Practical ML understanding - You will organize core Machine Learning concepts and learn when to use classification, clustering, or dimensionality reduction for specific data problems and business tasks.
Better model fit - You will learn to spot overfitting and underfitting, so you can judge model quality more accurately and reduce the risk of drawing misleading conclusions from data.
Working with SciKit Learn - You will practice the basics of SciKit Learn, making it easier to build your own analysis pipeline, run models, and compare results in a structured workflow.
Fast start in Google Colab - You will learn how to use Google Colab to run notebooks without local setup, test code more conveniently, and move from idea to experiment much faster.
End-to-end model workflow - You will understand the stages of building ML solutions, from preparing data to evaluating models, so you can plan projects better and avoid a trial-and-error approach.
Evaluating and comparing models - You will learn how to choose metrics, interpret classifiers, and understand supervised learning results, helping you compare different modeling approaches with confidence.
Working with unlabeled data - You will learn how to use clustering, dimensionality reduction, cross-validation, and stratification to handle unlabeled data better and test models more reliably.
Neural nets and Computer Vision - You will explore neural network structure and image classification basics, giving you a solid understanding of how Deep Learning works in computer vision tasks.
Basic computer skills - You should be comfortable using a computer, web browser, and files, because during the training you will work in notebook environments and complete hands-on exercises.
Programming basics - You should know basic programming concepts and understand variables, conditional statements, and loops, so you can follow the examples and adjust them on your own.
Data analysis basics - You should understand basic data concepts such as tables, columns, records, and datasets, so it is easier for you to analyze examples and interpret model outputs.
Basic mathematics - You should have school-level math fundamentals, especially reading charts and understanding simple numerical relationships, to follow how metrics and models behave.
