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.
Overview
Syllabus
- Course 1: Training Your First Machine Learning Model from Scratch
- Course 2: Data Preprocessing For Machine Learning
- Course 3: Diving Deep into Regression
- Course 4: Cracking Classification
- Course 5: Deep Dive into Regression and Classification Metrics
- Course 6: Ensembles in Machine Learning
- Course 7: Hypertuning and Cross-Validation
Courses
-
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.
-
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.
-
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.
-
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.
-
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.
-
Learn about ensemble learning techniques, such as bagging, boosting, and stacking, which combine multiple models to achieve superior predictive performance.
-
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.
Related Courses
Related Articles
-
[2026] 2000+ Free Developer and IT Certifications
-
[2026] Massive List of MOOC-based Microcredentials
-
[2026] Unlock 2000+ Free Certificates: Master Tech & Soft Skills with CodeSignal Learn
-
Become a Supercommunicator: Practical Skills for Better Conversations
-
CodeSignal – The Duolingo of Coding Practice
-
14 Best Machine Learning Courses for 2026: Scikit-learn, TensorFlow, and more
