Machine Learning Model: Python Sklearn & Keras

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Session 2: Cheatsheets and different Machine Learning Problems

with AndreyBu|5 years experience|Python

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Machine Learning Model: Python Sklearn & Keras

Machine learning (ML) is one of the liveliest fields of artificial intelligence (AI) that is causing major disruptions in our world today. And, to sink your feet deeper in the field, you need to know how to build machine learning models that can accurately predict outcomes. Two of the most popular Python libraries for building machine learning models are Scikit-learn and Keras. In this hands-on project tutorial, you’ll learn how to work with the two libraries in building amazing models for solving various machine learning problems. This is the practical project you need to take your machine learning career to the next level.

Overview

Creating a machine learning application for a hedge fund

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

Project Outline

Session 1: ML Models: Setting up Environment (Anaconda, IDEs, etc.)

Setting up the programming environment for Windows (Installing Python 3.5+, PyCharm, Spyder3, and Jupyter)
Taking a look at different ML-libraries for Python
Going through various methods and algorithms present in Scikit-learn, Keras, and Pandas

Session 2: Cheatsheets and different Machine Learning Problems

Python 3.5+
Scikit-learn, Keras, Pandas, and Numpy
First Steps with Pandas and Numpy; visualizing and exploring our datasets
Visualizing data with Orange

Session 3: Features, Targets, Pandas and Data

Use our labeled data to build an ML model
Get the accuracy of the model
Optimize its parameters for better results

Session 4: Data Mining

What is Numer.ai and how does it work
What does the dataset represent?
Explorative analysis and visualization of the dataset

Session 5: Hello World in Machine Learning Part

Session 5.1: Hello World in Machine Learning Part 1/3

Most common and basic ("Hello World") example in the field on machine learning

Session 5.2: Hello World in Machine Learning Part 2/3

Using a UI Tool "Orange" to perform Machine Learning tasks with a simple Drag&Drop.
You can perform complex Data Analytics with Orange without knowing how to code.

Session 5.3: Hello World in Machine Learning Part 3/3

Splitting Data
Confusion Matrix

Session 6: Introduction into the Numerai Network/Project

Explanation of real-world Machine Learning Challenge
The greater your ML Model Accuracy/Score, the more money you earn

Session 7: First steps into Numerai and Kaggle

Trying to read Numerai Dataset.

Session 8: Basic Understanding of Class separation

Explanation of Class Separation
Visualization of class separations

Session 9: Basic Comparison between SVM and Neural Nets

Explanation of low-level computation/equations of a neural network
Underlying structure of a neural network (biases, neurons, weights, etc.)

Session 10: Recognizing Handwritten Digits with Neural Nets

import MNIST Dataset
Preparing Handwritten Digit Recognition

Session 11: Fitting Machine Learning Model in Sklearn

Use Data to train our ML Classifier
First insights about stock market trading

Session 12: Using Stock Exchange Data in Machine Learning

Importing stock market data from Yahoo
First look, and visualizing stock data

Session 13: Finalizing The Project

Creating MLPClassifier
Final thoughts on Machine Learning and Stock Market Trading

Tools: Orange, Jupyter. PyCharm, Anaconda