Best Machine Learning Course


16 Tips To Select The Best Machine Learning Course

Suggested Udemy Courses:

Machine Learning A-Z™: Hands-On Python & R In Data Science

Python for Data Science and Machine Learning Bootcamp

Data Science, Deep Learning, & Machine Learning with Python

Data Science and Machine Learning Bootcamp with R

Deep Learning Prerequisites: Logistic Regression in Python

Deep Learning Prerequisites: Linear Regression in Python

Data Science and Machine Learning Bootcamp (with Python)

Deep Learning A-Z™: Hands-On Artificial Neural Networks

Data Science A-Z™: Real-Life Data Science Exercises Included


We help you understand what the best machine learning course outline should contain!

Looking for good machine learning courses online? What if you could save time, and money, by getting the ideal course right away?

Finding a good machine learning course online can be a challenge. There are several factors to consider when deciding on a good course. This article will provide you with the context to find the best course in machine learning. Think about this article like a syllabus of topics that the best machine learning course should cover.

First, the reputation of the course is very important. Look for big-name providers. Coursera, EdX, Udacity, Udemy, and Lynda come to mind. Many of the courses that these MOOCs offer are in partnership with large, well-known universities. Tenure-track professors often architect and run these courses. For example, Stanford’s Andrew Ng runs an excellent machine-learning course. Other prestigious universities, Stanford, Johns Hopkins, and the University of Michigan, partner with companies like Google, Amazon, and Microsoft to bring you top-notch courses for free or close to it.

Second, you should look for machine learning courses that have lectures in scripting languages. Why? Because you need to use a scripting language to implement machine learning algorithms. The most popular scripting languages that people use for machine learning today are Python and R. Gaining an understanding of Python and R is important to succeed in a machine learning course. Watch out for courses that do not introduce one of these languages or do not have these languages at all. Any high-level machine learning course will go over how scripting languages interface with machine learning.

Third, it is important to look out for how the courses deal with data. In a job, data is not neatly packaged. Using extract load transform process (ETL), you should learn how to extract data from a SQL database or an API. Only then will you be able to clean the data and analyze it (e.g., R – dplyr, data.table, reshape; Python – Numpy, Pandas). Similarly, using exploratory data analysis (EDA) should be a requirement of a good machine learning course. One you acquire the data, before you use a machine learning algorithm, you should use EDA to understand what the data is and what it means (e.g., R – instance, ggplot2, lattice; Python – matplotlib, seaborn). A good machine learning course should offer data visualization content.

Those are the basic parameters of what makes a good machine learning course. Now, it is time to dive deeper into some details to figure out the difference between a good and great course. A great course will include reproducible analysis: students should be able to reproduce products that they learned in the course. The students should be able to follow steps to an outcome, using something like RMarkdown (knitr package). An introduction to GitHub is important so students understand what version control is and what it means to work in a team. While not a requirement, if a course offers a brush up in calculus and linear algebra, it would ensure that students have a foundation for understanding machine learning. Similarly, a course should give an introduction and some advanced statistics, specifically covering normal and binomial distributions, confidence intervals, and p-values.

From a more advanced perspective, the course should give students an opportunity to learn the difference between unsupervised machine learning and supervised machine learning. At the beginning, it would make sense if a course started with cluster analysis using k-means and hierarchical clustering. Similarly, courses should offer dimension reduction examples like component analysis as part of the statistics module. In the supervised machine learning space, a course should cover regression models, logistic regression, classification algorithms, and ensemble methodology. Finally, the course should provide an overview of deep neural networks and natural language processing. These advanced elements would be included in a top-quality machine learning course.

A successful course will have many examples. Examples help students clarify some aspects of machine learning. A great course will give the students machine learning theory sources for students who would like to focus more on the theoretical underpinnings behind these algorithms. Because machine learning is so team-based, a good course will provide solo and teamwork. For this reason, it is important to assign projects by taking data from machine learning repositories like Kaggle, UCI, and others, and also to give exams by providing assessments and by answering multiple choice questions.


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