After years of researching the online learning landscape and signing up for numerous machine learning courses on different platforms, the author of this article has collected the best five machine learning courses available.
From Medium by LearnDataSci, Compiled by Heart of the Machine.
Machine learning, rooted in statistics, is emerging as one of the most interesting and fastest-growing areas of computer science. Machine learning can be applied to countless industries and applications to make them more efficient and intelligent.
Chatbots, spam filtering, advertising services, search engines and fraud detection are just some examples of how machine learning models can be used in everyday life. Machine learning allows us to find patterns and create mathematical models for things that humans cannot.
Unlike data science courses, which cover exploratory data analysis, statistics, communication and visualization techniques, machine learning courses focus on machine learning algorithms, principles of mathematics, and how to write algorithms in a programming language.
This article introduces the top 5 machine learning courses:
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Wu En of machine learning courses: https://www.learndatasci.com/out/coursera-machine-learning/
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Wu En for “deep learning specialized course” : https://www.learndatasci.com/out/coursera-deep-learning-specialization/
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Machine Learning with Python by SAEED AGHABOZORGI https://www.learndatasci.com/out/coursera-ibm-machine-learning-python/
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The Advanced Machine Learning specialized course: https://www.learndatasci.com/out/coursera-advanced-machine-learning-specialization/
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At Columbia University in the Machine Learning courses: https://www.learndatasci.com/out/edx-columbia-machine-learning/
Selection criteria
The five machine learning courses presented in this article follow the following criteria:
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Strictly focused on machine learning.
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Use a free open source programming language such as Python, R, or Octave.
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Use free open source libraries.
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Includes programming assignments and practices.
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Explain the mathematics of how the algorithm works.
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Students can pace themselves and get new classes about once a month.
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The lecturers are interesting and the classes are interesting.
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Ratings and reviews on various websites and forums are above average.
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If you want to learn machine learning fully as soon as possible, you should also read books about it in addition to online learning. The author recommends the following two books, which greatly influenced his study.
books
1. An Introduction to Statistical Learning: with Applications in R
Free online version: http://www-bcf.usc.edu/~gareth/ISL/
With clear and straightforward explanations and examples, this book can help readers improve their mathematical understanding of basic machine learning techniques. The book is more theoretical, but still contains some exercises and examples using R.
2. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
This book is a good complement to the previous one and deals primarily with machine learning applications using Python. This book is paired with any of the following courses to help you hone your programming skills and learn how to apply machine learning directly to your projects.
Here are the highlights of this article: Top 5 Machine learning courses
I. Machine Learning by Ng
The course is for beginners and is taught and created by Stanford Professor Andrew Ng, co-founder of Google Brain and Coursera.
Assignments for this course require the use of the open source programming language Octave, not Python or R. This may seem strange to many, but for newcomers Octave is an easy way to learn the basics of machine learning.
Overall, the material is informative, taught directly by Ng, explaining in detail all the mathematics necessary for each algorithm, as well as some calculus and linear algebra. This course is largely self-contained, but a little advance knowledge of linear algebra is helpful.
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Course provider: Andrew Ng, Stanford University
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Cost: Free; A course certificate is $79
Course Structure:
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Univariate linear regression
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Outline of linear algebra
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Multivariate linear regression
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Octave/Matlab tutorial
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Logistic regression
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regularization
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Neural networks: representation
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Neural networks: Learning
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Suggestions for using machine learning
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Machine learning system design
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Support vector machine
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Dimension reduction
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Anomaly detection
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Recommendation system
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Large-scale machine learning
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Application case: Photo OCR
The course lasts 11 weeks. If you can stick with the entire course, you should have a good basic understanding of machine learning in about four months.
Later, you can take advanced or specialized courses that interest you, such as deep learning, machine learning engineering, etc.
This course is undoubtedly the best course for beginners.
Refer to the article: resources | Wu En “machine learning” notes, Columbia University graduate student
2. Ng Deep Learning Courses
It was also taught by Ng. This is a more advanced course series for anyone interested in machine learning, deep learning and its principles and applications.
The course consists of five courses, each of which uses the Python programming language and the TensorFlow neural network library for assignments and lectures. This course is a good follow-up to Ng’s machine learning course, as the teaching style is similar and you can also learn machine learning using Python.
