Coursera Machine Learning is the name of the video training collection in the field of machine learning. The topic of machine learning has become very widespread today, and in fact, it is considered a solution to progress toward artificial intelligence at a very high level. In this training course, you can strengthen your knowledge in the field of machine_learning by learning the most effective and key techniques and learning how to implement them. In this course, theoretical and practical material has been prepared for you.
In this course and at the beginning of specific chapters, you will begin to learn about different concepts such as parametric and non-parametric algorithms, kernels, neural networks, etc. Also, by watching this course and learning the principles and basics of machine_learning, you will be able to build intelligent robots that have the ability to understand and control. This course is also designed and published for those interested who want to learn machine learning in a practical and practical way.
What you will learn in the Coursera Machine Learning training series:
- Basic to advanced machine_learning
- Familiarity with techniques and practical skills in your real projects
- Learning material from a theoretical and practical aspect to improve skills
- Getting familiar with and understanding the use of parametric and non-parametric algorithms
- Familiarity with neural networks and artificial intelligence in practice
- Publisher: Coursera
- English language
- Duration: Assuming 8 hours of work per week, about 2 months
- Number of courses: 3
- Instructor: Andrew Ng , Eddy Shyu, Aarti Bagul and Geoff Ladwig
- File format: mp4
- Course Level: Introductory
- Presenting institution/university: DeepLearning.AI and Stanford University
Courses available in the Coursera Machine Learning training set:
Supervised Machine_Learning: Regression and Classification
Advanced Learning Algorithms
Unsupervised Learning, Recommenders, Reinforcement Learning
Coursera Machine Learning course prerequisites
What background knowledge is necessary for the Machine Learning Specialization?
Learners should understand basic coding (for loops, functions, if/else statements) and high school-level math (arithmetic, algebra). Any additional math concepts will be explained along the way.
Who is the Machine Learning Specialization for?
The Machine Learning Specialization is a beginner-level program aimed at those new to AI and looking to gain a foundational understanding of machine learning models and real-world experience building systems using Python.
This specialization is suitable for learners with some basic knowledge of programming and high-school level math, as well as early-stage professionals in software engineering and data analysis who wish to upskill in machine learning.
After extracting, watch with your favorite player.
This educational series consists of 3 separate courses.
Course 1 – Supervised Machine_Learning: Regression and Classification
Download the course – 656 MB
Download additional files (Jupyter Notebooks, Python Scripts, etc.)
Course 2 – Advanced Learning Algorithms
Download part 1 – 1 GB
Download part 2 – 89 MB
Course 3 – Unsupervised Learning, Recommenders, Reinforcement Learning
Download the course – 762 MB
file password link
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