Data Science

7 courses

Introduction to Machine Learning with SciKit Learn

This learning path is designed to cover all the basics of machine learning using the SciKit-learn library. You will go through the process of training a machine learning model, starting with data preprocessing and ending with advanced model evaluation techniques.

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This learning path includes:

7 courses with bite-sized lessons and practices

33 engaging lessons in text and video formats

138 hands-on practices

One-on-one guidance from Cosmo, our AI tutor

Verified skills you'll gain

DEVELOPING

Data Cleaning and Preprocessing

INTERMEDIATE

Machine Learning and Predictive Modeling

INTERMEDIATE

Model Evaluation, Validation, and Selection

Tools you'll use

Numpy

Pandas

Python

Scikit-learn

Trusted by learners working at top companies

5 lessons

21 practices

Training Your First Machine Learning Model from Scratch

This course delves into the foundational steps required to build and train a linear regression model from scratch using scikit-learn. You will understand the basics of model training, evaluation, and prediction.

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5 lessons

23 practices

Data Preprocessing For Machine Learning

This course covers essential data preprocessing techniques such as handling missing values, encoding categorical features, feature scaling, and splitting the dataset for training and testing.

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5 lessons

19 practices

Diving Deep into Regression

This course explores regression techniques beyond linear models, including polynomial regression, ridge, lasso, and elastic net regressions. You will learn everything about regularization to train perfect regression models.

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5 lessons

21 practices

Cracking Classification

This course focuses on key classification techniques and evaluation metrics, including logistic regression, decision tree, and k-nearest neighbors (KNN) classifiers. You will understand how to compare and evaluate classifier performance using various metrics.

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4 lessons

16 practices

Deep Dive into Regression and Classification Metrics

This course provides an in-depth understanding of evaluation metrics for both regression and classification models. By the end of this course, you will be able to evaluate the performance of your machine learning models properly in different scenarios.

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5 lessons

20 practices

Ensembles in Machine Learning

Learn about ensemble learning techniques, such as bagging, boosting, and stacking, which combine multiple models to achieve superior predictive performance.

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4 lessons

18 practices

Hypertuning and Cross-Validation

Master hyperparameter tuning and cross-validation techniques to optimize the performance of your machine learning models. Learn how to perform grid search, random search, and various cross-validation methods.

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Meet Cosmo: The smartest AI guide in the universe

Our built-in AI guide and tutor, Cosmo, prompts you with challenges that are built just for you and unblocks you when you get stuck.

Whether you’re leveling up or just starting out, find your next step here.