Is there a certain order to take these courses?
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Hello @Hal_Roberts! Welcome to the DeepLearning.AI community!
We highly recommend following the courses in a sequential manner, however, if you already have a solid foundation in a certain topic, you may choose to skip ahead to a higher-level course. But reviewing previous concepts can still be valuable for reinforcement.
Best,
Saif.
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Hi, Hal.
There are a lot of courses and specializations available here. They are grouped into “Introductory”, “Intermediate” and “Advanced” categories. Here’s a page that is the place to start to explore the curriculum here. Of course it also matters a lot what your background is and what your goals are. What is it that you want to learn and get out of the courses here? With more information about your background and your goals, it will be easier to make suggestions about possible paths you can take through the courses here.
Regards,
Paul
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Ok so my schedule I have built is:
ai for everyone
machine learning spec
math for machine learning
deep learning spec
NLP
ai for medicine
tensorflow developer
tensorflow data deployment spec
tensorflow advanced
GANS
MLOps
practical data science on AWS
I am currently a data analyst and have been using python for a few years now. I understand data, but am new to machine learning. I am planning on learning to become a machine learning engineer and wanting to build the best path to get there.
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Hi @Hal_Roberts! I understand that you’re interested in taking all of our courses, but it’s not the most recommended approach.
As @paulinpaloalto suggested, having more information about your background and goals will make it easier to suggest a personalized path through our courses.
If you’re unsure of your future goals and what you want to achieve through our courses, I recommend starting with our Introductory courses (first take AI for Everyone, then Machine Learning Specialization). This will give you a solid understanding of Machine Learning and help you make informed decisions about which courses to take next.
Best,
Saif.
Would you have any recommendations on courses to becoming a machine learning engineer?
Hi Hal!
As an ML engineer, having domain knowledge in the specific industry you are working in can be highly beneficial. For instance, an ML engineer in the healthcare industry would benefit from knowledge of medicine, while one in the finance industry would benefit from knowledge of economics, accounting, and financial regulations. Similarly, if you are working in the oil and gas industry (like me), having knowledge of drilling and reservoir engineering would be an asset.
As a starting point, it is recommended to pursue a Machine Learning Specialization. This will provide a foundation of the ML, and from there, you can make informed decisions on which courses or areas to specialize in next based on your interests and career goals.
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Update: sorry, when I wrote the following, I (obviously) missed your intermediate post where you said you were experienced in python and gave your proposed roadmap. Please just consider this as a general map.
Do you already have programming experience including in python? If not, then you need to take a python course first. All the courses here assume python proficiency.
Do you know enough Linear Algebra to know how matrix multiplication (dot product style) works and are comfortable working with vectors and matrices? If not, then you should take at least Course 1 of Mathematics for Machine Learning (M4ML) as your next step after learning python (if required). Course 2 and Course 3 of M4ML cover calculus and probability and statistics. M4ML is new and I haven’t taken it yet (I already have a math degree), but most of the intermediate curriculum is designed such that you don’t need calculus or statistics knowledge (for DLS e.g.), but knowing those will help your intuitions.
If you already have both python and linear algebra in your toolbox, then you can start with MLS as Saif has suggested and then you’ll definitely want to take DLS next after that. But you could also just jump directly to DLS if you have the math and programming. DLS does not assume any previous ML knowledge. MLS is more introductory and will give you exposure to other types of ML algorithms besides neural networks, but DLS is really the core of the intermediate curriculum here. It gives you a thorough introduction to all the main types of Deep Neural Networks: Fully Connected Nets, Convolutional Nets and Recurrent Nets (Sequence Models).
Once you’ve mastered DLS, then you have a number of options. You can explore the various TensorFlow courses to learn more about how to apply TF. You may also want to then take some of the more domain specific courses as Saif mentioned, e.g. AI for Medicine or Natural Language Processing. The GANs Specialization is also very interesting and not really domain specific.
That should be enough to get you started and as you learn you will certainly develop your own curiosity about which directions are the most interesting to pursue further.
I hope you will enjoy your learning journey!
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