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Course provider: Andrew Ng, Deeplearning. ai
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Cost: Free; $49 / month for course certificate
Course Structure:
Neural networks and deep learning
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Introduction to Deep Learning
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Basic concepts of neural networks
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Shallow neural network
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Deep neural network
2. Improve neural network: parameter tuning, regularization and optimization
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Deep learning practices
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Optimization algorithm
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Hyperparameter tuning, batch normalization, and programming frameworks
3. Build machine learning projects
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Machine Learning Strategies (1)
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Machine Learning Strategies (2)
Convolutional neural network
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Fundamentals of convolutional neural networks
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Deep convolution models: a case study
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Target detection
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Special applications: face recognition and neural style transfer
5. Sequence model
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Recurrent neural network
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Natural language processing and word embedding
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Sequence models and attentional mechanisms
To understand the algorithms introduced in this course, you should be familiar with linear algebra and machine learning. If you need advice on how to learn the math you need, see the Learning Guide at the end of this article.
Reference article:
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Deepplearning. Ai course learning experience: a necessary course for Deeplearning (certified)
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Introduction | Wu En da Deeplearning. Ai all course learning experience sharing
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Resources | Wu En da deeplearning. Five course complete notes know about the ai?
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This is a beautiful infographic that summarizes the deeplearning.ai course that Ng likes
Machine learning in Python
This is also a beginner’s course, focusing only on the most basic machine learning algorithms. The lecturer, slide animation and explanation of the algorithm are well combined to give you an intuitive understanding of the basic concepts.
The course uses Python, but there is less about the math behind the algorithms. With each module, you will have the opportunity to download an interactive Jupyter Notebook in your browser to practice the new concepts you learn. Each Notebook solidifies your knowledge and provides detailed instructions for using algorithms on real data.
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Course provider: IBM, Cognitive Class
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Cost: Free; For course certificate, $39 / month
Course Structure:
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Introduction to machine learning
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Return to the
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classification
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clustering
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Recommendation system
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The final project
One of the biggest benefits of this course is that it provides practical advice for each algorithm. When teaching the new algorithm, the instructor will explain how it works, its pros and cons, and what situations you should use it in. Other courses rarely cover this, but this information is important for beginners to understand the wider context.
Advanced machine learning courses
This is another advanced course on machine learning. If you want to learn as much as possible about machine learning, this course is a great choice.
The teaching of the course was excellent: excellent and to the point. Since this is an advanced course, you’ll need more math. If you’ve already taken an introductory course and reviewed linear algebra and calculus, this course is a good place to supplement your other expertise in machine learning.
Much of the material covered in this course is essential to many machine learning projects.
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Course Provider: National Research University Higher School of Economics (HSE), Russia
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Cost: Free; $49 / month for course certificate
Course Structure:
1. Introduction to deep learning
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Optimizing the profile
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Introduction to Neural networks
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Image deep learning
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Unsupervised representational learning
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Sequential deep learning
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The final project
2. How to win a data science competition: Learn from the top Kagglers
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Introduction and review
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Model feature processing and generation
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Final Project Description
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Exploratory data analysis
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validation
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The data reveal that
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Measures to optimize
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Advanced Feature Engineering 1
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Hyperparameter optimization
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Advanced Feature Engineering ii
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integration
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Competition is introduced
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The final project
3. Machine learning Bayesian approach
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Introduction to Bayesian methods and conjugate priors
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Expectation maximization algorithm
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Variational inference and Implicit Dirichlet Distribution (LDA)
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Markov chain Monte Carlo
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Variational autoencoder
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Gaussian process and Bayesian optimization
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The final project
4. Practical reinforcement learning
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Introduction to the
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Core of reinforcement learning: dynamic programming
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Modelless method
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Method based on approximation
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Policy-based approach
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explore
Deep learning in computer vision
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Introduction to image processing and computer Vision
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Convolution features of visual recognition
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Target detection
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Target tracking and motion recognition
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Image segmentation and synthesis
6. Natural language processing
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Concept introduction and text classification
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Language modeling and sequence annotation
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Semantic vector space model
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Sequence to sequence task
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Dialogue system
7. Tackling the LHC challenge with machine learning
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Introduction to particle physics for data scientists
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Particle identification
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Exploring new physics in rare decay
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Machine learning for dark matter hints in the new CERN experiment
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Detector optimization
The course will take about 8-10 months to complete, so if you start today, you’ll learn a lot about machine learning and cutting-edge applications over the course of almost a year.
During these months, you will also create several real projects. These projects will greatly enhance your resume and make your GitHub more appealing.
Machine learning
This is an advanced level course with the highest mathematical foundation requirements. You need a solid foundation in linear algebra, calculus, probability, and programming. The interesting programming assignments for this course can be done using Python or Octave, but there are no courses on either language.
