I have also linked my other profiles and my recent achievements. If you scroll down, you will see my most starred projects and contribution info. I have been approached by CEOs, recruiters, startup founders, students, and researchers through GitHub. Most of them want to know more about my project and how they can change it for the pacific data application. Kaggle is the super-platform for data scientists and machine learning engineers.

The list of 10 cheat sheets is for beginners, students, job seekers, and professionals. These are my favorite, and they are hand-picked so that you don't have to search for the best cheat sheet for every subcategory of data science. The cheat sheets are life savers. It has helped me multiple times when I was preparing for data science and machine learning interviews. It just took me 30 minutes to review all of the old but necessary concepts and prepare for any technocal question.

The world of data science is still new as compared to other software-related fields, and it doesn't have a gold standard on what skills you need to acquire to be called a professional data scientist. This is where DataCamp certification comes in to access your knowledge and skills. Just like in the world of computer networks, the Cisco certification is a gold standard. Similarly, DataCamp is accessing an individual's skills by conducting various challenges. During the Certificate Challenge, I was a professional data scientist working with various companies on various projects.

Over the past few months, I have been collecting AI cheat sheets. From time to time I share them with friends and colleagues and recently I have been getting asked a lot, so I decided to organize and share the entire collection. To make things more interesting and give context, I added descriptions and/or excerpts for each major topic. This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it. Scikit-learn (formerly scikits.learn) is a free softwaremachine learninglibrary for the Python programming language.

Deep learning is a machine learning method that uses neural networks for prediction tasks. Deep learning methods can be used for a variety of tasks including object detection, synthetic data generation, user recommendation, and much more. In this post, I will walk through some of the best resources for getting started with deep learning. There are several online resources that are great for getting started with deep learning. Sentdex is a YouTube channel, run by Harrison Kinsley, that has several tutorials on how to implement machine learning algorithms in python.

Thank you for visiting this site. I am currently with PyImageSearch where I apply deep learning to solve real-world problems in computer vision and bring some of the solutions to edge devices. I am also responsible for providing Q&A support to PyImageSearch readers. Previously at DataCamp, I developed projects for DataCamp Project. My DataCamp projects Predicting Credit Card Approvals and Analyze International Debt Statistics are now launched and so is my DataCamp practice pool Advanced Deep Learning with Keras in Python (I created exercises for DataCamp Practice too).

One of my favorite places to learn data science is an under-the-radar educational website, DataCamp. DataCamp doesn't get nearly the attention that some of the larger, more well-funded online coding schools get, but, I often find myself on one of their tutorials whenever I'm learning something new related to statistics or machine learning. Over the past few months, I've dedicated at least a few hours a week to learning the underpinnings of automation and, where I find something interesting, to blog about my experience. Unlike almost every other school or tutorial I've encountered, DataCamp has a delightfully distinct and powerful approach to education: every single piece of instruction is paired with a simple example and interactive tutorial. There are no long lectures; there are no complicated diagrams.

For analytic inference, we will learn to run chi-squared tests. Of course not all survey data are categorical and so in this chapter, we will explore analyzing quantitative survey data. We will learn to compute survey-weighted statistics, such as the mean and quantiles. For data visualization, we'll construct bar-graphs, histograms and density plots. We will close out the chapter by conducting analytic inference with survey-weighted t-tests. To model survey data also requires careful consideration of how the data were collected. We will start our modeling chapter by learning how to incorporate survey weights into scatter plots through aesthetics such as size, color, and transparency. We'll model the survey data with linear regression and will explore how to incorporate categorical predictors and polynomial terms into our models.

Here I will be posting some of the data science and machine learning projects that I have been working on. The main motivation for making this blog is that I will soon be starting the Fast AI Deep Learning course. Blogging along with the lectures seemed like a great opportunity for me to be really hands-on with the material and get acquainted with other students. Let me first start by giving you a quick background of my journey into data science. About a year ago I started writing my master thesis for the study Business Administration.

If you're reading this, you already know (or could reasonably conclude by powers of deduction) that we (Yhat) have a blog. The tagline of our blog is simple, machine learning, data science, engineering. Those are the things our team writes about a few times a week. We like to think we have some pretty good ideas (flying a semi-autonomous drone around the office, for example), but those ideas are really just some combination of the team's thoughts, our reader's ideas, and what we read and steal from all of our favorite bookmarked sites/newsletters/blogs and community forums. In the words of Austin Kleon (a very cool writer/artist), "Every new idea is just a mashup or a remix of one or more previous ideas."