If you ask any successful Kaggler what tips they have to improve your data science skill set, they all have the same answer. They will tell you to study the top solutions of completed Kaggle competitions. Kaggle is a platform for data science competitions for various types of problems. Competitors compete by building Machine Learning models and submitting their predictions. The competitor with the most accurate predictions takes home a prize.

Getting started with data science can be a confusing journey, especially if the person is not from the STEM field. In this article, I explore and define the essential aspects of data science you need to get started correctly. This article will mainly tackle the technical skills required for a data scientist. To become a data scientist, you need to be familiar with programming, statistics, and machine learning. This article will outline the steps you can take to become a data scientist and the important libraries you need to know.

You've probably heard of Kaggle data science competitions, but did you know that Kaggle has many other features that can help you with your next machine learning project? For people looking for datasets for their next machine learning project, Kaggle allows you to access public datasets by others and share your own datasets. For those looking to build and train their own machine learning models, Kaggle also offers an in-browser notebook environment and some free GPU hours. You can also look at other people's public notebooks as well! Other than the website, Kaggle also has a command-line interface (CLI) which you can use within the command line to access and download datasets.

Welcome to Feature Selection for Machine Learning, the most comprehensive course on feature selection available online. In this course, you will learn how to select the variables in your data set and build simpler, faster, more reliable and more interpretable machine learning models. Who is this course for? You've given your first steps into data science, you know the most commonly used machine learning models, you probably built a few linear regression or decision tree based models. You are familiar with data pre-processing techniques like removing missing data, transforming variables, encoding categorical variables.

Welcome to Feature Engineering for Machine Learning, the most comprehensive course on feature engineering available online. In this course, you will learn how to engineer features and build more powerful machine learning models. Who is this course for? So, you've made your first steps into data science, you know the most commonly used prediction models, you perhaps even built a linear regression or a classification tree model. At this stage you're probably starting to encounter some challenges - you realize that your data set is dirty, there are lots of values missing, some variables contain labels instead of numbers, others do not meet the assumptions of the models, and on top of everything you wonder whether this is the right way to code things up.

Welcome to Feature Selection for Machine Learning, the most comprehensive course on feature selection available online. In this course, you will learn how to select the variables in your data set and build simpler, faster, more reliable and more interpretable machine learning models. Who is this course for? You've given your first steps into data science, you know the most commonly used machine learning models, you probably built a few linear regression or decision tree based models. You are familiar with data pre-processing techniques like removing missing data, transforming variables, encoding categorical variables.

Feature Engineering for Machine Learning Feature Engineering is a Representation Problem. Machine learning algorithms learn a solution to a problem from sample data. Welcome to Feature Engineering for Machine Learning, the most comprehensive course on feature engineering available online. In this course, you will learn how to engineer features and build more powerful machine learning models. Who is this course for?

AI transformation is the next phase of digital transformation. Businesses are willing to invest in AI technologies to stay ahead of competitors. AI Transformation is a process that may take 2-3 years, but organizations can start to see the returns within 6 to 12 months. Digital transformation is required before companies can start their AI transformation because digital data is necessary for AI training and digital processes are necessary to roll-out AI solutions in most cases. Feel free to read about what digital transformation is and our extensive digital transformation guide if you believe that your company has not yet progressed on its digital transformation journey.

Python or R – which tool should you learn? If I got a penny each time I came across this question.. There is a widely held belief that mastering data science is about learning how to apply techniques in Python or R. Or any other tool. That tool has become the central point around which all other data science functions revolve. The assumption (or myth) is that being able to write code using existing libraries (numpy, scikit-learn, caret, etc.) should be enough to label yourself an expert.

As any data scientist is probably familiar, there exists a vast world of predictive modelling competitions on the internet; the most well-known competitions likely being those from Kaggle. Some of these competitions are incentivized financially, others just by curiosity. The basic structure for nearly all of the financially incentivized competitions is that all participants (or teams of participants) train and develop models on a training dataset and send in their submissions as predictions on a test dataset of which the labels are hidden from the competitors. Subsequently, the'best' models are awarded their respective chunk of the financial reward based on some predetermined accuracy metric. Most opinions regarding these predictive modelling competitions are that competition based on financial incentive creates demand for brilliant people to discover solutions to hard problems.