In this The Machine Learning Series in Python: Level 1 Course you will master the foundations of Machine Learning and practice building ML models with real-world case studies. We will start from scratch and explain: What Machine Learning is, The Machine Learning Process of how to build a ML model, Regression: Predict a continuous number, Simple Linear Regression, Ordinary Least Squares, Multiple Linear Regression, R-Squared, Adjusted R-Squared. We will also do the following the three following practical activities: Real-World Case Study: Build a Multiple Linear Regression model, Real-World Case Study: Build a Logistic Regression model, Real-World Case Study: Build a K-Means Clustering model.

This article will share insights on how to build lean startups that change society for the better and leave a positive impact on the planet. There are hundreds of use cases where AI can help to do exactly this. Impact-driven startups have the potential to solve real-world problems, tackle environmental problems, and improve the lives of many people, especially vulnerable populations. Billions of dollars are already flowing into AI ventures, which are primarily addressing profit gains and industrial automation. The AI for Good movement where often commercial meets social value is slowly picking up. Now, in order to build impact-driven AI startups, there are a few essential steps to follow.

The boom of deep learning induced many industries and academies to introduce machine learning based approaches into their concern, competitively. However, existing machine learning frameworks are limited to sufficiently fulfill the collaboration and management for both data and models. We proposed NSML, a machine learning as a service (MLaaS) platform, to meet these demands. NSML helps machine learning work be easily launched on a NSML cluster and provides a collaborative environment which can afford development at enterprise scale. Finally, NSML users can deploy their own commercial services with NSML cluster. In addition, NSML furnishes convenient visualization tools which assist the users in analyzing their work. To verify the usefulness and accessibility of NSML, we performed some experiments with common examples. Furthermore, we examined the collaborative advantages of NSML through three competitions with real-world use cases.