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Machine Learning for Humans
171 pages, 2014
Part 1. Introduction. The big picture of artificial intelligence and machine learning—past, present, and future. Part 2. Supervised Machine Learning. In supervised machine learning, data are fed into a mathematical model, which is then 'trained' on the basis of that data; once trained, the model can then be used to predict outcomes based on new (unseen) data.
Part 2. 1: Linear Regression and Classification with Logistic Regression/Support Vector Machine (SVM). In this section, we will cover two widely used approaches to supervised machine learning: linear regression and support vector machines (SVMs). Part 2. 2: Non-Parametric Methods for Supervised Machine Learning. This section will focus on non-parametric approaches to supervised machine learning, including k-nearest neighbors (k-NNs), decision trees, and random forests.
Part 3: Unsupervised Machine Learning . Unsupervised methods for machine learning are able to make predictions without being given any existing data about the properties of the underlying data it's working with; instead, unsupervised methods learn from their own data, thus making them less reliant on the labels given by human beings
Vishal Maini explains machine learning as a tool that enables computers to learn from data and make predictions or decisions without being explicitly programmed. It's like teaching a computer to play chess by showing it thousands of games, rather than coding the rules.
In Machine Learning for Humans, you'll explore the three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Each type has its own strengths and weaknesses, and understanding these can help you choose the right approach for your specific problem.
Maini shows how machine learning is not just a theoretical concept, but a practical tool that's already being used in a wide range of industries. From predicting stock prices to diagnosing diseases, machine learning is transforming the way we live and work.
The author emphasizes that data is the lifeblood of machine learning. Without high-quality, relevant data, even the most sophisticated machine learning algorithm will fail. He encourages readers to take a look at how they can collect and use data effectively.
Vishal Maini doesn't just focus on the present state of machine learning, but also looks into its future. He discusses the potential of deep learning, a subfield of machine learning that's inspired by the structure of the human brain. This is a fascinating area to research if you're interested in the cutting-edge of technology.
