Machine Learning Definition
The Machine Learning subfield of science 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, this paradigm can be viewed as “programming by example”. The subarea of artificial intelligence intersects broadly with other fields like statistics, mathematics, physics, theoretical computer science, and more. Machine Learning can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. It can be a game changer in all these domains and is set to be a pillar of our future civilization. If one wants a program to predict something, one can run it through a Machine Learning algorithm with historical data and “train” the model, it will then predict future patterns. Machine Learning is quite vast and is expanding rapidly, into different sub-specialties and types.
Examples of Machine Learning problems include, “Is this car?”, “How much is this house worth?”, “Will this person like this movie?”, “Who is this?”, “What did you say?”, and “How do you fly this thing?”. All of these problems are excellent targets for a Machine Learning project, and in fact, it has been applied to each of them with great success.
Machine Learning can be broadly divided into three categories:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning.
Supervised Learning involves training a model on a labeled dataset, where the output is known. The goal is to develop a model that can predict the output of new data. Examples of Supervised Learning include predicting the price of a house based on its features or identifying whether an email is spam or not. In the majority of supervised learning applications, the ultimate goal is to develop a finely tuned-predictor function. “Learning” consists of using sophisticated mathematical algorithms to optimize this function so that, given input data about a certain domain, it will accurately predict some interesting value. The goal of Machine Learning is not to make “perfect” guesses but to make guesses that are good enough to be useful.
Many modern Machine Learning problems take thousands or even millions of dimensions of data to build predictions using hundreds of coefficients. The iterative approach taken by Machine Learning algorithms works very well for multiple problems, but it doesn’t mean Machine Learning can solve any arbitrary problem, it can’t, but it is very a powerful tool in our hands. In supervised learning, there are two categories of problems:
Regression – the value being predicted is continuous, it answers questions like: “How much?” or “How many?”
Classification – yes-or-no prediction, categorical answer, Eg. “Is this cat?”, “Is this product category x?”.
from sklearn.datasets import load_iris from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # Load the iris dataset iris = load_iris() X = iris.data y = iris.target # Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=13) # Train a random forest classifier rf = RandomForestClassifier(n_estimators=100, random_state=13) rf.fit(X_train, y_train) # Make predictions on the testing set y_pred = rf.predict(X_test) # Calculate the accuracy of the model accuracy = accuracy_score(y_test, y_pred) print("Accuracy:", accuracy)
The underlying theory is more or less the same, differences are the design of the predictor and the design of the cost function.
Unsupervised Learning involves training a model on an unlabeled dataset, where the output is unknown. The goal is to identify patterns and relationships in the data. Examples of Unsupervised Learning include clustering similar products together or identifying topics in a large dataset of documents. Unsupervised learning typically is tasked with finding relationships within data. There are no training examples, the system is given a set of data and tasked with finding patterns. A good example is identifying groups of friends in social network data. The algorithms used to do this are different from those used for supervised learning.
Reinforcement Learning involves training a model to make decisions based on rewards and penalties. The goal is to develop a model that can learn from its mistakes and improve its performance over time. Examples of Reinforcement Learning include teaching a computer to play a game or training a robot to perform a task.
Machine Learning is an incredibly powerful tool, it will help to solve some of the human most burning problems, as well as open up whole new opportunities.
|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