Mark Wickham [Mark Wickham] - Practical Java Machine Learning: Projects with Google Cloud Platform and Amazon Web Services

• A review all of the terminology of AI and its sub-fields including machine learning

• Why ML is important and why Java is a good choice for implementation

• Setup instructions for the most popular development environments

• An introduction to ML-Gates, a development methodology for ML

• The business case for ML and monetization strategies

• Why this book does not cover deep learning, and why that is a good thing

• When and why you may need deep learning

• How to think creatively when exploring ML solutions

• An overview of key ML findings

• Artificial intelligence (AI) : Anything that pretends to be smart.

• Machine learning (ML) : A generic term that includes the subfields of deep learning (DL) and classic machine learning (CML).

• Deep learning (DL) : A class of machine learning algorithms that utilize neural networks.

• Reinforcement learning (RL) : A supervised learning style that receives feedback, but not necessarily for each input.

• Neural networks (NN) : A computer system modeled on the human brain and nervous system.

• Classic machine learning (CML) : A term that more narrowly defines the set of ML algorithms that excludes the deep learning algorithms.

• Data mining (DM) : Finding hidden patterns in data, a task typically performed by people.

• Machine learning gate (MLG): The book will present a development methodology called ML-Gates. The gate numbers start at ML-Gate 5 and conclude at ML-Gate 0. MLG3, for example, is the abbreviation for ML-Gate 3 of the methodology.

• Random Forest (RF) algorithm : A learning method for classification, regression and other tasks, that operates by constructing decision trees at training time.

• Naive Bayes (NB) algorithm : A family of probabilistic classifiers based on applying Bayes theorem with strong (naive) independence assumptions between the features.

• K-nearest neighbor (KNN) algorithm : A non-parametric method used for classification and regression where the input consists of the k closest training examples in the feature space.

• Support vector machine (SVM) algorithm : A supervised learning model with associated learning algorithm that analyzes data used for classification and regression.

AI Definitions and Domains

Data miningis all abouthumansdiscovering the hidden patterns in data, whilemachine learningautomates the process and allows thecomputerto perform the work through the use of algorithms.

It is helpful to think about each of these terms in context of infrastructure and algorithms. Figure shows a graphical representation of these relationships. Notice that statistics are the underlying foundation, while artificial intelligence on the right-hand side includes everything within each of the additional subfields of DM, ML, and DL.

Machine learning is all about the practice ofselectingandapplying algorithmsto our data.

I will discuss algorithms in detail in Chapter . The algorithms are the secret sauce that enables the machine to find the hidden patterns in our data.

Figure 1-1

Artificial intelligence subfield relationships

1.2 Historical

The term artificial intelligence is hardly new. It has actually been in use since the 1970s. A quick scan of reference books will provide a variety of definitions that have in fact changed over the decades. Figure shows a representation of 1970s AI, a robot named Shakey, alongside a representation of what it might look like today.

Figure 1-2

AI, past and present

Most historians agree that there have been a couple of AI winters . They represent periods of time when AI fell out of favor for various reasons, something akin to a technological ice age. They are characterized by a trend that begins with pessimism in the research community, followed by pessimisms in the media, and finally followed by severe cutbacks in funding. These periods, along with some historical context, are summarized in Table .