Machine Learning

Machine learning is a subfield of science, that provides computers with the ability to learn without being explicitly programmed.   The goal of machine learning is to develop learning algorithms, that do the learning automatically without human intervention or assistance, just by being exposed to new data. The machine learning paradigm can be viewed as “programming by example”. This subarea of artificial intelligence intersects broadly with other fields like statistics, mathematics, physics, theoretical computer science, and more.

Machine learning is typically categorized into three main types: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the model is trained using labeled data, which consists of inputs and their corresponding outputs. The computer then learns to predict outputs for new inputs based on the patterns it has learned from the data. Examples of supervised learning tasks include image classification, speech recognition, and fraud detection.

In unsupervised learning, the computer is given a set of inputs without any corresponding outputs. The goal of the model is to identify patterns or structures in the data without being explicitly told what to look for. Examples of unsupervised learning tasks include clustering, anomaly detection, and dimensionality reduction.

In reinforcement learning, the computer learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. The computer learns to make decisions that maximize its reward over time. Examples of reinforcement learning tasks include game playing, robotics, and autonomous driving.

Machine learning algorithms can be further categorized based on the techniques used to learn from data. Some common techniques include decision trees, neural networks, support vector machines, and Bayesian networks. These algorithms can be used for a wide range of applications, including natural language processing, computer vision, and predictive analytics.

In order to train machine learning models, large amounts of high-quality data are required. This data must be representative of the problem being solved, and it must be properly labeled and annotated. Once the model has been trained, it can be used to make predictions on new data.

Machine learning is a rapidly evolving field, and it has the potential to transform many industries and domains. However, it also raises important ethical and social questions around bias, fairness, and privacy. As such, it is important to carefully consider the impact of machine learning on society and to ensure that these systems are developed and used responsibly.

Data Science from Scratch: First Principles with Python

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.

If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.

Get a crash course in Python
Learn the basics of linear algebra, statistics, and probability—and understand how and when they’re used in data science
Collect, explore, clean, munge, and manipulate data
Dive into the fundamentals of machine learning
Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
Explore recommender systems, natural language processing, network analysis, MapReduce, and databases