Machine Learning Fundamentals [Python]

Name: Machine Learning Fundamentals [Python]
Rating: 4.3 (190 reviews)

Understanding Machine Learning for Data Science in python. Best skill to get in free time.

Created byRishi Bansal

Last updated 7/2020

English

English [Auto],

What you'll learn

What is basically Machine Learning? How Machine Learns?
Learn the most Basic Mathematics behind Simple Linear Regression and its Best fit line.
What is Gradient Descent, how it works Internally with full Mathematical explanation.

Course content

2 sections • 9 lectures • 59m total length

What is Machine Learning?11:41
Explore how machine learning turns input-output examples into a mathematical model, from linear regression to predicting future data, while clarifying differences among artificial intelligence, machine learning, and data science.
Types of Machine Learning7:25
Explore the three main types of machine learning—supervised, unsupervised, and reinforcement learning—along with association rule examples, using labeled data, clustering, market basket analysis, and chess-style rewards.
Supervised Learning8:52
Explore supervised learning by distinguishing inputs and labels, training with features to predict outputs, and applying regression and classification tasks such as house prices and spam detection.

Best Fit Line6:40
Explore regression by treating predicting a continuous value, like a stock price, as a supervised task. Minimize squared errors (least squares) to derive the best fit line.
Simple Linear Regression: Concept5:38
Define the best fit line and regression equation for simple linear regression, use it to predict future values, compare univariate and two-variable cases, and minimize the cost function.
Ordinary Least Square(OLS)3:05
Minimize the cost function, the sum of squared differences between predicted and actual values, using gradient descent or ordinary least squares to fit a line.
Gradient Descent: Mathematics Behind it8:58
Master gradient descent by applying derivatives and partial derivatives to minimize the cost function in simple linear regression, updating the parameter m iteratively toward the minimum.
Gradient Descent: Learning Rate3:41
Explore how the gradient descent learning rate controls step size to reach a minimum: too large causes overshoot and oscillation, too small slows convergence, so choose an appropriate rate.
Gradient Descent: Algorithm3:49
Explore the gradient descent algorithm, apply learning rate to minimize a cost function, and update parameters iteratively via derivatives with simultaneous equations.

Requirements

For Machine Learning Concept no prerequisite. Anyone can do this course.
Understanding of Data Preprocessing is required for Coding.
After completing this course, you can connect to me on my blog for any question.
Python is required to do the coding part

Description

This course is designed to understand basic Concept of Machine Learning. Anyone can opt for this course. No prior understanding of Machine Learning is required. Simple Linear Regression Concepts are covered in detail. Coding part is not covered, however wherever possible I have attached the code in the resources.

Now question is why this course?

This Course will not only teach you the basics of Machine learning and Simple Linear Regression. It will also cover in depth mathematical explanation of Cost function and use of Gradient Descent for Simple Linear Regression. Understanding these is must for a solid foundation before entering into Machine Learning World. This foundation will help you to understand all other algorithms and mathematics behind it.

Who this course is for:

Anyone who is looking or dont know from where to start Machine Learning can opt for this course.
This will provide a good foundation in understanding concept of Machine Learning.

Machine Learning Fundamentals [Python]

What you'll learn

Explore related topics

Course content

Introduction to Machine Learning3 lectures • 28min

Supervised Learning: Regression6 lectures • 32min

Requirements

Description

Who this course is for: