National Research University Higher School of Economics gives an opportunity through Coursera to archive vast ideas in applied machine learning techniques; this Specialization is the key to a balanced and extensive online curriculum. This is a good choice to fill out the rest of your machine learning expertise.
Coursera Advanced Machine Learning Specialization Review
Table of Contents
About the Specialization
The Coursera Advanced Machine Learning Specialization is a combination of 7 courses. They are:
- Introduction to Deep Learning: Basic understanding of modern neural networks, Basic knowledge of Python, Basic linear algebra, and probability
- How to Win Data Science Competitions: Learn from Top Kagglers, data modeling skills in various domains analyze and solve competitively such predictive modeling tasks.
- Bayesian Methods for Machine Learning: Bayesian methods allow you to compress your models a hundred folds.
- Practical Reinforcement Learning: foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc.
- Deep Learning in Computer Vision: computer vision, starting from basics and then turning to more modern deep learning models.
- Natural Language Processing: sentiment analysis, summarization, dialogue state tracking, to name a few.
- Addressing the Large Hadron Collider Challenges by Machine Learning: data generation machine for the time being.
“Applied” machine learning
You will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings after finishing all 7 courses.
You will be able to solve a wide variety of real-world problems like image captioning and automatic game playing throughout the course projects.
The Specialization learning structure
As prerequisites, it will be good for the learner to know about calculus and linear algebra (especially derivatives, matrices, and operations with them), probability theory (random variables, distributions, and moments), basic programming in python (functions, loops, numpy), basic machine learning (linear models, decision trees, boosting and random forests).
There will be assignments and Hands-on Projects.
You will gain the hands-on experience of applying advanced machine learning techniques that provide the foundation to the current state-of-the-art in AI.
Duration of the Specialization course
This Specialization takes about 8-10 months to complete the series of courses, so if it starts today, in a little under a year you’ll have learned a massive amount of machine learning and be able to start tackling more cutting-edge applications.
Mentors of the Specialization
Mikhail Hushchyn, Researcher at Laboratory for Methods of Big Data Analysis, HSE Faculty of Computer Science and many more qualified data analysis are the mentors of this Specialization.
Achievement through the course
You will able to create several real projects that result in a computer learning how to read, see, and play.
These projects will be great candidates for your portfolio and will result in your GitHub or Linking profile looking very active to any interested employers.
And after finishing all courses you will and complete the hands-on project, you’ll earn a Certificate that you can share with prospective employers and your professional network.
How to start and end
When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrolments and your progress. After the first 7 days free trial, there will be no refunds, but you can cancel your subscription at any time $49 USD per month to continue learning.
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Farzana Ahmed Sabera, Working as Digital Marketing Executive at Reinforce Lab Digital. Writer at JA DIRECTIVES with diverse knowledge of writing content and articles. Completed Bachelor of Science in Computer Science & Engineering, from University of Asia Pacific, Dhaka, Bangladesh. Done several research works focused on Digital Image Processing. Published research paper on International Journal. Always love to work with new technologies.