How to Start AI and ML journey

[Aditya_Garg21]

Hi, i wish to start my ai and ml journey through the various specializations and courses offered by Andrew Ng. I have little experience in Python (I know about basics, identifiers, data types, variables, operators, if elif else statement, while for loop, break continue pass and user defined functions.). I require a detailed roadmap of how i can start my journey, which courses to take and when and what all i should learn apart from the courses. Would be grateful if you could also tell me how i can take this to the next level by learning about concepts such as Deep Learning, Tensorflow, Keras, LSTM, NLP, Generative AI, PyTorch, Computer Vision, RNN, CNN, Reinforcement Learning, etc.
Would be extremely grateful if someone can provide help in detail.
Thanks

[elirod]

Hi @Aditya_Garg21

Welcome to the community.

If you are asking for broad view of the data science career path you can try this:

Step by step guide to becoming a Data Scientist in 2023

AI and Data Science Roadmap I have noticed a consistent demand from our dear learners for some sort of roadmap that can assist them in their learning journey. In response to this, i recognize the importance of providing a clear and comprehensive guide to aid learners in achieving their educational goals. The Learning Sea Navigating the path of learning can often be a daunting task, especially in today’s fast-paced world where information is abundant and diverse. With so many options available, …

But, if you are asking for a roadmap regards the deeplearning.ai platform, i would follow this path:

Introductory

1. Mathematics for Machine Learning and Data Science Specialization
2. Machine Learning Specialization

Intermediate

1. Deep Learning Specialization
2. Tensor Flow: Data And Deployment Specialization
3. Tensor Flow: Advanced Techniques Specialization

Advanced

1. Machine Learning Engineer Specialization
2. Practical Data Science Specialization.

Keep in mind that these are just suggestions. The best roadmap it will depend of your goals.

I hope this help

Best regards
elirod

[Aditya_Garg21]

Thank you so much Sir!!! Will surely follow this.

[aimr]

Hi Aditya,

In addition to what my colleague @elirod has mentioned, in the Intermediate part, what has been useful to some students was:

1. In parallel or right after the Deep Learning Specialization, take the Tensor Flow Professional Specialization. The tensor specialization is very hands on, very well explained, and very coordinated with the Deep Learning Specialization. It will help you to consolidate your knowledge.
2. Leave the Tensor Flow: Data and Deployment Specialization and the Tensor Flow: Advanced Techniques Specialization for the Advanced part.
   2.1 The Tensor Flow: Data and Deployment Specialization complements very nicely with the Machine Learning Engineer Specialization
   2.2 The Tensor Flow: Advanced Techniques Specialization will prepare you for more advanced courses like Natural Language Processing, Medical Image, Generative, etc …

Hope it helps.

[elirod]

Hi @aimr

Thank you for your contribution. I didn’t recommended the Tensor Flow Professional Specialization in my early overview because i thought that it would be focus on the certified path only. But, after a kindly review, it looks like to me a better path to follow in this context.

Thanks again.