Overview of Machine Learning in Python

Garbage in, garbage out. Your model is only as good as your data. Learn the essential data prep and evaluation workflow in Python.

#1about 2 minutes

Understanding the main paradigms of machine learning

Learn the distinctions between supervised, unsupervised, semi-supervised, and reinforcement learning based on data and goals.

#2about 1 minute

Differentiating between regression and classification tasks

Supervised learning is broken down into predicting continuous variables with regression and discrete labels with classification.

#3about 3 minutes

Why data preparation is a critical first step

The principle of 'garbage in, garbage out' highlights the need to analyze and clean data before training any model.

#4about 2 minutes

Converting categorical data into numerical features

Transform non-numerical string data into a machine-readable format using label, ordinal, and one-hot encoding techniques.

#5about 2 minutes

Standardizing numerical data with scaling techniques

Handle large or small numerical values that can negatively impact training by applying scaling methods like min-max or Z-score normalization.

#6about 2 minutes

Choosing a strategy for handling missing data

Address missing values in a dataset by either deleting the affected rows/columns or using imputation to replace them.

#7about 3 minutes

Analyzing a dataset for potential bias and imbalance

A practical example using the adult census dataset demonstrates how to identify and understand biases related to age, sex, and race.

#8about 5 minutes

Splitting data for model training and evaluation

Properly divide your dataset into training and testing sets using strategies like holdout, cross-validation, and stratification to avoid data leakage.

#9about 2 minutes

Using metrics to evaluate model performance

Measure classification model success with accuracy, precision, and F1 score, and regression model success with mean absolute or squared error.

#10about 3 minutes

Understanding overfitting and the bias-variance tradeoff

Find the optimal model complexity by balancing the training error and test error to avoid underfitting or overfitting.

#11about 3 minutes

Tuning hyperparameters and selecting the right algorithm

Optimize model performance by searching for the best parameters with grid search or randomized search, and explore meta-learning for algorithm selection.

#12about 3 minutes

Introduction to decision trees and random forests

Decision trees offer a transparent, white-box model, but random forests typically provide better performance by combining multiple trees.

#13about 7 minutes

Code demo: Preprocessing and training a classifier

A step-by-step Python example shows how to preprocess data, handle missing values, and train decision tree and random forest classifiers using scikit-learn.

#14about 4 minutes