Lean ML- Beginner Guide to Machine Learning with Python

Lean ML: Beginners Guide to Machine Learning with Python, available at $54.99, has an average rating of 5, with 26 lectures, based on 1 reviews, and has 10 subscribers.

You will learn about Essential machine learning theory Must know pros and cons of common ML algorithms Learn how to obtain free datasets on any topic Code your first prediction model in Python This course is ideal for individuals who are Beginners and anyone curious about machine learning and AI It is particularly useful for Beginners and anyone curious about machine learning and AI.

Enroll now: Lean ML: Beginners Guide to Machine Learning with Python

Summary

Title: Lean ML: Beginners Guide to Machine Learning with Python

Price: $54.99

Average Rating: 5

Number of Lectures: 26

Number of Published Lectures: 26

Number of Curriculum Items: 26

Number of Published Curriculum Objects: 26

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Essential machine learning theory

Must know pros and cons of common ML algorithms

Learn how to obtain free datasets on any topic

Code your first prediction model in Python

Who Should Attend

Beginners and anyone curious about machine learning and AI

Target Audiences

Beginners and anyone curious about machine learning and AI

Don’t know where to begin with machine learning? Getting lost in complex equations and dense theory?

Master the fundamentals of machine learning with ease!

Whether you’re coming from a background in mobile and web development, business analysis, engineering, or a university student, your journey into this exciting and complex field starts with learning the fundamentals.

In this online video course, I will teach you the basics of machine learning. I’ll walk you through the fundamental concepts, algorithms,and terms without overwhelming you in advanced math and lines and lines of code.

After completing this online course you can confidently go on to more complex learning resources on other learning platforms or maybe a general understanding of machine learning is enough to satisfy your needs for now. I’ll also provide recommendations for further learning resources at the end of the course.

Class requirements

This online class has no requirements and while we will use the programming language Python as part of our code exercises, you do not need a background in coding to complete the project or the content covered in this course. Some knowledge of coding, however, would be beneficial to your understanding of later sections.

See you in the first video!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: What is Machine Learning?

Lecture 3: The ML Ecosystem

Lecture 4: X & y Variables

Lecture 5: Model Overview

Lecture 6: Feature Selection

Lecture 7: Data Scrubbing

Lecture 8: Training & Test Data

Lecture 9: Self-learning Algorithms

Lecture 10: Classification & Regression

Lecture 11: Introduction to Machine Learning Libraries

Lecture 12: Working in Jupyter Notebook

Lecture 13: Evaluation

Lecture 14: Linear Regression

Lecture 15: Logistic Regression

Lecture 16: Support Vector Machines

Lecture 17: Bias & Variance

Lecture 18: k-Means Clustering

Lecture 19: k-Nearest Neighbors

Lecture 20: Decision Trees

Lecture 21: Random Forests

Lecture 22: Gradient Boosting

Lecture 23: Introducing the Class Project

Lecture 24: Downloading the Dataset

Lecture 25: Python Exercise

Lecture 26: Next Steps

Instructors

Oliver Theobald
Machine Learning Author & Instructor

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Frequently Asked Questions

How long do I have access to the course materials?

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

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!