An Introduction to Machine Learning
What is Machine Learning?
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to update its data without human intervention.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide.
The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust their actions accordingly.
Some Machine Learning Methods
The following are the most common algorithms in Machine learning:
· Regression algorithms: Linear and logistic regression are examples of regression algorithms used to understand relationships in data. Linear regression is used to predict the value of a dependent variable based on the value of an independent variable. For example, a linear regression algorithm could be trained to predict a salesperson’s annual sales (the dependent variable) based on its relationship to the salesperson’s education or years of experience (the independent variables.) Logistic regression is used when the dependent variable is binary in nature: A or B. Another type of regression algorithm called a support vector machine is useful when dependent variables are more difficult to classify.
· Decision trees: Decision trees use classified data to make recommendations based on a set of decision rules. For example, a decision tree that recommends betting on a particular horse to win, place, or show could use data about the horse (e.g., age, winning percentage, pedigree) and apply rules to those factors to recommend an action or decision.
· Instance-based algorithms: A good example of an instance-based algorithm is K-Nearest Neighbor or k-nn. It uses classification to estimate how likely a data point is to being a member of one group or another based on its proximity to other data points.
Algorithms using unlabeled data include the following:
· Clustering algorithms: Think of clusters as groups. Clustering focuses on identifying groups of similar records and labeling the records according to the group to which they belong. This is done without prior knowledge about the groups and their characteristics. Types of clustering algorithms include the K-means, TwoStep, and Kohonen clustering.
· Association algorithms: Association algorithms find patterns and relationships in data and identify frequent ‘if-then’ relationships called association rules. These are similar to the rules used in data mining.
What is TensorFlow the machine learning platform ? …
TensorFlow is an open-source, end-to-end platform for creating Machine Learning applications. It is a symbolic math library that uses data-flow and differentiable programming to perform various tasks focused on training and inference of deep neural networks. It allows developers to create machine learning applications using various tools, libraries, and community resources.
Currently, the most famous deep learning library in the world is Google’s TensorFlow. Google uses machine learning in all of its products to improve its search engine, translation, image captioning, or recommendations processes.
How TensorFlow Works ? …
TensorFlow enables you to build data-flow graphs and structures to define how data moves through a graph by taking inputs as a multi-dimensional array called Tensor. It allows you to construct a flowchart of operations that can be performed on these inputs.
TensorFlow works in three parts:
· Preprocessing the data
· Build the model
· Train and estimate the model
This is why it is called TensorFlow because the tensor goes in and flows through a list of operations, and then it comes out the other side.
Why is TensorFlow popular ? …
TensorFlow is the most popular and used deep learning framework on GitHub. TensorFlow is the best library for machine learning because it is built to be accessible for everyone. The TensorFlow library incorporates different APIs to build at scale deep learning.
TensorFlow is based on graph computation; it allows the developer to visualize the construction of their neural network with TensorBoard. This tool is helpful to debug the program. Finally, TensorFlow is built to be deployed at scale. It runs on CPU and GPU.
At the end …
Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information … On the other hand, machine-learning programs often fail to deliver expected results. There are numerous reasons for this: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.
In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision. Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.