
- 1 lecture 00:26
- Upper Confidence Bound (UCB): 11 lectures 02:19:49
- Thompson Sampling: 7 lectures 01:16:38
------- Part 7: Natural Language Processing ------
25 lectures 03:01:03
------- Part 8: Deep Learning ------
- 2 lectures 12:58
- Artificial Neural Networks: 25 lectures 03:32:41
- Convolutional Neural Networks: 22 lectures 03:02:13
------- Part 9: Dimensionality Reduction ------
- 1 lecture 00:35
- Principal Component Analysis (PCA): 8 lectures 01:13:56
- Linear Discriminant Analysis (LDA): 4 lectures 45:17
- Kernel PCA: 3 lectures 38:15
------- Part 10: Model Selection & Boosting ------
- 1 lecture 00:30
- Model Selection: 6 lectures 01:16:44
- XGBoost: 4 lectures 43:45
- Bonus lectures: 1 lecture 02:00
prerequisites:
- Just some high school mathematics level.
