This is the third course of total five courses for obtaining "Deep Learning Specialization" on "Coursera" platform by Andrew Ng which is provided by "deeplearning.ai".


This course explains how to build a successful machine learning project. This course also has two "flight simulators" that let the students practice decision-making as a machine learning project leader. This provides industry experience.

This course explain the low level concept of:

1. Understanding of diagnosing errors in a machine learning system.
2. Prioritizing the most promising directions for reducing error.
3. Understanding of complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance. 
4. Applying end-to-end learning, transfer learning, and multi-task learning.

This is the second course of total five courses for obtaining "Deep Learning Specialization" on "Coursera" platform by Andrew Ng which is provided by "deeplearning.ai".

Rather than the deep learning being a black box, this course explains what drives performance and how to get good results with industry best-practices.

This course explain the low level concept of:

1. Effective uses of the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking.
2. Application of variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
3. The best-practices for the deep learning era how to set up train/dev/test sets and analyze bias/variance

Besides this course also includes application of neural networks in TensorFlow as well as with raw code.

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This course is likely more advance course on Artificial Intelligence. As the Machine Learning course by Andrew Ng on "Coursera" had given me a strong foundation, I was able to accomplish this extended version of the course.

This is the first course of total five courses for obtaining "Deep Learning Specialization" on "Coursera" platform by Andrew Ng which is provided by "deeplearning.ai".

This course explain the underlying concept of Neural Network (NN) such as Neural Network Layers, Hidden Layers, Linear function, Activation function, Parameters, Hyper-parameters, Forward-Propagation, Cost function, Gradient-descent, Backward-Propagation etc.

Besides the lectures by Andrew Ng (as usual amazing), the programming assignment were structured and organized in a way that will help anyone to get the complicated concept easily as ever possible.

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I always wondered on participating on "kaggle". But for the lack of confidence I was hesitating to try it out. This one gives me an overview how the kaggle problem looks like. Now I can say, I am at least a little bit familiar with it. The problem was on the famous "Titanic" problem.


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Being interested on Data Science some people recommended me to try out Datacamp. This one is the first one I tried on Datacamp.


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This course was kind of crash course on Image Recognition (Supervised Classification Problem). Though it touches the surface level of Tensorflow, it gives a basic level of understanding about Tensorflow.

This is one of the highest rating Deep Learning course can be found on internet. Personally, I liked the intuition  part of the courses because it approaches the concept the simplest and easiest possible way, it could be. I learned a lot of things from the course and it gives me the confidence to go further deep on this arena.



The certification could be verified here.

I was always wondering how artificial intelligence functions, what it looks like, what its all about? Being a self taught it was'nt easy for me to getting here. It took me quite a long way to came here.and still a long way to go. I found this course by researching on the web the best one to start with. Some people refer to it as a bible of ML(Machine Learning).

While getting introduced to it, I was getting amazed by the mentor "Andrew Ng". His approach towards ML made all the concepts easy to understand. I am glad to took the course.

Its being said that, "Foundation needs to be strong before you getting advanced on it". Well, I think "Andrew Ng" just made it right.

All the credit goes to the amazing mentor "Andrew Ng".

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