Machine Learning Quickies

If an interviewer shows you a sample dataset and asks you to tell what comes to your mind, he likely wants to ask you about MISSING VALUES in the dataset. So, check if the dataset has any missing values and answer him accordingly. The next thing the interviewer will ask you will likely be about strategies for dealing with those missing values. So, be prepared. 4 Strategies to Deal with Missing Values in a Dataset: Drop all columns with missing values (wastes a lot of valuable data, so NOT recommended) Drop all rows with missing values (if there are only a few no of rows with missing values, then you can do this) Imputation: You basically fill the missing value with some default value (like -1) or some calculated value (like mean). This is the most used strategy. Imputation with tracking: Basically, you use imputation on a column, and then you create a new column to keep track of the rows where you have applied imputation. The new column will be either TRUE or FALSE based on whether you...

 To settle the debate once and for all, in this article I will talk about deep learning and how it is different and/or similar to machine learning. I will start off with machine learning and then connect it to deep learning, eventually establishing how deep learning is a subset of machine learning.  Machine learning : where it all starts As you might know already, machine learning or ML is giving machines ability to learn like human beings and for this it uses multiple types of algorithms which are mentioned as follows: Linear based (regression etc.) Tree based (decision tree, random forest, xgboost, catboost, etc.) Neural Network based (ANN, CNN, perceptron, etc) kNN, etc. Diagrammatically, it can be shown as follows:  Machine learning: various algorithms Machine learning via Neural Network Now, out of all these various types of algorithms, consider the one using neural networks. This one can further be divided into 2 categories:  Simple neural networks: meaning the...

 Learning in the domain of machine learning is predominantly seen as in 4 buckets or types. Yes, there are 4 types of learning and in this article I will go through each one in a brief manner. For starters, the types of learning are: Supervised Learning Unsupervised Learning Semi supervised Learning Reinforcement Learning Supervised Learning Explanation Supervised learning basically deals with labelled data. For example, let us say you have a flower species dataset in which you have flower characteristics (or features) like length of petal, length of sepal, width of petal and width of sepal and then you have an associated label (or target) which is the species of the flower. (This dataset can be found here .)  The main point here is that the data is in the form of (X,y) where X is the set of input features (petal length, sepal length, petal width and sepal width) and y is the target which is flower species in this case.  In supervised learning, the target is known for the...

  Aspects of developing a learning system In the previous blog posts, I have already talked about the meaning of the term "learning" in machine learning. Go check out that blog ( here! ) if you have not read it already as it is a prerequisite for this one. This post will talk about how that "learning" is learned by machine learning systems and I will try to explain the basic algorithm that is followed in this process and the various aspects involved in the learning process. Algorithm to design a learning system in ML workflow The basic algorithm followed by an explanation is as follows: Choosing the training experience. Choosing the target function. Choosing the target function's representation. Choosing the learning or function approximation algorithm to infer the target function. Explanation Choosing the training experience The training experience can be thought of as the training data. Basically, you need to have training data which the learning system will l...

 Following are some of the the applications of machine learning in various domains that I have come across : Advertising Advertising or advertisement forms one of the major business models in the world presently. Most of the modern day businesses, be it social media sites or writing apps, all earn and sustain themselves through advertising content to their users based on relevancy (or maybe not! ha-ha). As such, it is obvious that machine learning finds its use in this domain in multiple forms: Predicting click rate ML is used in predicting the number or percentage of viewers who will actually click on an ad displayed to them. Recommending appropriate ads  ML is used in recommending appropriate ads to users based on their interests and activity. Read recommendation engines to understand how it is actually done. Medicine and healthcare Medicine and healthcare forms one of the most important sectors of any place. As such whatever improves its quality needs to be invested in as s...

ML and AI: Benefits The first question that comes to mind whenever starting to learn something new is " Why ?". It is no different for Machine Learning (ML) and Artificial Intelligence (AI). Why should you learn ML and AI? What are the benefits of learning ML and AI ? Is there any use to learning ML and AI? What do ML and AI offer that make them so special? Here I will try to answer these questions regarding the benefits of machine learning and artificial intelligence. Feel free to skip to the summary at the end if you need direct points. So, why should you learn ML and AI? Is it simply because they are popular and everybody's doing them, so you should also do them?  No.  So, what is the reason? Explanation ML is a subset of AI (Artificial Intelligence) and the whole point is that you are trying to give computers human-like intelligence. Why do you want to do this? One reason could be curiosity and another is reducing human effort. Just think about it. If computers could ...