Machine Learning Fundamentals
Description
In this 4-day course you will learn how to use several machine learning algorithms. We will start with simple linear regression and work our way towards deep neural networks. we will also learn how to handle our data and create efficient data sets that will help our machine learning process be faster and better.
Intended audience
This course is intended for Software engineers as well as decision makers in the organization.
Topics
Module 01 - What is Machine Learning?
What is machine learning good for
Where is machine learning used
Terminology
Module 02 – Regression
Linear Regression
Calculating and reducing Loss
Intro to Pandas
Intro to TensorFlow
Module 03 – Generalization
Overfitting and how to avoid it
Creating your data sets
Module 04 - Feature Engineering
Mapping Numerical and categorical values
Multi and One hot encoding
Scaling
Binning
Data Verification
Module 05 - Non Linear Features
Feature Crosses
Module 06 – Regularization
Measuring Complexity
L2 regulatization
Lambda
Module 07 - Logistic Regression
Understanding Logistic Regression
Logistic Regression Loss
Regularization
Module 08 – Classification
Threasholding
Expanding the true false notion
Accuracy, Precision and Recall
Module 09 – Sparsity
Handling huge sparse vectors
L1 regularization
Module 10 - Neural Nets
Non Linear Problems
Hidden Layers
Activation Functions
Common Failures
Regularization
MultiClass Neural Networks
SoftMax
Module 11 - Embeddings
Collaborative Filtering
Reducing Dimensions
Wotd2Vec
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