Given the rise of data science and machine learning as an in-demand career, many people are wondering how to get started as a Data Scientist. Forbes explores how to get started in this article.

Many people are looking to break into data science, from undergraduates to career changers, have asked me how I’ve attained my current data science position at Pacific Life. I’ve referred them to many different resources, including discussions I’ve had on the blog and the Scatter Podcast. In the interest of providing job seekers with a comprehensive view of what I’ve learned that works, I’ve put together the five most valuable lessons learned. I’ve written this article to make your data science job hunt easier and as efficient as possible.

In this follow up video to “How To Build An AI Startup With PyTorch,” the great Siraj Raval explores how to make money with TensorFlow 2.0.

From the video description:

I’ve built an app called NeuralFund that uses Tensorflow 2.0 to make automated investment decisions. I used Tensorflow 2.0 to train a transformer network on time series data that i downloaded using the Yahoo Finance API. Then, I used Tensorflow Serving + Flask to create a simple web app around it. I’ll explain what the important parts you should know in Tensorflow 2.0 are, then I’ll guide you through my code & thought process of building an AI startup using it. Enjoy!

By the way, the code for this video is available on GitHub.

As big data, cloud computing and artificial intelligence are becoming a part of our every day lives and various industries, there is increasing evidence of how these technologies can contribute towards solving globally pressing issues such as deforestation. Here’s an interesting article looking at that very problem.

Advanced technologies such as AI and big data are being leveraged in the fight against deforestation. SilviaTerra is using AI to monitor forests. SilviaTerra has a software-based approach to solve forest inventory problems by assessing forests using satellite imagery and machine learning. The algorithm, powered by AI, is enabling precision forestry at a fraction of time, fieldwork and cost of conventional methods.

Here’s a great explainer video that walks through the Support Vector Machine (SVM) algorithm.

From the video description:

A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes.