Machine Learning with Python: End to End

Seven lessons from fundamentals and data prep to evaluation, pipelines, and deployment.

7 lessons · about 61 minutes of reading

1
Machine Learning Fundamentals: What It Is and How It Works
Understand what machine learning is, the main types, the key vocabulary, and the end-to-end workflow before writing any code.
8 min read
2
Data Preparation for Machine Learning with pandas
Load, inspect, and clean data with pandas, handling missing values and categorical columns before training.
9 min read
3
Train/Test Split and Your First Model with scikit-learn
Split your data, train a classifier with scikit-learn, and measure how well it generalizes to unseen examples.
8 min read
4
Evaluating Machine Learning Models: Accuracy, Precision, Recall, and More
Choose the right metric for classification and regression, and read a confusion matrix correctly.
9 min read
5
Overfitting, Cross-Validation, and Hyperparameter Tuning
Detect overfitting, get reliable estimates with cross-validation, and tune hyperparameters with GridSearchCV.
9 min read
6
scikit-learn Pipelines and Feature Engineering
Combine preprocessing and modeling into one Pipeline so training and prediction stay consistent and leakage-free.
9 min read
7
Saving and Deploying a Machine Learning Model as an API
Save a trained scikit-learn Pipeline with joblib, serve predictions with FastAPI, and plan for monitoring in production.
9 min read

Machine Learning Fundamentals: What It Is and How It Works