Artificial Neural Networks(ANN) Made Easy

Course Covers below topics in detail
•Quick recap of model building and validation
•Introduction to ANN
•Hidden Layers in ANN
•Back Propagation in ANN
•ANN model building on Python
•TensorFlow Introduction
•Building ANN models in TensorFlow
•Keras Introduction
•ANN hyper-parameters
•Regularization in ANN
•Activation functions
•Learning Rate and Momentum
•Optimization Algorithms
•Basics of Deep Learning
Pre-requite for the course. 
•You need to know basics of python coding
•You should have working experience on python packages like Pandas, Sk-learn
•You need to have basic knowledge on Regression and Logistic Regression
•You must know model validation metrics like accuracy, confusion matrix
•You  must know concepts like over-fitting and under-fitting
•In simple terms, Our Machine Learning Made Easy course on Python is the pre-requite.
Other Details
•Datasets, Code and PPT are available in the resources section within the first lecture video of each session.
•Code has been written and tested with latest and stable version of python and tensor-flow as of Sep2018