Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 48000 Hz, 2ch | Size: 1.91 GB
Genre: eLearning Video | Duration: 44 lectures (5 hour, 19 mins) | Language: English
Machine learning basics, mathematically learn algorithms, algorithms using python from scratch and sklearn.
What you'll learn
Learn the Basics of Machine learning
Implement linear regression, polynomial regression, regularization, logistic regression using python from scratch and sklearn library
Linear Regression and mathematics behind linear regression
Polynomial regression and mathematics
Gradient descent technique
Ridge and Losso Regression
Bias - Variance Trade off and regularization
Logistic regression and mathematics behind logistic regression
Requirements
Basic Python
Basic Mathematical operations on matrix
Spyder IDE, Python, SKlearn installed in the computer.
Description
This course is for you if you are looking for the basics of machine learning.
If you want to know how to implement the linear regression, polynomial regression and logistic regression using python without using sklearn and understand these algorithms mathematically?
In this course you will learn the mathematics behind the linear regression, polynomial regression and logistic regression. Then you will implement these algorithms without using sklearn and using sklearn.
The course has the following topics
Section 1: Fundamentals of machine learning.
What is machine learning?,
When to use machine learning.
Supervised and unsupervised algorithms, Regression, classification and clustering
Section 2: Linear Regression
Linear Regression using normal equation
Implementing Simple linear regression, multiple linear regression using normal equation.
Model accuracy.
Implement linear regression using sklearn
Section 3: Linear regression using Gradient Descent
Explanation of Gradient descent and using the gradient descent to find the parameters.
Different types of gradient descent.
Python code for gradient descent without sklearn.
Python code for gradient descent using sklearn
Section 4: Polynomial regression
What is polynomial regression and when to use the polynomial regression.
Implement polynomial regression using python
Section 5: Bias and Variance
Understanding the bias and variance.
Effect of bias and variance on model accuracy.
Implementing regularisation to overcome variance.
Section 6: Logistic regression
What is logistic regression
Sigmoid function
Maximum likelihood estimation
Implementing gradient ascent to find the parameter values
Python code for logistic regression without sklearn
Python code for logistic regression with sklearn
Evaluating the model performance
Who this course is for:
Beginner to Machine Learning
Those willing to understand maths behind linear regression, logistic regression.
Homepage: https://www.udemy.com/course/machine-learning-a-beginners-guide/
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