Machine Learning
A 17-part video course that introduces classical machine learning with Python, covering regression, classification, and the tools used to build models.
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Now playing: Introduction to Machine Learning for Beginners [Part 1] | Machine Learning for Beginners
This learning path follows the Machine Learning for Beginners video series from Microsoft Developer, presented by Bea Stollnitz. It starts with the fundamentals and history of machine learning, then moves into hands-on work with regression and logistic regression models. You will use Jupyter Notebooks in Visual Studio Code and libraries such as scikit-learn, NumPy, Pandas, and Matplotlib. The course is based on Microsoft's free, open source ML For Beginners curriculum. No prior machine learning experience is required, though some familiarity with Python will help.
Beginners who want a structured free introduction before deeper practice.
Lesson 1: Introduction to Machine Learning for Beginners [Part 1] | Machine Learning for Beginners
Bea Stollnitz introduces the course and the fundamentals of classical machine learning. The lesson covers what machine learning is and how it can be applied to solve problems.
Open on YouTubeLesson 2: The history of Machine Learning [Part 2] | Machine Learning for Beginners
This lesson looks at the history of machine learning and artificial intelligence. It covers key milestones and challenges that shaped the field.
Open on YouTubeLesson 3: Techniques for Machine Learning [Part 3] | Machine Learning for Beginners
The lesson walks through the process of building machine learning models and the techniques involved. It also covers how to decide whether AI is the right approach for a given problem.
Open on YouTubeLesson 4: Introduction to Regression models for Machine Learning [Part 5] | Machine Learning for Beginners
An introduction to regression models, which are used to investigate relationships between variables. The lesson explains where regression fits in machine learning.
Open on YouTubeLesson 5: Set up Jupyter Notebooks to start building regression models [Pt 6] | Machine Learning for Beginners
This tutorial walks through setting up a Jupyter notebook using a virtual environment for machine learning projects. Jupyter notebooks will be used for most of the remaining videos.
Open on YouTubeLesson 6: Your First Linear Regression Project in Python [Part 7] | Machine Learning for Beginners
Build your first linear regression project in Python using a toy dataset from scikit-learn. The lesson guides you through the basic steps of creating and running a model.
Open on YouTubeLesson 7: How to Analyze and Clean a Dataset [Part 8] | Machine Learning for Beginners
This lesson covers how to analyze and clean a dataset. It shows common steps for preparing data before using it in a machine learning model.
Open on YouTubeLesson 8: How to Visualize Data with Matplotlib [Part 9] | Machine Learning for Beginners
Learn how to visualize data using the Matplotlib library. The lesson shows how plots can help you understand a dataset before modeling.
Open on YouTubeLesson 9: Understanding Linear Regression [Part 10] | Machine Learning for Beginners
This lesson explains the concept of linear regression, a fundamental machine learning algorithm. It covers how linear regression works and what it is used for.
Open on YouTubeLesson 10: Looking for Correlation: The Key to Linear Regression [Part 11] | Machine Learning for Beginners
The lesson explains correlation and why it matters for linear regression. It shows how checking correlation can help you make better predictions.
Open on YouTubeLesson 11: Linear and Polynomial Regression using Scikit-learn [Part 12] | Machine Learning for Beginners
Explore linear and polynomial regression models for predicting pumpkin prices using scikit-learn. The lesson compares the two approaches in a practical project.
Open on YouTubeLesson 12: Categorical Feature Predictions with Linear Regression [Part 13] | Machine Learning for Beginners
This lesson covers categorical features: what they are, when to use them, and how to create them. It continues with the pumpkin dataset to improve regression model performance.
Open on YouTubeLesson 13: Understanding Logistic Regression for Machine Learning Classification [Part 14] | ML for Beginners
An introduction to logistic regression for classification tasks. The lesson compares logistic regression to linear regression and covers its applications.
Open on YouTubeLesson 14: Data Analysis and Preparation for Logistic Regression [Part 15] | Machine Learning for Beginners
This lesson demonstrates how to analyze and prepare data for a logistic regression model. It uses the pumpkin dataset with the goal of predicting whether a pumpkin is a certain type.
Open on YouTubeLesson 15: Logistic Regression for classification of data [Part 16] | Machine Learning for Beginners
Train a logistic regression model using the pumpkin data cleaned and transformed in the previous lesson. The lesson covers dividing data into input features and labels and fitting the model.
Open on YouTubeLesson 16: Analyzing Logistic Regression Performance with ROC Curves [Part 17] | Machine Learning for Beginners
Learn how to analyze the performance of a logistic regression model using ROC curves. The lesson evaluates the classifier built in the previous video.
Open on YouTubeLesson 17: Setup your tools ready to build Machine Learning models [Part 4] | Machine Learning for Beginners
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