Advanced Machine Learning Specialization Coursera Review – 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.
Advanced Machine Learning Specialization Coursera Review
The Advanced Machine Learning Specialization Coursera learning Structure
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.
Enrollment: About 49k+ learners have already enrolled in this specialization.
Advanced Machine Learning Specialization Instructors
Mikhail Hushchyn, a Researcher at the Laboratory for Methods of Big Data Analysis, HSE Faculty of Computer Science, and much more qualified data analysis are the mentors of this Specialization.
Requirements to Take Advanced Machine Learning Specialization
The requirements are suggested, calculus and linear algebra, probability theory, basic programming in python, and basic machine learning.
The intended audience is all people who are already familiar with basic machine learning and want to get hands-on experience in research and development in the field of modern machine learning.
Advanced Machine Learning Specialization Scale up Your Skill in
You can enlarge your knowledge in-
- Recurrent Neural Network
- Convolutional Neural Network
- Deep Learning
- Data Analysis
- Feature Extraction
- Feature Engineering
- Bayesian Optimization
- Gaussian Process
- Markov Chain Monte Carlo (MCMC)
- Variational Bayesian Methods
Courses in Advanced Machine Learning Specialization Coursera
The Advanced Machine Learning Specialization Coursera is a combination of 7 courses. They are:
Course-1: Introduction to Deep Learning (Rating 4.6/5)
The course starts with linear models and a discussion of stochastic optimization methods that are crucial for training deep neural networks.
Here you can study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers.
Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks.
In the course, you can implement a deep neural network for the task of image captioning.
Course-2: How to Win Data Science Competitions: Learn from Top Kagglers (Rating 4.7/5)
In this course, you will learn to analyze and solve competitively such as predictive modeling tasks.
When you finish this class, you will understand how to solve predictive modeling competitions efficiently and learn which of the skills obtained can apply to real-world tasks.
By completing this course, you will become aware of inconsistencies, high noise levels, errors, and other data-related issues.
This course will teach you how to get high-rank solutions against thousands of competitors with a focus on the practical usage of machine learning methods.
Course-3: Bayesian Methods for Machine Learning (Rating 4.5/5)
You can learn from this online tutorial how to define a probabilistic model to make predictions from it.
Learn how one can automate workflow and how to speed it up using some advanced techniques.
By completing this part you can understand the application of Bayesian methods to deep learning and how to generate new images with it
From this course, you will also learn, how Bayesian methods allow you to compress your models a hundred folds.
Course-4: Practical Reinforcement Learning (Rating 4.2/5)
Here you will find out the foundations of Reinforce Learning methods: value/policy iteration, q-learning, policy gradient, etc.
You can find, math & batteries included using deep neural networks for RL tasks that are also known as “the hype train” state-of-the-art RL algorithms.
Learn how to apply duct tape to them for practical problems and also learn part of a neural network to play games.
Course-5: Deep Learning in Computer Vision (Rating 3.8/5)
A lot of new applications of computer vision techniques have been introduced in this part of this specialization.
The goal of this course is to introduce students to computer vision starting from the basics.
This tutorial will cover both image and video recognition, including
- Image classification and annotation
- Object recognition and image search
- Various object detection techniques, motion estimation
- Object tracking in video
- Human action recognition
- Image stylization, editing, and new image generation
Course-6: Natural Language Processing (Rating 4.5/5)
This course covers a wide range of tasks in Natural Language Processing from basic to advanced like sentiment analysis, summarization, and dialogue state tracking, to name a few.
By completing this part, you will be able to recognize NLP tasks in your day-to-day work and judge what techniques are likely to work well.
You will find a discussion about word alignment models in machine translation and see how similar it is to the attention mechanism in encoder-decoder neural networks.
Course-7: Addressing the Large Hadron Collider Challenges by Machine Learning (Rating 4.5/5)
In this course, you will introduce the main concepts of Physics behind those data flow so the main puzzles the Universe Physicists are seeking answers to will be much more transparent.
You will learn the major stages of the data processing pipelines, and focus on the role of Machine Learning techniques.
After the completion of the course, you will understand both the principles of Experimental Physics and Machine Learning much better.
Earn a Career Credential
You will gain hands-on experience in applying advanced machine learning techniques that provide the foundation for the current state-of-the-art in AI.
You will be able to create several real projects that result in a computer learning how to read, see, and play.
And after finishing all courses you will and completing the hands-on project, you’ll earn a Certificate that you can share with prospective employers and your professional network.
These Career Credentials will help you to unlock access to work in top universities and organizations as well as you can get a chance to get a career credential from the world’s best educational institution.
A career in Advanced Machine Learning
These projects will be great candidates for your portfolio and will result in your GitHub or Linked profile looking very active to any interested employers.
Starting a career in Machine Learning is not very hard. Quality material is available online, all you have to do is stay motivated and patient, at the end, it is all worth it.
In modern times, Machine Learning is one of the most popular career choices. Machine Learning is very popular as it reduces a lot of human efforts and increases machine performance by enabling machines to learn for themselves.
Consequently, there are many career paths in Machine Learning that are popular and well-paying such as;
- Machine Learning Engineer
- Machine learning Researcher
- Data Scientist
- NLP (Natural Language Processing) Scientist
- Business Intelligence Developer
- Human-Centered Machine Learning Designer
This Specialization takes about 8-10 months to complete a 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.
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 enrollments and your progress.
After the first 7 days of the free trial, there will be no refunds, but you can cancel your subscription at any time for USD 49 per month to continue learning.
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