Introduction

In this project tutorial, we are going to create two simple machine learning applications in Python 3.5+ using two totally different libraries: Scikit-learn and Keras. We’ll cover all the details, including resources, tools, and languages, that are necessary to build a Python machine learning model—starting from the basics such as setting up the right tools and frameworks to more advanced topics related to the development. In this classification problem, our model will be trained to classify the dataset from the open Hedge Fund, which can be accessed at www.numer.ai. The dataset consists of 21 features and 2 categories or classes. This problem is complex because the data we’ll be trying to predict comes from the volatile stock market data. Before diving into the main task, we’ll see how a “Hello World” in machine learning looks like. After that, we’ll learn how to use numer.ai and their pre-defined datasets. After brute forcing the framework and method parameters, you’ll gain enough skills to create your own machine learning model.

What are the requirements? To speed up your learning in this project, you need the following:

Basic skills in Python 3.5+

Basic working knowledge of Jupyter Notebook, PyCharm ID, or Spyder3 IDE

Stable Internet connection

A lot of curiosity for machine learning

Who is the target audience?

Do you want to start a career in the exciting world of machine learning?

Do you want to build simple machine learning models?

Do you want to experience how to use Scikit-learn and Keras machine learning libraries?

Do you want to know the parameters of a ML-model you can tweak to get optimized results?

What will you learn after finishing this project?

How to create your own machine learning model for predicting outcomes

How to use the Scikit-learn and Keras Python-based machine learning libraries

How to use Python for authoring simple machine learning models

How to create and apply the best machine learning practices for your specific use cases

When are the streaming sessions (streaming schedule)?

Weekly, 3:00 pm EST on Saturdays and Sundays