Problem Definition and Data Preparation for a Real-Life Data Science Project

Making Your Project Presentable: Last Touches for an Impressive Portfolio

Bonus lesson: Turning a Regression Problem into a Classification Problem

Is your model actually that good? Validating Machine Learning Models

Improve Machine Learning Model Performance with Tuning

Step-by-Step Preparing and Training a Machine Learning Model

Feature Engineering: Integrate a new data source into your dataset

Feature Engineering: Create new features using existing data

The Basics of Evaluating Machine Learning Projects

How to Train a Benchmark Model for your Machine Learning Project

Problem Definition and Data Preparation for a Real-Life Data Science Project

Extract Hour, Day, Month Info from Pandas DateTime

Fixing Incorrect Column Types in Pandas to Prepare Data for ML

Data Cleaning after Identifying Data Problems in Pandas

Data Exploration: Identifying Data Issues and Their Potential Causes

Using Data Visualization to Identify Data Problems

How Collecting Data Works on a Real-Life Data Science Project

Creating a GitHub Repository for a Data Science Project

Introduction to Jupyter Notebooks: explore the main functionalities

How to Set up a Data Science Development Environment

Hands-on Data Science course: Let's build a project together from scratch!

Python or R: Data Professor answers!

Starting out on YouTube: why the Data Professor got into the YT business

It only takes 2 minutes to master your tasklist. Productivity tips from @DataProfessor

Key Concepts and Techniques for Natural Language Processing