h264, yuv420p, 1280×720 | English, aac, 48000 Hz, 2 channels, s16 | 10h 20mn | 4.9 GB
Instructors: Dr. Ryan Ahmed, Kirill Eremenko, Mitchell Bouchard

Apply ANNs to predict house prices given parameters such as area, number of rooms.

Build 8+ Practical Projects and Master Machine Learning Regression Techniques Using Python, Scikit Learn and Keras
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
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
Machine Learning basics
PC with Internet connetion
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.
The course provides students with practical hands-on experience in training machine learning regression models using real-world dataset. This course covers several technique in a practical 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 targeted towards students wanting to gain a fundamental understanding of machine learning regression models. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master machine learning regression models and can directly apply these skills to solve real world challenging problems.
Who this course is for:
Data Scientists who want to apply their knowledge on Real World Case Studies

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