Introduction to Machine Learning with Scikit-Learn: Key Points

IntroductionWhat is machine learning?Getting started with Scikit-LearnWhat will we cover today?

Machine learning is a set of tools and techniques that use data to make predictions.
Artificial intelligence is a broader term that refers to making computers show human-like intelligence.
Deep learning is a subset of machine learning.
All machine learning systems have limitations to be aware of.

A supervised learning pipeline includes data loading, cleaning, feature selection, training, and testing.
Scikit-Learn provides simple, consistent tools for regression, model fitting, and performance evaluation.
Always split data into train and test sets to avoid overfitting and to assess model generalization.
Dummy coding (one-hot encoding) converts categorical variables into a numeric form usable by ML models.
Polynomial regression can capture non-linear trends by expanding features into polynomial terms.
Early exploratory data analysis (EDA) helps reveal relationships, clusters, and potential predictors before modeling.
Overfitting occurs when a model learns noise instead of signal—simpler models and good splits help mitigate this.

Classification is a supervised learning task where the goal is to predict discrete class labels from labeled examples.
Train/test splits let us estimate how well a classifier will generalize to unseen data; for classification, stratifying by class is often important.
Decision trees are easy to train and interpret, but can overfit when depth and other hyperparameters are not controlled.
Hyperparameters (such as max_depth) control model complexity and behavior but are not learned directly from the data.
Models that rely on distances or geometric margins in feature space (such as SVMs) usually require standardized inputs; tree-based models typically do not.
Comparing different classifiers (for example, decision trees vs SVMs) on the same train/test split helps reveal tradeoffs between accuracy, robustness, and interpretability.

Ensemble methods combine predictions from multiple models to produce more stable and accurate results than most single models.
Bagging (such as random forests) trains the same model on different bootstrap samples and averages their predictions, usually reducing variance and overfitting.
Boosting trains models in sequence, focusing later models on the mistakes of earlier ones, often improving accuracy at the cost of increased complexity and computation.
Stacking uses a meta-model to combine the outputs of several diverse base models trained on the same data.
Random forests often outperform single decision trees by averaging many shallow, noisy trees into a more robust classifier.
VotingRegressor and VotingClassifier provide a simple way to stack multiple estimators in Scikit-Learn for regression or classification tasks.
Choosing an ensemble method and tuning its hyperparameters is closely tied to the bias–variance tradeoff and the characteristics of the dataset.

Clustering is an unsupervised learning method that groups similar data points without labeled outputs.
K-means is simple, fast, and well-suited for large datasets, but it assumes spherical clusters and linear boundaries.
The number of clusters (k) must be specified in advance, and silhouette scores can help evaluate and compare clustering quality.
K-means can perform poorly when clusters overlap or have complex shapes, such as concentric circles.
Spectral clustering overcomes many of k-means’ geometric limitations using graph-based and kernel methods, but is slower and less scalable.
Scikit-Learn provides tools for generating datasets (make_blobs, make_circles) and implementing both k-means and spectral clustering.
Choosing the right clustering method often depends on the dataset’s structure, size, and available computational resources.

PCA is a linear dimensionality reduction technique for tabular data
t-SNE is another dimensionality reduction technique for tabular data that is more general than PCA

Perceptrons are artificial neurons which build neural networks.
A perceptron takes multiple inputs, multiplies each by a weight value and sums the weighted inputs. It then applies an activation function to the sum.
A single perceptron can solve simple functions which are linearly separable.
Multiple perceptrons can be combined to form a neural network which can solve functions that aren’t linearly separable.
We can train a whole neural network with the back propagation algorithm. Scikit-learn includes an implementation of this algorithm.
Training a neural network requires some training data to show the network examples of what to learn.
To validate our training we split the training data into a training set and a test set.
To ensure the whole dataset can be used in training and testing we can train multiple times with different subsets of the data acting as training/testing data. This is called cross validation.
Deep learning neural networks are a very powerful modern machine learning technique. Scikit-Learn does not support these but other libraries like Tensorflow do.
Several companies now offer cloud APIs where we can train neural networks on powerful computers.

This course has only touched on a few areas of machine learning and is designed to teach you just enough to do something useful.
Machine learning is a rapidly evolving field and new tools and techniques are constantly appearing.