Top 12 Courses for Machine Learning & Deep Learning

 


 

Before delving into what to do, how to do and where to do it from, I wanna establish some trust and context so that you feel confident reading my opinion and choosing the best courses that seems the best fit for you. After understanding and realizing crystal clearly that I wanna choose deep learning as my career, for the past 2 years I have completed more than 100+ courses online with complete submissions and serious devotion. From all those courses, I have picked my top 12 courses related to Machine learning and Deep learning to get started (beginner), master some skills (Intermediate) and becoming ready for real-life big production projects (Advanced).

I have numbered the courses in chronological order according to how I feel would be the best pathway to follow while learning, so that no concept gap is left while we are moving ahead of complexity and difficulty.

Let’s begin the whole framework of how to do it and where to do it from:

Beginner

 

1. Machine Learning A-Z (Udemy)

The course is available on Udemy and taught by SuperDataScience’s team. This was my first step towards learning ML and I am satisfied with what I learned from this course. The course requires you to have good python knowledge and hands-on and most of the work is done using the scikit-learn library. Although lessons with R language is also available so it’s up to your personal choice to either see them or not. This course will help to settle the base for other courses to deepen the understanding. So, if you are only looking to learn basic ML and don’t want to delve into much detail of every concept, this would be a good choice.

2. Mathematics for Machine learning, imperial college of London (Coursera)

For a ton of more high-level courses in Machine Learning and Data Science, you discover you need to brush up on the fundamentals in mathematics - stuff you may have learned before in school or college, yet which was instructed in another specific circumstance, or not intuitively, with the result that you struggle to relate it to how it's utilized in Computer Science. This specialization intends to overcome that issue, raising you to an acceptable level in the underlying maths, building an intuitive understanding, and relating it to Machine Learning and Data Science.

3. Machine learning -- Andrew Ng, Stanford University (Youtube)

This course needs little to no introduction in the domain of machine learning, If you are serious about Machine learning, It is “THE” best course to learn that from. Taught by one of the best instructors in the industry, Andrew Ng (Stanford Professor and founder of Deeplearning.AI whose courses you will see below). The course is available on Youtube for Free and consists of over 100 videos with a detailed explanation of most of the machine learning concepts that you will ever need, further on which you can delve deeper if you feel to do so.

4. Machine Learning with Tensorflow on Google Cloud Platform (Coursera)

If you choose to begin with this course, I would suggest that you attend only the first 2 courses from the specialization and for the rest, first take any of the above courses along with few hands-on projects so that you can cope up with their speed to teach ML on Google cloud. If the course content still seems hard, try completing the course just below for deep learning and then you will be all set to resume it without any issues.

5. Deep Learning Specialization by Deeplearning.AI (Coursera)

My absolute favourite course online, If you are looking to start learning Deep learning after Machine learning, don’t go anywhere. Simply sincerely complete this specialization on Coursera taught by none other than Andrew Ng itself through his company Deeplearning.AI that I mentioned above. This specialization will take you through a journey where you will learn deep learning through mathematics (its core structure) and its’ applied implementation on multiple domains like computer vision, Natural language processing and etc. Completing this course will open various paths for you to drive your future forward towards a more defined direction.

 

Intermediate

 

6. Stanford CS230: Deep Learning (Youtube)

This is an official course offered by Stanford university to their students. This along with other courses are made available to all the people for free by Stanford through sharing the recorded lectures of the class on youtube. If you have already completed or currently doing the Deep Learning specialization mentioned above, this course will be the best to move parallelly with. It focuses on the in-depth discussion on topics taught in the specialization with some interesting projects to make throughout the course.

7. Practical Deep Learning for coders by fastai (Youtube / fast.ai)

This is a really popular course, loved by thousands of learners who then gets to make state-of-the-art models and win various A.I. competition worldwide. A completely free course that is available both on youtube and their official site (fast.ai). It’s not just limited to video lectures but also labs to practise every single thing that you will learn, along with an awesome book to keep referencing concepts, learn more in detail while you are working or revising some things. The complete package that you will get from this course will be sufficient enough to make you feel confident and do some extraordinary things with your skills and creativity. Instead of me briefing on this, check out what the course offers: Practical Deep Learning for Coders | Practical Deep Learning for Coders (fast.ai)

8. Deeplearning.AI Tensorflow Developer (Coursera)

This course was created with the motive to teach the learners essential functioning and usage of TensorFlow for all sorts of domains and eventually cover most of the topics that are part of the syllabus for the “Google Tensorflow Certification Exam”. This is the first specialization that you would wanna do if you are looking to master your TensorFlow skills. Specialization picking after this one is mentioned below. It would be a really great way to start or even sharpen your hands-on deep learning skills.

9. TensorFlow: Data and Deployment (Coursera)

After completing the above specialization, if you wish to learn to deploy your models in the world and some advanced TensorFlow tools and services, this would be a great choice. I would recommend you to do this course after to successfully completed the above one because making models is fine but what happens after that (deployment) is equally important and crucial as a machine learning engineer or something related.

 

Advanced

 

10. Machine Learning Engineering for Production (MLOps) (Coursera)

The Machine Learning Engineering for Production (MLOps) Specialization covers how to understand and visualize, build, and maintain complete workflows and integrated systems that constantly keep operating in production. Understanding only machine learning and deep learning ideas are fundamental, however, if you are serious about building a powerful AI career as a machine learning engineer, deep learning engineer or machine learning systems engineer, you need production engineering capabilities too. In noticeable difference with standard machine learning modeling, production systems need to handle tireless evolving data. Through this Specialization, you will figure out how to utilize popular tools and strategies for doing all of this efficiently and effectively.

11. Advanced Machine Learning on Google Cloud (Coursera)

This 5-course specialization centers around cutting edge machine learning subjects using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This specialization gets where "Machine Learning with TensorFlow on GCP" left off and shows you how to assemble scalable, accurate, and production-ready ML models for structured data, image data, time-series, and natural language text. It closes with a course on building recommendation systems. Topics introduced in earlier courses are referred to in later courses, so I will suggest you take the courses in the same order.

12. TensorFlow: Advanced Techniques (Coursera)

After completing the two specializations mentioned above regarding TensorFlow by Deeplearning.AI. You can further expand your knowledge of Tensorflow’s Functional API and then build exotic non-sequential models. You will also be able to figure out how to optimize training in different environments with multiple processors and chip types and get introduced to cutting edge computer vision scenarios. For example, object identification, image segmentation etc. Moreover learn about generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs. After completion of this, you can confidently stand with your chest pumped and feeling proud about your hard work and skills that you honed with Tensorflow and Deep learning models.


That was the end of the article, if you pick out the best courses for you and start learning, you will be learning with the highest quality content available online, in my current knowledge. This will definitely prevent wasting your time and get skilled faster.


Gargeya Sharma

B.Tech Computer Science 3rd year
Specialized in Data Science and Deep Learning
Data Scientist Intern at Upswing Cognitive Hospitality Solutions
For more info check out my Github Home Page


LinkedIn           GitHub

Photo by Nick Morrison on Unsplash

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