Mastering Machine Learning Concepts

It’s time to give yourself a very generous pat on the back because you’ve officially arrived at the chapter on machine learning concepts. Take a moment to appreciate how far you’ve come, as well as all the preliminary information in the earlier chapters it takes to truly grasp this chapter. Many learners do themselves a disservice by jumping right into machine learning without first understanding its underlying principles (for example, statistics) and preliminary tasks (for example, data wrangling or pre-modeling), so this puts you ahead of the curve as someone well-equipped to understand the inner workings of machine learning algorithms and how and when to use them.

Throughout this chapter, we will cover a wide array of machine learning topics, providing you with the foundation needed to understand the intricacies of various algorithms and techniques. Our journey will begin with a detailed examination of the machine learning...

Introducing the machine learning workflow

If you’re a data scientist preparing for a technical interview, understanding the machine learning workflow is non-negotiable. Machine learning is concerned with the design and application of algorithms and techniques that allow computers to learn patterns that are often applied to solve business problems.

At its core, the workflow consists of several key stages, beginning with a well-defined problem statement and culminating in the application of a model trained on unseen data. Each stage, whether it’s selecting the appropriate model, tuning hyperparameters, or making predictions, serves as an essential step in the data science process. Mastery of these stages not only sharpens your technical acumen but also equips you with the systematic thinking required to tackle a wide range of data-related problems:

Figure 10.1: Workflow for machine learning projects

The importance of the machine learning...

Getting started with supervised machine learning

Supervised learning is a type of machine learning where the algorithm learns from a labeled dataset, which consists of input features and their corresponding target variables or labels. These labels are the “response variable,” “target variable,” or “output variable” – in other words, the thing you are trying to predict.

There are two types of supervised modeling that we will focus on:

• Regression
• Classification

Let’s take a closer look at them.

Regression versus classification

Regression is a specific type of supervised learning where the goal is to predict continuous numerical values. In a regression task, the algorithm learns a mapping between input features and a continuous target variable. The output of the regression model is a continuous value, which can represent quantities such as price, temperature, sales, or any other real-valued quantity. Linear...

Getting started with unsupervised machine learning

Unsupervised machine learning is a fascinating branch of artificial intelligence that focuses on discovering patterns, relationships, and structures within data without explicit guidance from labeled outcomes. Unlike supervised learning, where models are trained with labeled data to make predictions, unsupervised learning aims to explore the inherent information present in the data itself. This type of learning is particularly valuable for uncovering hidden insights, finding clusters, reducing dimensionality, and revealing underlying representations. Clustering is a common use case for unsupervised learning.

Clustering refers to grouping data points into distinct subsets or “clusters” based on similarities in their features without using pre-labeled data as a guide. Imagine that you have a scatter plot of data points and want to color-code groups of points that seem to cluster together; this is essentially what clustering...

Summarizing other notable machine learning models

In the dynamic landscape of machine learning, a plethora of models cater to diverse data and problem domains. In this section, we will highlight other notable models, each offering unique capabilities and addressing specific challenges. From text processing to survival analysis, we’ll explore a spectrum of models that expand the horizons of machine learning applications.

So, let’s take a look:

• Generalized additive models (GAMs): GAMs extend linear regression by accommodating nonlinear relationships between variables. By employing smooth functions, GAMs offer a flexible framework to capture complex interactions and patterns in data, making them valuable tools for various domains, including environmental science, economics, and healthcare.
• Naïve Bayes: This is a probabilistic classifier grounded in Bayes’ theorem. Despite its simplicity, Naive Bayes excels in text classification, spam filtering...

Understanding the bias-variance trade-off

In the journey of building machine learning models, understanding how well they perform on unseen data is paramount. Evaluating a model’s performance provides insights into its effectiveness, generalization capabilities, and potential areas for improvement. In this section, we delve into the critical process of using test sets to assess model performance comprehensively.

Model evaluation is a crucial step in the machine learning pipeline that validates the utility of a model in real-world scenarios. It gauges how well the model’s predictions align with actual outcomes, ensuring that the model can make accurate and reliable decisions beyond the training data. When assessing a model’s performance, it’s essential to consider two key aspects: bias and variance.

Bias refers to the error due to overly simplistic assumptions in the learning algorithm, leading to an underfit model that misses relevant relationships...

Tuning with hyperparameters

Hyperparameter tuning is the process of systematically searching for and selecting the optimal values for the hyperparameters of a machine learning model. Unlike model parameters, which are learned from data during training, hyperparameters are determined by the practitioner and define characteristics such as the complexity of the model, the learning rate, regularization strength, and more. The goal of hyperparameter tuning is to identify the hyperparameter values that lead to the best possible model performance on unseen data.

Hyperparameter tuning involves experimenting with different values for each hyperparameter and evaluating the model’s performance using appropriate evaluation metrics, often on a validation set. This process can be guided by different strategies, such as grid search, random search, or more advanced techniques such as Bayesian optimization.

Grid search

Grid search is a systematic approach to hyperparameter tuning. It...

Summary

In our study of machine learning, we delved deeply into crucial concepts, obtaining significant insights. Our exploration spanned both supervised and unsupervised learning, equipping us with a diverse set of models.

In this chapter, we harnessed models ranging from linear and logistic regression to tree-based techniques such as random forests and XGBoost. These models have enabled us to capture intricate relationships and accurately estimate class probabilities. Additionally, our foray into clustering methods, including K-means, hierarchical clustering, and DBSCAN, has allowed us to master the art of extracting patterns from unlabeled data. Furthermore, our knowledge has been augmented with vital skills in hyperparameter tuning and model evaluation. We learned how to refine models using tools such as grid search and have come to understand key evaluation metrics, such as accuracy and precision.

As we gear up for data science interviews, this knowledge stands as a testament...