The highlight of this course is that it covers probabilistic approaches to machine learning. If you’ve already read textbooks like Machine Learning: A Probabilistic Perspective, this course will be A good addition.
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Course provider: Columbia University
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Cost: Free; A course certificate is $300
Course Structure:
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Maximum likelihood estimation, linear regression, least square method
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Ridge regression, bias – variance, Bayes’ rule, maximum posterior probabilistic inference
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Nearest neighbor classification, Bayesian classifier, linear classifier, perceptron
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Logistic regression, Laplace approximation, kernel method, Gauss process
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Maximum interval, Support Vector Machine (SVM), Tree, Random forest, Boosting algorithm
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Clustering, K-means, EM algorithm, missing data
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Mixed Gaussian process, matrix decomposition
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Nonnegative matrix factorization, hidden factor model, principal component analysis and its variants
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Markov model, hidden Markov model
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Continuous state space model, association analysis
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Model selection and future trend
Many beginner courses may have covered many of these topics, but the math in this course is solid. If you’ve already done this, and you want to take the math a step further, and you want to derive some algorithms by doing programming assignments, then this course is worth taking.
Study guide
First, I will introduce the knowledge base required for most machine learning courses.
Course Knowledge Reserve
Advanced courses require the following knowledge:
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Linear algebra
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The probability of
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Differential and integral calculus
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programming
These are essential knowledge for understanding the inner workings of machine learning. Many beginner courses usually require at least some programming background and familiarity with the basics of linear algebra, such as vectors, matrices and their notation.
Machine Learning by Ng, the first course mentioned in this article, reviews most of the math required, but if you haven’t taken linear algebra before, it may be difficult to learn both.
If you need to brush up on the basics of math, follow these tips:
Python is recommended as the programming language for most machine learning courses. Even if you’re taking Ng’s Machine Learning course (which uses Octave), you should make time to learn Python because you’ll need it sooner or later. Dataquest. IO is also an excellent Python resource with many free Python lessons in its interactive browser environment.
These knowledge reserves are helpful to understand the working principle of the algorithm.
Based algorithm
Here is a set of basic algorithms that you need to familiarize yourself with and practice using:
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Linear regression
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Logistic regression
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K-means clustering
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K – nearest neighbor
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Support Vector Machine (SVM)
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The decision tree
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Random forests
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Naive Bayes
These algorithms are essential to understand, and there are many others. These algorithms and their variants are introduced in this course. Understanding how these technologies work and when to use them is important to tackle new projects.
In addition to these basic algorithms, there are some more advanced techniques to learn:
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integration
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Boosting
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Dimension reduction
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Reinforcement learning
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Neural networks and deep learning
This is just the beginning. These algorithms are often used in the most interesting machine learning solutions, and they are a useful addition to your toolbox.
As with basic technology, every time you learn a new tool, you need to apply it directly to the project to deepen your understanding and understanding.
To solve the project
Learning machine learning online is difficult and rewarding. But remember, just watching videos and taking quizzes doesn’t prove that you’re actually studying the material. You will learn more if you create a project that uses different data and goals than the class.
As soon as you start learning the basics of machine learning, you should look for interesting data to practice your new skills. This course will give learners an idea of when to apply algorithms, so it is good practice to apply machine learning algorithms in your own projects.
Through trial and error, exploration, and feedback, you will discover how to use different techniques, how to measure results, and how to categorize or predict. About ML project, here is a sample list, see: https://github.com/NirantK/awesome-project-ideas.
Solving projects will help you understand machine learning better and at a higher level. If you start to understand more advanced concepts (such as deep learning), there are an infinite number of techniques and methods to understand and use.
New Research on Reading
Machine learning is a rapidly evolving field, with new technologies and applications emerging every day. Once you’ve mastered the basics of machine learning, you can read research papers that interest you.
There are websites for instant access to new papers in your area of interest. For Google Scholar, type in the keywords “Machine Learning” and “Twitter”, or any other topic of interest, and click “Create Alert” to be notified by email of new papers.
Make a habit of reading reminder emails every week, scanning papers to determine if you need to read them, and then digging into the specifics of a particular study. If some of the research is relevant to the project you’re working on, see if you can apply these techniques to your own problem.
conclusion
Machine learning is a very interesting field for you to learn and experience. I hope you can find a suitable course here.
Machine learning is an important part of data science. If you’re interested in statistics, visualization, data analysis, etc., check out this article on top data science course recommendations: https://www.learndatasci.com/best – data – science – online – courses /.
