Machine learning 101: Where to begin?

When is machine learning overkill? Learn the most critical first step before building any model, using the project of an intelligent cat feeder.

#1about 4 minutes

Understanding core machine learning concepts and types

Distinguish between AI, machine learning, and deep learning, and explore the four main approaches: supervised, unsupervised, semi-supervised, and reinforcement learning.

#2about 2 minutes

Why you must define the problem first

Before coding, it is crucial to define the problem you are solving and determine if machine learning is the right solution over simpler business rules.

#3about 4 minutes

Collecting and exploring your initial dataset

Discover where to find public datasets like Kaggle and use Python to perform an initial exploration of your data to identify issues like missing values.

#4about 3 minutes

Preparing and augmenting data for training

Learn to clean, transform, and expand your dataset using techniques like feature encoding and data augmentation while avoiding the common pitfall of overfitting.

#5about 4 minutes

Splitting data and selecting a model algorithm

Properly divide your data into training and testing sets, then get an overview of common algorithms like regression, decision trees, and random forests.

#6about 6 minutes

Evaluating and improving your model's performance

Use tools like the confusion matrix and metrics like mean squared error to assess your model's accuracy and apply techniques for improvement, such as handling outliers.

#7about 2 minutes

Real-world applications and key takeaways

See how companies like Netflix use machine learning and review the key steps for starting your own ML project, from data collection to model verification.