Machine Learning

Machine learning is the science or application of computer algorithms to automate analytical model building through experience and the use of data. Machine learning is a branch of artificial intelligence where computers act without being explicitly programmed by learning, growing, changing by themselves every time new data is fed. It is considered by researchers as the best way to develop human level artificial intelligence.

In today’s world, machine learning is so pervasive that we use it every day without recognizing it. It is used in areas such as speech recognition, face recognition, effective web searches and so many other areas. In this course, you will learn about effective machine learning techniques, gain practice implementing them and how to apply them to your specific problem.

Data Science

Data science is an interdisciplinary field of study that combines domain expertise, programming skills/algorithms, and knowledge of mathematics and statistics to extract meaningful insights from structured and unstructured data. Data is the foundation of innovation hence a good data scientist is one who knows how to extract the data, whole needs to connect with, hire, or the technologies he needs to deploy to get the job done. Moreover, a data scientist is one who can link business objectives with data marts, and who can simply connect the dots from business gains to human behaviors and from data generation to money spent. A data scientist’s duties include developing strategies for analyzing, exploring and visualizing data, building models with data using programming languages and deploying these models into applications.

Deep Learning

Deep learning is a branch of machine learning which is a subset of artificial intelligence in which multilayered neural networks learn from big volumes of data that is unstructured or unlabeled. Deep learning is inspired by the human brain and tries to mimic human behavior by attempting to draw similar conclusions as humans would by continuously analyzing data with a given logical structure. Examples of application of deep learning include how Netflix and you tube are able to recommend movies and songs that are particular to your taste/ liking, self-driving cars and many others.

Neural networks are trained to perform tasks such as filtering, clustering, classification on data similar to the way our brains identify patterns and classify different types of information increasing the likelihood of a correct output. Artificial neural networks have unique features that enable problem solving that machine learning models can never solve.

Neural networks consist of a collection of nodes called the neurons that model the biological neurons in the human brain. A neuron is a graphical representation of a numerical value which keeps on changing weights between the neurons as the neural network learns. The weights between the neurons keep on changing through training.

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