Machine Learning Regression Masterclass in Python
Build 8+ Practical Projects and Master Machine Learning Regression Techniques Using Python, Scikit Learn and Keras
Artificial Intelligence (AI) revolution is here! The technology is progressing at a massive scale and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries.
Machine Learning is a subfield of Artificial Intelligence that enables machines to improve at a given task with experience. Machine Learning is an extremely hot topic; the demand for experienced machine learning engineers and data scientists has been steadily growing in the past 5 years. According to a report released by Research and Markets, the global AI and machine learning technology sectors are expected to grow from $1.4B to $8.8B by 2022 and it is predicted that AI tech sector will create around 2.3 million jobs by 2020.
The purpose of this course is to provide students with knowledge of key aspects of machine learning regression techniques in a practical, easy and fun way. Regression is an important machine learning technique that works by predicting a continuous (dependant) variable based on multiple other independent variables. Regression strategies are widely used for stock market predictions, real estate trend analysis, and targeted marketing campaigns.
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What you’ll learn
- Master Python programming and Scikit learn as applied to machine learning regression
- Understand the underlying theory behind simple and multiple linear regression techniques
- Apply simple linear regression techniques to predict product sales volume and vehicle fuel economy
- Apply multiple linear regression to predict stock prices and Universities acceptance rate
- Cover the basics and underlying theory of polynomial regression
- Apply polynomial regression to predict employees’ salary and commodity prices
- Understand the theory behind logistic regression
- Apply logistic regression to predict the probability that customer will purchase a product on Amazon using customer features
- Understand the underlying theory and mathematics behind Artificial Neural Networks
- Learn how to train network weights and biases and select the proper transfer functions
- Train Artificial Neural Networks (ANNs) using back propagation and gradient descent methods
- Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance
- Apply ANNs to predict house prices given parameters such as area, number of rooms..etc
- Assess the performance of trained Machine learning models using KPI (Key Performance indicators) such as Mean Absolute error, Mean squared Error, and Root Mean Squared Error intuition, R-Squared intuition, Adjusted R-Squared and F-Test
- Understand the underlying theory and intuition behind Lasso and Ridge regression techniques
Sample real-world, practical projects
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