Introduction to Machine Learning with Python

Introduction to Machine Learning with Python, available at Free, has an average rating of 3.2, with 24 lectures, based on 5 reviews, and has 1203 subscribers.

You will learn about Learn how gradient descent optimizes the parameters of a machine learning model Fit a linear regression model to a dataset to predict the win rate of a MLB baseball team Train a decision tree and random forest model to predict the credit risk of a borrower Learn how k-means clustering works and apply it to a dataset of news headlines This course is ideal for individuals who are Data Scientist looking to further their machine learning knowledge or Beginner ML Engineers aiming to break into machine learning It is particularly useful for Data Scientist looking to further their machine learning knowledge or Beginner ML Engineers aiming to break into machine learning.

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Summary

Title: Introduction to Machine Learning with Python

Price: Free

Average Rating: 3.2

Number of Lectures: 24

Number of Published Lectures: 24

Number of Curriculum Items: 24

Number of Published Curriculum Objects: 24

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Learn how gradient descent optimizes the parameters of a machine learning model
Fit a linear regression model to a dataset to predict the win rate of a MLB baseball team
Train a decision tree and random forest model to predict the credit risk of a borrower
Learn how k-means clustering works and apply it to a dataset of news headlines

Who Should Attend

Data Scientist looking to further their machine learning knowledge
Beginner ML Engineers aiming to break into machine learning

Target Audiences

Data Scientist looking to further their machine learning knowledge
Beginner ML Engineers aiming to break into machine learning

This course is designed to provide a thorough introduction to the world of machine learning. This course is perfect for beginners and those looking to enhance their data science skills using Python.

Section 1: Introduction to Machine Learning In this section, we will explore the fundamentals of machine learning. We’ll start by defining machine learning and understanding its significance in today’s data-driven world. We’ll walk through a simple example, such as finding the line of best fit, to illustrate core concepts. Key topics like cost functions and the optimization technique of gradient descent will be covered, along with understanding the importance of the learning rate.

Section 2: Regression Regression analysis is a powerful tool for predicting continuous outcomes. We’ll dive into different regression models and learn how to evaluate their performance. You’ll gain hands-on experience by exploring datasets and fitting both linear and multiple regression models. This section ensures a solid foundation for understanding how regression works and how to apply it effectively.

Section 3: Classification Classification techniques are essential for predicting categorical outcomes. We’ll begin by explaining what classification is and introducing logistic regression. You’ll work with datasets to fit logistic regression models and understand their applications. The section also covers advanced techniques like decision trees and random forests, providing a comprehensive understanding of various classification methods.

Section 4: Clustering Clustering helps in grouping data points with similar characteristics. We’ll focus on the K-means clustering algorithm, starting with an overview of the method. You’ll learn how to explore datasets and fit clustering models to uncover hidden patterns and insights within your data.

By the end of this course, you’ll be equipped with practical skills and knowledge to implement machine learning models using Python, empowering you to tackle real-world data challenges with confidence.

Course Curriculum

Chapter 1: Introduction to Machine Learning

Lecture 1: What Is Machine Learning?

Lecture 2: Line of Best Fit

Lecture 3: Cost Function

Lecture 4: Gradient Descent

Chapter 2: Regression Models

Lecture 1: What Is Regression?

Lecture 2: Exploring Our Dataset

Lecture 3: Fitting a Simple Linear Regression Model

Lecture 4: Fitting a Multiple Regression Model

Lecture 5: Notebook & Dataset for Regression Section

Chapter 3: Classification Models

Lecture 1: What Is Classification?

Lecture 2: Introducing Logistic Regression

Lecture 3: Classification Dataset Overview

Lecture 4: Fitting Our Logistic Regression Model

Lecture 5: Evaluating Our Logistic Regression Model

Lecture 6: German Credit Dataset

Lecture 7: Logistic Regression Notebook

Lecture 8: What Are Decision Trees?

Lecture 9: Training a Decision Tree Classifier

Lecture 10: Random Forest

Lecture 11: Decision Tree & Random Forest Notebook

Chapter 4: Clustering

Lecture 1: Read Before Beginning Section

Lecture 2: Clustering Notebook

Lecture 3: K-means Clustering Explained

Lecture 4: Fitting Our K-means Clustering Model

Instructors

Ingenium Academy
#1 place for math & science education online.

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4 stars: 2 votes
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Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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