I started the “TensorFlow Data and Deployment specialization” and i am currently in week 1 course 1. i discovered that i have to take other courses before this one but i don’t know in what order! In what order should i take the DeepLearning.Ai courses ? what should i start with first!
( i took the ML and DL and Math specializations)
@Alaa_Sweed Since you’ve already taken the Machine Learning (ML), Deep Learning (DL), and Mathematics specializations, you’re well-prepared for TensorFlow-related courses. However, the “TensorFlow Data and Deployment” specialization is more advanced (as it is an Intermediate level and require Recommended experience as mention in the course) but since you have already familiar with this concept. What I can suggest is that brush up all your learning which you have done in above courses and try to follow some books which focus on TensorFlow.
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Okay seems reasonable, do you recommend any specific books that don’t need much time to finish since my schedule is crowded these days…, and from your advice, what course about TensorFlow should i start first?
Thank you for your time and patience ! 
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@Alaa_Sweed You may find these two books suitable for your needs:
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow – Aurélien Géron
- Deep Learning with Python – François Chollet
Additionally, you can explore other relevant resources based on your interests.
On another note, could you specify the exact courses you have completed previously? While you mentioned specializations in Machine Learning (ML), Deep Learning (DL), and Mathematics, knowing the specific courses will help us provide more tailored recommendations.
With Regards
Dr. Prashant
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Start with
TensorFlow developer professional, then you can probably go back to this course specialisation you took.
then take tensorflow advanced
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Thank you for the Book recommendations!
As for the specific courses, if i understand your question correctly, the courses i took are the ones in the specializations i mentioned, being:
Deep Learning Specialization:
- Neural Networks and Deep
Learning - Improving Deep Neural
Networks: Hyperparameter
Tuning, Regularization and
Optimization - Structuring Machine
Learning Projects - Convolutional Neural
Networks - Sequence Models
ML Specialization:
- Supervised Machine
Learning: Regression and
Classification - Advanced Learning
Algorithms - Unsupervised Learning,
Recommenders,
Reinforcement Learning
Mathematics for ML and DA:
- Linear Algebra for Machine
Learning and Data Science - Calculus for Machine
Learning and Data Science - Probability & Statistics for
Machine Learning & Data
Science
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Alright i will take this curriculum then, i appreciate your time, Thank you very much
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