Lean ML: Beginner's Guide to Machine Learning with Python

Name: Lean ML: Beginner's Guide to Machine Learning with Python
Rating: 5.0 (1 reviews)

The easiest way to learn machine learning without going back to college.

Created byOliver Theobald

Last updated 1/2024

English

What you'll 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

Course content

1 section • 26 lectures • 1h 35m total length

Introduction0:48
Hello and welcome to Lean ML. My name is Oliver and I‘m the author of the book Machine Learning for Absolute Beginners.
In this course, I'll teach and reveal to you my personal framework for adding machine learning to your skillset. The whole goal of the course is to take you from absolutely zero, to coding on your own machine learning model from start to finish.
By the end of the course, you'll be able to download a free dataset and use 20 or so lines of code to build your own machine learning model using the step-by-step method taught in this course.
My Lean ML method provides you with the minimum effective dose of theory (explained in simple terms), reinforced with practical demonstrations--all without complex math and boring datasets.
Each video is placed in order to guide you through your transformation starting from zero.
Both the coding and datasets start off simple. These easy wins not only help to build up your confidence but also set you up for tackling progressively higher levels of difficulty that will test and strengthen your machine learning muscles.
If you're tried to study machine learning before failed to implement or retain it as a skillset, this is the course for you.
Ok, so let’s get started!
What is Machine Learning?7:36
Discover how machine learning lets computers predict from data, not explicit instructions. See how models differ from algorithms and traditional programming, with examples like search and recommendations.
The ML Ecosystem3:04
X & y Variables1:49
Learn how machine learning uses independent variables X and a dependent variable y to predict outcomes from data, building a model that estimates values like house prices from features.
Model Overview0:57
Feature Selection3:57
Explore feature concepts and variable types in a data set, distinguishing continuous and discrete features, and understanding independent and dependent variables in a practical ad-targeting example.
Data Scrubbing3:30
Training & Test Data1:39
Self-learning Algorithms13:07
Classification & Regression2:50
Explore the difference between regression and classification in supervised learning, including linear regression outputs and category predictions, with examples like predicting sales and spam detection.
Introduction to Machine Learning Libraries5:03
Working in Jupyter Notebook2:55
Install your Python development environment with Anaconda to run Jupyter Notebook for building machine learning models, then launch a new Python 3 notebook in your browser.
Evaluation4:37
Linear Regression7:30
Logistic Regression2:41
Support Vector Machines4:07
Bias & Variance3:37
k-Means Clustering3:02
k-Nearest Neighbors2:15
Decision Trees2:24
Random Forests2:12
Random forests combine multiple decision trees to improve accuracy and stability through ensemble learning, using bootstrap sampling and random feature selection for robust regression or classification.
Gradient Boosting3:56
Gradient boosting uses sequential weak learners, weighting each new tree to correct previous mistakes. This weighting yields stronger predictions and often higher accuracy than random forests.
Introducing the Class Project2:04
Downloading the Dataset1:01
Explore how to download the advertising dataset from Kaggle, including registering for a free account, downloading the CSV, and unzipping it to prepare data and explore visualizations of variables.
Python Exercise6:25
Next Steps2:17

Requirements

None

Description

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!

Who this course is for:

Beginners and anyone curious about machine learning and AI