Description
Machine Learning & Deep Learning in Python & R is the name of a machine learning and in-depth learning course in Python and R languages that covers regression, decision tree, SVM, neural networks, and time series prediction. This course covers all the steps that a person must take to be able to solve business problems by linear regression.
Most courses focus only on how the analysis was done, but we believe that what happens before and after the analysis is much more important. It is much more important than the analysis itself to be able to evaluate the efficiency of your model after the analysis and interpret the results that really help your business.
What you will learn in Machine Learning & Deep Learning in Python & R:
- How to solve real-life problems using machine_learning techniques.
- Machine learning models such as linear regression, logical regression, and KNN
- Advanced machine learning models such as Decision Tree, XGBoost, Random Forest, and SVM
- Understand the basics of statistics and machine_learning concepts
- How to perform basic statistical operations and implement machine learning in Python
- In-depth concepts of data collection and data processing for machine learning issues
- How to turn business issues into machine learning issues
Course details:
Publisher: Udemy
Instructors: Start-Tech Academy
Language: English
Education Level: Basic to Advanced
Number of Courses: 282 Lessons in 42 Sections
Duration: 35h 0m
Course topics
Course prerequisites:
Students will need to install Anaconda software but we have a separate lecture to guide you install the same
Pictures
Installation guide
After Extract, watch with your favorite Player.
English subtitle
Quality: 720p
Changes:
Version 2021/4 has increased by 1 lesson compared to 2021/2.
Version 2021/11 has not changed in the number of courses compared to 2021/4, but its total time has decreased by 1 minute.
download link
Download section 1 – 3 GB
Download Part 2 – 3 GB
Download section 3 – 3 GB
Download Section 4 – 1.90 GB
file password link
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