The outline of this course is mentioned below:

1.Dealing with Real-World Data Bias/Variance Tradeoff K-Fold Cross-Validation to avoid overfitting Data Cleaning and Normalization Cleaning web log data Normalizing numerical data Detecting outlie

2.Apache Spark: Machine Learning on Big Data Installing Spark - Part 1 Installing Spark - Part 2 Spark Introduction Spark and the Resilient Distributed Dataset (RDD) Introducing MLLib Decision Trees in Spark K-Means Clustering in Spark TF / IDF Searching Wikipedia with Spark Using the Spark 2.0 DataFrame API for MLLib

3.Experimental Design A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas

4.Deep Learning and Neural Networks Introduction to Deep Learning Deep Learning Pre-Requisites The History of Artificial Neural Networks Deep Learning in the Tensorflow Playground Deep Learning Details Introducing Tensorflow Using Tensorflow, Part 1 Using Tensorflow, Part 2 Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN's) Using CNN's for handwriting recognition Recurrent Neural Networks (RNN's) Using a RNN for sentiment analysis The Ethics of Deep Learning Learning More about Deep Learning Deep Learning Project Solution More No Shilling: Part 1 More No Shilling: Part 2

5.Final Project Your final project assignment Final project review 6.You made it! More to Explore 7.BONUS Bonus Lecture: Discounts on my Spark and MapReduce courses!