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.

Some Machine Learning Methods

The following are the most common algorithms in Machine learning:

What is TensorFlow the machine learning platform ? …

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:

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.

Software Engineer — NY, USA