Machine Learning Fun and Easy - YouTube


Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.

How machine learning is helping marketers get the edge


Some say marketing is an art. Sure, being creative with your slogans, copywriting and other marketing collateral is important but the'art' component should not extend to areas where it does not belong. Creatively brainstorming your product pricing strategy or your PPC spending is never a good idea - science needs to applied. As data grows bigger, however, new approaches need to be adopted when gathering and transforming that data into meaningful actions. That's where machine learning techniques are making the biggest impact.

Machine learning job: Data Scientist at Homesnap (Bethesda, Maryland, United States)


Data Scientist at Homesnap Bethesda, Maryland, United States (Posted Sep 25 2018) About the company Homesnap is the market-leading national home search platform that provides real-time MLS data to consumers and free leads for agents. The integrated Homesnap platform delivers a superior home search experience to millions of consumers each month, while providing over 875,000 agents with access to powerful, intelligent mobile tools that accelerate their success. Job description With our powerful real estate data, we have the ability to make a huge impact on the industry. We need your analysis, scripting, and data mining skills to help us deliver even better products. Your knowledge of AI, statistics, and algorithms will be essential to our success.

A Beginner's Guide to Neural Networks and Deep Learning


Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated. Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of the data you store and manage. They help to group unlabeled data according to similarities among the example inputs, and they classify data when they have a labeled dataset to train on. What kind of problems does deep learning solve, and more importantly, can it solve yours?

5 Free R Programming Courses for Data Scientists and ML Programmers


The course contains more than 4 hours of content and 2 articles. Its step by step approach is great for beginners and Martin has done a wonderful job to keep this course hands-on and simple. You will start by setting up your own development environment by installing the R and RStudio interface, add-on packages, and learn how to use the R exercise database and the R help tools. After that, you will learn various ways to import data, first coding steps including basic R functions, loops, and other graphical tools, which is the strength of R The whole course should take approx.

A gentle introduction to decision trees using R


Most techniques of predictive analytics have their origins in probability or statistical theory (see my post on Naïve Bayes, for example). In this post I'll look at one that has more a commonplace origin: the way in which humans make decisions. When making decisions, we typically identify the options available and then evaluate them based on criteria that are important to us. The intuitive appeal of such a procedure is in no small measure due to the fact that it can be easily explained through a visual. The tree structure depicted here provides a neat, easy-to-follow description of the issue under consideration and its resolution.

Linear Regression in the Wild


In one of my job interviews for a data scientist position, I was given a home assignment I'd like to share with you. The interviewer sent me a CSV file containing samples of measured quantities x and y, where y is a response variable which can be written as an explicit function of x. It is known that the technique used for measuring x is twice as better than that for measuring y in the sense of standard deviation. Here are all the imports I'll need: It clearly looks like linear regression case. First I'll manually remove the outliers: I'll use LinearRegression to fit the best line: If you're not familiar with the linear regression assumptions, you can read about it in the article Going Deeper into Regression Analysis with Assumptions, Plots & Solutions.

Glossary of Machine Learning Terms


ROC curves are widely used because they are relatively simple to understand and capture more than one aspect of the classification.

Equality Constrained Decision Trees: For the Algorithmic Enforcement of Group Fairness Artificial Intelligence

Fairness, through its many forms and definitions, has become an important issue facing the machine learning community. In this work, we consider how to incorporate group fairness constraints in kernel regression methods. More specifically, we focus on examining the incorporation of these constraints in decision tree regression when cast as a form of kernel regression, with direct applications to random forests and boosted trees amongst other widespread popular inference techniques. We show that order of complexity of memory and computation is preserved for such models and bounds the expected perturbations to the model in terms of the number of leaves of the trees. Importantly, the approach works on trained models and hence can be easily applied to models in current use.

Regularization in Machine Learning: Connect the dots


Following are the various steps we will walk together and try gaining an understanding. In this post, we will consider Linear Regression as the algorithm where the target variable'y' will be explained by 2 features'x1' and'x2' whose coefficients are β1 and β2. First up, lets get some minor prerequisites out of the way in order to understand their use down the line. Optional: Refer Chapter 3 in the link below to gain understanding about Linear Regression. In Fig 1(a) below, Gradient Descent is represented in 3-dim.