Machine Learning for Industry

Artificial Neural Networks (ANNs), popularly known as Neural Networks, are supervised Machine Learning algorithms capable of solving both classification and regression problems. Here we have discussed the analogy from human brain, terms used to define any neural network, what are the things present in a schematic diagram of ANN, advantages and disadvantages of ANN with respect to other machine learning algorithms and lastly what are the practical use-cases of ANN.

In order to fully grasp the concept of a Neural Network, it is crucial to understand the various components that make up a Neural Network. In this article, we delve into the key components of an Artificial Neural Network, including Neurons, Input Layers, Output Layers, Hidden Layers, Connections, Parameters, Activation Functions, Optimization Algorithms, and Cost Functions. These components work together to solve both classification and regression problems in Machine Learning.

Data Science, the application of scientific methods to extract valuable insights from data, is crucial for businesses to perform various analyses. In this paragraph, we discuss the importance of Data Science, its usage in businesses, the roles and responsibilities of a data scientist, the necessary skills for a career in Data Science, the distinctions between Data Science, Machine Learning, Data Engineering, and Business Analysis, and the challenges faced by data scientists.

Big Data, the stage of data that exceeds the capabilities of traditional storage, analytical and processing methods, requires specialized solutions like the Hadoop framework. In this article, we explore the characteristics, types, examples, advantages, and challenges of Big Data and how Hadoop supports its use-cases.

K-Nearest Neighbor is a supervised learning algorithm that can be used to solve classification and regression problems. This algorithm learns without explicitly mapping input variables to the target variables. It is probably the first machine learning algorithm, and due to its simplicity, it is still accepted in solving many industrial problems.

Scikit-learn is a free machine learning framework available for Python, providing an interface for supervised and unsupervised learning. It is built over the SciPy library and provides every feature catering to every ML requirement. In this blog, we will learn the essential concepts, tools, and features related to Scikit-learn.