Machine Learning with Python: Zero to GBMs
Offered By: Jovian
Course Description
Overview
"Machine Learning with Python: Zero to GBMs" is a practical and beginner-friendly introduction to supervised machine learning, decision trees, and gradient boosting using Python. This is a self-paced course where you can:
- Watch hands-on coding-focused video tutorials
- Practice coding with cloud Jupyter notebooks
- Build an end-to-end real-world course project
- Earn a verified certificate of accomplishment
- Interact with a global community of learners
You will solve 2 coding assignments & build a course project where you'll train ML models using a large real-world dataset.
Syllabus
Lesson 1 - Linear Regression with Scikit Learn
- Preparing data for machine learning
- Linear regression with multiple features
- Generating predictions and evaluating models
- Downloading & processing Kaggle datasets
- Training a logistic regression model
- Model evaluation, prediction & persistence
- Download and prepare a dataset for training
- Train a linear regression model using sklearn
- Make predictions and evaluate the model
- Downloading a real-world dataset
- Preparing a dataset for training
- Training & interpreting decision trees
- Training and interpreting random forests
- Ensemble methods and random forests
- Hyperparameter tuning and regularization
- Prepare a real-world dataset for training
- Train decision tree and random forest
- Tune hyperparameters and regularize
- Training and evaluating a XGBoost model
- Data normalization and cross-validation
- Hyperparameter tuning and regularization
- Perform data cleaning & feature engineering
- Training, compare & tune multiple models
- Document and publish your work online
- Clustering and dimensionality reduction
- Collaborative filtering and recommendations
- Other supervised learning algorithms
Taught by
Aakash N S
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