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
- 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.
- 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.
- 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.
- 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.
- 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.
- Ensembles in Machine Learning
- Learn about ensemble learning techniques, such as bagging, boosting, and stacking, which combine multiple models to achieve superior predictive performance.
- 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.
Courses
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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.
