Machine learning (ML) is causing quite the buzz in the healthcare industry as a whole . Payers to healthcare companies around the world are taking advantage of ML today. In this post, I will demonstrate a use case and show how we can harness the power of ML and apply it real world problems. We’ll walk through a very simple baseline model for predicting heart disease from patient data, how to load the data, and make some predictions.

In previous discussion I shared my notes on Deep Learning Book Part I: Applied Math and Machine Learning Basics. This is a…

This blog post a continuation to Oh My Guudness….. Machine Learning.

*Please note that this post is for my future self to review the materials on this book without reading it all over again.*

There are many statistical tools that help us to achieve machine learning goal of solving a task not only on training data but also to generalize. Estimators, bias and variance are foundational concepts useful to characterize notions of generalization, underfitting and overfitting.

**Point estimation** is the relation between input and target variables. We want to estimate a parameter. The true value of the parameter : 𝜽…

This blog post is my progress for day 7 in the #100DaysofMLCode. Going back to the basics with ML has helped me to make better sense of what I have learned. In this blog post I will be sharing my notes on Deep Learning Book Chapter 5: Machine Learning Basics. This chapter will be discussed in two parts.

*Please note that this post is for my future self to review the materials on this book without reading it all over again.*

Machine learning is fun, challenging, puzzling, and even a bit scary if you’re one of those people that believe…

This blog post is my progress for day 6 in the #100DaysofMLCode. Going back to the basics with ML has helped me to make better sense of what I have learned. In this blog post I will be sharing my notes on Deep Learning Book Chapter 4: Numerical Computation. I’m definitely feeling the meme above.

*Please note that this post is for my future self to review the materials on this book without reading it all over again.*

Computing large numerical computation is common in ML. For example, Bayesian Statistics often requires computing very large multidimensional integrals.

- Underflow occurs when…

This blog post is my progress for day 5 in the #100DaysofMLCode. Going back to the basics with ML has helped me to make better sense of what I have learned. In this blog post I will be sharing my notes on Deep Learning Book Chapter 3: Probability and Information Theory.

Everything thing we observe around us presents itself to some degree of uncertainty. **Probability theory**, a branch of mathematics concerned with the analysis of random phenomena…

This post is a continuation of my previous post **Scalars, Vectors and Matrices, oh my**.** **We will dive right in and begin with linear dependence and span.

The first thing to ask when are faced with system of linear equations: What is the number of solutions? Three cases can represent the number solutions for system of linear equations **Ax=b. **Can** **there ever be more than one solution and less than a infinite number of solutions?** **No, two lines cannot cross more than once but can be either parallel or superimposed.

**Day 3** and 4 was spent studying the foundational and math side of machine learning. Machine learning can be applied without mathematics. However, applying machine learning is more effective knowing what these systems are doing under the hood. It helps to work through introductory books to gain an understanding of the foundational side of machine learning.

On day day 3 and 4, I read **Deep Learning Chapter 2**: **Linear Algebra** written by Ian Goodfellow. Linear Algebra is everywhere in machine learning and can be seen in the basic materials. It’s widely used throughout engineering and science. Linear algebra is used…

On Friday, July 06, 2018, I embarked on 100 day journey to expand my knowledge and sharpen my machine learning (ML) skills. To start the challenge, I began by setting up all the required resources and tools that I need to complete this challenge and also selected a few projects that gain my interest.

**Day 1** began** **with the Warm Up: Predict Blood Donations dataset. This is a good place to start and to work with a less complex dataset. The goal for this DrivenData challenge is to predict whether or not a donor will give blood the next opportunity…