What are the applications of machine learning? Type of machine learning?

According to Wikipedia:Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so.

What is Machine Learning? History of Machine Learning.

Before starting this blog, I want to share an incident in my life when I realize what computers are. I was in 4th grade when my computer teacher asked the question “Are computers intelligent???”. I was the only one in my class to answer that “Yes, computers are intelligent”. But my teacher said that I m wrong and claims that “Computers are not intelligent, rather they use human intelligence to perform tasks”. This made me curious, I asked myself “cant computers think by themself??”. …

It’s been reviewed by Harvard Business that Data Science is the “sexiest job of 21st century”. But it is important to know what makes this job so valuable? Why Data science comes under the category of “Highly paid profession”? In this article, we will walk through some of the main reasons as to why one should learn Data Science. We will also understand the market scenario and how Data Scientist helps in making good decisions.

“Doubts are good. Confusion is excellent. Questions are awesome.

All these are attempts to expand the wisdom of mind.”

―Manoj Arora

In a classification problem, it is often important to specify the performance assessment. This can be valuable when the cost of different misclassifications varies significantly. Classification accuracy is also a measure showing how well the classifier correctly identifies the objects.

A confusion matrix also called a contingency table or error matrix gets across the picture when it comes to visualizing the performance of a classifier. The columns of the matrix represent the instances of the predicted classes and the rows represent the instances of the actual class. …

An outlier is a data point in a data set that is distant from all other observations. A data point that lies outside the overall distribution of the dataset. Or in a layman term, we can say, an outlier is something that behaves differently from the combination/collection of the data.

Outliers can be very informative about the subject-area and data collection process. It’s essential to understand how outliers occur and whether they might happen again as a normal part of the process or study area. To understand outliers, we need to go through these points:

- what causes the outliers?
- Impact of the…

Exploratory data analysis is one of the best practices used in data science today. While starting a career in Data Science, people generally don’t know the difference between Data analysis and exploratory data analysis. There is not a very big difference between the two, but both have different purposes.

Exploratory Data Analysis(EDA): Exploratory data analysis is a complement to inferential statistics, which tends to be fairly rigid with rules and formulas. At an advanced level, EDA involves looking at and describing the data set from different angles and then summarizing it.

Data Analysis: Data Analysis is the statistics and probability to figure out trends in the data set. It is used to show historical data by using some analytics tools. It helps in drilling down the information, to transform metrics, facts, and figures into initiatives for improvement. …

A hypothesis is an assumption about a particular situation of the world that is testable. Research scientists proceed by making a guess that they are sure (or they think) is wrong. The process of hypothesis testing makes sense as it is a fairly intuitive way to reach conclusions about the world and the people in it. We often informally do hypothesis testing all the time to make sense of things in our daily routine also. Statisticians test hypotheses of a specific variety. They have data that represent a sample of values from a real or theoretical population about which they wish to reach conclusions. …

One of the most basic concepts in statistics is hypothesis testing. Not just in Data Science, Hypothesis testing is important in every field. Want to know how??? Let us take an example. You must have heard about lifebuoy?? Suppose lifebuoy claims that, it kills 99.9% of germs. So how can they say so? There has to be a testing technique to prove this claim right?? So hypothesis testing uses to prove a claim or any assumptions.

1.Definition of hypothesis testing.

2.Null and Alternative Hypothesis testing.

3.Simple and composite hypothesis testing.

4.One-tailed and two-tailed testing.

5.Critical Region.

6.Type I and Type II error. …

The term “Data science” comes in 1996 which was included in the title of a statistical conference (International Federation of Classification Societies (IFCS). In early 1997, there was an even more radical view suggesting to rename statistics to Data Science. Recently, “Donoho” provides an overview of Data Science which focuses on the evolution of Data Science from statistics. Statistics is one of the most important disciplines to provide tools and methods to find structure in and to give deeper insight into data, and the most important discipline to analyze and quantify uncertainty.

One of the most comprehensive definitions of Data Science was recently given by Cao as the…

*Like watching movies???*

But what to watch?? we often ask this question to ourselves in free time. There are a tremendous amount of movies, TV shows, and documentaries available on Netflix, and choosing one from them is really a headache. In my free time, I was going throw the same problem, so I have given a shot to building the recommendation engine that could be guiding me some movies based on my previous watch.

For any recommendation system, we consider users and some items, so in this case, (Netflix) items are movies. Before starting, let us know what a recommendation system does. Basically, it matches the content on the bases of similarities between a given set of users and a set of items. …

About