Introduction to Machine Learning with SciKit Learn | CodeSignal Learn

intermediate

Introduction to Machine Learning with SciKit Learn

Data Science

7 courses

138 practices

18 hours

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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561 learners

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Certificate of Achievement

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

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Course 1

Training Your First Machine Learning Model from Scratch

5 lessons

21 practices

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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Course 2

Data Preprocessing For Machine Learning

5 lessons

23 practices

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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Course 3

Diving Deep into Regression

5 lessons

19 practices

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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Course 4

Cracking Classification

5 lessons

21 practices

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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Course 5

Deep Dive into Regression and Classification Metrics

4 lessons

16 practices

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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Course 6

Ensembles in Machine Learning

5 lessons

20 practices

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

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Course 7

Hypertuning and Cross-Validation

4 lessons

18 practices

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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