Machine learning, as a field, is changing rapidly. To stay at the top, you need to keep learning. And with so many courses available, it can be a bit intimidating to decide which one to take. In this article, we'll highlight some of the best courses that define what it means to excel in machine learning. You can start by building a solid foundation in machine learning, then move on to advanced techniques to become a master.
1. Machine Learning from Stanford University (Andrew Ng, Coursera)
Summary: Taught by renowned AI expert Andrew Ng, this course is one of the most well-known and thorough introductory courses to machine learning.
Major features:
a. Foundational Knowledge: Includes algorithms and core techniques you need to know, such as supervised and unsupervised learning, and best practices.
b. Hands-on: There are homework and quiz exercises that reinforce students' concepts.
c. Broad applicability – concepts that can be taught across multiple industries.
Why it defines excellence: This clear explanation by Andrew Ng is very practical and makes a very difficult concept very clear even for beginners.
2. Deep Learning Specialization by Andrew Ng (Coursera)
Overview: Explore the Deep Learning courses offered by Coursera. One of the largest and most popular courses in the deep learning field, this five-course specialization covers the topics of neural networks and deep learning.
Major features:
a. Covers a wide range of topics: neural networks, convolutional networks, sequence models, etc.
b. Hands-on real-world challenges: Hands-on projects with real-world datasets.
c. Expert Guidance: Guidance from Andrew Ng and other leading experts in the field.
What defines excellence: This course has a strong focus on deep learning; practical skills directly applied to relevant real-world problems.
3. Machine Learning Engineer Nanodegree, Udacity
Course Overview: This course aims to enable learners to become Machine Learning Engineers.
Major features:
a. Industry-relevant skills: Focus on in-demand tools and techniques.
b. Actual Project: Implement a project created in collaboration with IBM and AWS.
c. Career Support: Guidelines, career planning and job placement support are available.
What defines excellence: With strong industry connections and a focus on practical skills, this program is perfect for anyone looking to build a career in machine learning.
4. CS50 Introduction to Artificial Intelligence with Python – edX
Summary: Harvard University's CS50 top AI course uses Python to help students understand the fundamentals of AI and machine learning.
Major features:
a. Comprehensive structure: search algorithms, optimization, machine learning, neural networks.
b. Interactive learning: Hands-on projects and problems.
c. Harvard Certification: Finally, a certificate issued by Harvard University.
Why it defines excellence: Harvard's CS50 series is known for the rigorous coursework and hands-on approach that learners trust.
5. Advanced Machine Learning Specialization (Coursera)
Summary: This specialization offered by the National Research University Higher School of Economics explains how advanced machine learning topics are put to use.
Major features:
a. Diverse topics: Deep Learning, Reinforcement Learning, Bayesian Methods, Computer Vision.
b. Expert Faculty: Our faculty are some of the most renowned researchers and practitioners in their fields.
c. Capstone Project: Apply this knowledge to a broad range of projects.
Why it defines excellence: Advanced ML techniques are taught at the right level to enable learners to apply the techniques to the most innovative applications.
6. Applied Data Science Specialization with Python (Coursera)
Summary: A course offered by the University of Michigan that uses Python to apply data science techniques.
Major features:
a. Practical focus: Emphasis on how to use data science in real life.
b. Diverse Topics: Topics include data visualization, machine learning, text analytics, and social network analysis.
c. Hands-on Projects: Includes real-world projects to help you put into practice the concepts you have learned.
Why we define excellence: Our pragmatic approach and exposure to Python, the leading language in data science, will make us highly in demand as a future machine learning professional.
7. MITx MicroMasters in Statistics and Data Science (edX)
MIT's MicroMaster's program in Statistics and Data Science provides a solid foundation for pursuing further advanced studies in data science.
Major features:
a. Comprehensive Curriculum: Covers all major areas including Probability, Data Analytics, Machine Learning, and Big Data.
b. Instructed by MIT faculty: Courses will be taught by experienced faculty from MIT.
c. Countable towards a Master's Degree: Can be used towards completion of a Master's degree.
Why it defines excellence: There's depth in the rigorous curriculum and its precise details, and the affiliation with MIT adds credibility.
8. Google AI Machine Learning Crash Course
Course Overview: Google's crash course is an introduction to the world of machine learning with a focus on TensorFlow.
Major features:
a. Hands-on approach: It mainly helps with practical implementation using TensorFlow.
b. Interactive lessons: The course includes video lectures, case studies, and coding exercises.
c. Google Expertise:- Leverage Google's decades of AI and machine learning knowledge.
Why: This course is very practical, uses TensorFlow, the most popular machine learning library, and gets right to the point.
