17 Nov Machine Learning Regression Masterclass in Python

The age of artificial intelligence (AI) has arrived! The technology is advancing rapidly and is finding widespread application in the healthcare, defense, banking, gaming, transportation, and robotics industries. Machine Learning is a subfield of Artificial Intelligence that allows machines to learn and improve at a given task over time. Machine Learning is a hot topic, with a steady increase in demand for experienced machine learning engineers and data scientists over the last five years. According to a report published by Research and Markets, the global AI and machine learning technology sectors are expected to grow from $1.4 billion to $8.8 billion by 2022, with the AI tech sector expected to create approximately 2.3 million jobs by 2020.

The goal of this course is to teach students key aspects of machine learning regression techniques in a practical, easy, and enjoyable manner. Regression is a useful machine learning technique that predicts a continuous (dependent) variable based on a number of other independent variables. Regression strategies are commonly used to forecast the stock market, analyse real estate trends, and create targeted marketing campaigns.

The course gives students hands-on experience training machine learning regression models with real-world datasets. This course teaches several techniques in a hands-on manner, including:

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Logistic Regression
• Decision trees regression
• Ridge Regression
• Lasso Regression
• Artificial Neural Networks for Regression analysis
• Regression Key performance indicators

The course is designed for students who want to learn the fundamentals of machine learning regression models. A basic understanding of programming is recommended. However, because these topics will be thoroughly covered during the first few course lectures, the course has no prerequisites and is open to any student with basic programming knowledge. Students who take this course will learn how to use machine learning regression models and how to apply these skills to solve real-world problems.

What Will You Learn From Our Machine Learning Regression Masterclass In Python Experts

• Understand Python programming and Scikit learn as they relate to machine learning regression.
• Understand the theory underlying simple and multiple linear regression techniques.
• Predict product sales volume and vehicle fuel economy using simple linear regression techniques.
• Predict stock prices and university acceptance rates using multiple linear regression.
• Explain the fundamentals and underlying theory of polynomial regression.
• Use polynomial regression to forecast employee salaries and commodity prices.
• Learn about the theory behind logistic regression.
• Using customer features, use logistic regression to predict the likelihood that a customer will purchase a product on Amazon.
• Learn about the underlying theory and mathematics of Artificial Neural Networks.
• Learn how to train network weights and biases and how to choose the appropriate transfer functions.
• Back propagation and gradient descent methods are used to train Artificial Neural Networks (ANNs).
• To improve network performance, optimise ANN hyper parameters such as the number of hidden layers and neurons.
• Use ANNs to forecast house prices based on parameters such as size, number of rooms, and so on.
• KPI (Key Performance Indicators) such as Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error intuition, R-Squared intuition, Adjusted R-Squared intuition, and F-Test are used to evaluate the performance of trained Machine Learning models.
• Learn about the theory and intuition behind the Lasso and Ridge regression techniques.
• Real-world, practical project examples

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