Machine Learning in Business Intelligence

An Overview

Every Business rely on data nowadays to analyze the fundamental information. They use these data to understand the current business performance and find out their past performance trends. This will help businesses to make important business decisions and also help them improve their revenue growth and profits by implementing best practises and key decisions. Not sure how many of you know about the following.

1. Traditional Descriptive Analysis
2. Predictive Analysis
3. Prescriptive Analysis

I will give you a glimpse on these topics as these are the basics that one should know. Descriptive Analysis records and visualize historical performance. One who performs this analysis should have good knowledge in statistics to design data extraction and manipulation processes. They are the key source for the programmers as they use the design pattern offered by the analyser. But the biggest disadvantage that we see in this approach here is that it is time consuming which may not be suitable for high demanding business most of the time. Predictive Analysis is better compared to old traditional models as it uses the effective data driven models with optimization for analysis and Prescriptive analysis focus on finding the best course of action in a scenario given the available data.

Reference Articles for Python, Data Science, Artificial Intelligence and Cloud Computing

Machine Learning

Machine Learning uses mathematical function to construct a model based on training data which is used to make predictions for the unknown data. You have to be strong in different algorithms which are required to train computers and I am listing down the few which are important if one has to be strong in machine learning.

1. Linear Regression
2. Logistic Regression
3. Random Forest
4. Support Vector Machines
5. Neural Networks

Structured Machine Learning helps you to learn structured hypotheses from data with rich internal structure which can be in the form of one or more relations. The Data represents structured inputs and structured outputs.

Neural Networks

I got to tell you one thing here is that machine learning algorithms such as linear regression and logistic regression become complicated when they try to use it for complex linear hypothesis. Neural Networks algorithms are developed on the basis to mimic the human brains. This algorithm is based on a neuron model that consists of three layers which is input layer, hidden layer and outer layer. So the collection of a large number of neuron models is referred to as Neural Networks. To improve the performance and capabilities of neural networks, the number of hidden layers in neural networks is increased which is where Deep Learning comes into play.

Deep Learning

As i said before, Deep Learning consists of multiple hidden layers and the major difference for machine learning and deep learning is feature abstraction for a model. Deep Learning models learn in a more structured way to extract features from raw data. It eliminates the time consuming process that other models consume during feature extraction. K Means Clustering and K nearest neighbours are some other important machine learning algorithms that you should know in detail. Deep Learning models also help to increase the efficiency in areas of image recognition. With a deep learning model, you can handle huge volumes of data to train the neural network with multiple layers.

I have just written a short note on the important concepts that you have to focus on in machine learning. This is a very deep subject and an interesting one if you have passion to learn this subject. There are many more articles available on machine learning online and I request you all to refer to it and enrich your skill.

Machine Learning is the Next Gen Technology and it will be there for a while.

Signing off !!!