Daksh TrehaninTowards AI·Apr 15, 2020Serving Data Science to a RookieSo, last week my team head asked me to interview some of the possible interns for the team for the role of data science and machine learning, and he forwarded me their resumes. He asked me to select at most 2 candidates from 8 eligible candidates. …Machine Learning4 min readMachine Learning4 min read

Daksh Trehan·Apr 6, 2020What Exactly Machine Learning is?Machine Learning explained as easy as possible — We all have heard of Machine Learning and all its perks. It might be the hottest field right now but most of still are not sure what Machine Learning is? So, Machine learning is a subset of Data Science. But what’s Data Science? Data Science is a process that involves…Machine Learning3 min readMachine Learning3 min read

Daksh Trehan·Apr 6, 2020Who uses Machine Learning and Why?So as discussed what exactly is machine learning? , now next step to know is who use machine learning and what’s exactly there purpose to use these technique? As expected, data is the new fuel that runs and owns world. …Machine Learning2 min readMachine Learning2 min read

Daksh TrehaninLevel Up Coding·Apr 22, 2020Relating Machine Learning Techniques to Real-Life.Explaining types of ML models as easy as it could be. Machine Learning In this article we’ll cover how machine learning works and what are the different types of machine learning algorithms; for a detailed explanation about each algorithm I will be starting a series that will be known by “everything about…Machine Learning5 min readMachine Learning5 min read

Daksh TrehaninDataDrivenInvestor·Apr 25, 2020Determining perfect fit for your ML model.Teaching Overfitting vs Underfitting vs Perfect fit in easiest way. Before getting into detailed explanation about each algorithm through medium of series known as “<algorithm_name> Explained “. It would be better if we could understand the way we want to tune our model. But before everything else, let’s recall definition…Machine Learning7 min readMachine Learning7 min read

Daksh TrehaninTowards AI·Apr 24, 2021One-Line, Magical Code to Perform EDA!One line solves all your problems! — “Data is the new oil” ~ Clive Humby Data is an integral part of our life and unlike other resources it is inexhaustible but here comes a catch, it is only useful to your organization if you know how to mend it and get its gist. Data Science is a…Artificial Intelligence5 min readArtificial Intelligence5 min read

Daksh TrehaninTowards AI·Jun 27, 2020Member-onlyStart-off your ML journey with K-Nearest Neighbors!Detailed theoretical explanation and scikit-learn implementation with an example! — K-Nearest Neighbors(KNN) is one of the elementary methods in machine learning and is a great way to introduce yourself in the world of Machine Learning. Table of Content: Introduction to K-NN How does K-NN works? How do we choose “K”? Pseudocode for KNN Implementing KNN to classify breast cancer as Malign and BenignMachine Learning7 min readMachine Learning7 min read

Daksh TrehaninTowards AI·May 7, 2020Linear Regression ExplainedExplaining Linear Regression as easy as it could be. — It is often the first machine-learning algorithm to be taught due to its fundamental nature. It is part of Supervised Learning that means the data required will be labeled. Regression refers to predicting continuous value. As the name suggests, it is a linear model; that is, it can only fit…Machine Learning6 min readMachine Learning6 min read

Daksh TrehaninTowards Data Science·May 22, 2020Gradient Descent ExplainedA comprehensive guide to Gradient Descent — Optimization refers to the task of minimizing/maximizing an objective function f(x) parameterized by x. In machine/deep learning terminology, it’s the task of minimizing the cost/loss function J(w) parameterized by the model’s parameters w ∈ R^d. Optimization algorithms (in the case of minimization) have one of the following goals: Find the…Machine Learning8 min readMachine Learning8 min read

Daksh TrehaninTowards Data Science·May 14, 2020Logistic Regression ExplainedExplaining Logistic Regression as easy as it could be. — In linear regression, the Y variable is always continuous. If the Y variable is categorical, you cannot use the linear regression model. So what would you do when the Y is a categorical variable with 2 classes? …Machine Learning4 min readMachine Learning4 min read

Daksh TrehaninTowards AI·Jun 30, 2020Member-onlySupporting the Math Behind Supporting Vector Machines!A quick tour to SVM constituting mathematical & theoretical explanation along with from the scratch implementation. — A support vector machine is another simple algorithm that every machine learning expert should have in his/her arsenal. The support vector machine is highly preferred by many as it produces data with remarkable accuracy demanding less computation power. SVM can be used for both regression and classification tasks.Machine Learning7 min readMachine Learning7 min read

Daksh TrehaninTowards AI·Jul 2, 2020Why Choose Random Forest and Not Decision TreesA concise guide to Decision Trees and Random Forest. — Decision trees belong to the family of the supervised classification algorithm. They perform quite well on classification problems, the decisional path is relatively easy to interpret, and the algorithm is fast and simple. The ensemble version of the Decision Trees is the Random Forest. Table of Content Decision Trees Introduction to Decision Trees.Machine Learning7 min readMachine Learning7 min read

Daksh TrehaninTowards AI·May 4, 2021Natural Language Processing: What, Why, and How?A complete beginner’s handbook to NLP — Table of Content: What is Natural Language Processing(NLP)? How does Natural Language Processing works? Tokenization Stemming & Lemmatization Stop Words Regex Bag of Words N-grams TF-IDF Ever wondered how Google search shows exactly what you want to see? …Machine Learning9 min readMachine Learning9 min read

Daksh TrehaninTowards AI·Jun 28, 2020Clustering: What Is It and When To use it?A comprehensive guide to K-Means, K-Means++, and DBSCAN. — Clustering is a Machine Learning technique whose aim is to group the data points having similar properties and/or features, while data points in different groups should have highly offbeat properties and/or features. Table of Content 1. K-means ⦁ Introduction to K-means ⦁ How K-means work? ⦁ Sci-kit implementation of K-means ⦁ Pros and…Machine Learning7 min readMachine Learning7 min read

Daksh TrehaninTowards AI·Jul 17, 2020Diving Deep into Deep LearningAn Unconventional Guide to Deep Learning — We reside in a world, where we are constantly surrounded by deep learning algorithms be it for the good or worse cause. From the Netflix recommendation system to Tesla’s autonomous cars, Deep Learning is leaving its stark appearance in our lives. …Machine Learning8 min readMachine Learning8 min read

Daksh TrehaninTowards AI·Jun 20, 2021How do GPUs Improve Neural Network Training?What GPU have to offer in comparison to CPU? — I bet most of us have heard about “GPUs”. There have been sayings that GPU is the best investment you can do for gaming. But the technology that once fancies the Gaming Industry, is now a core element of various other realms including Artificial Intelligence, Video Rendering, Healthcare. The two…Machine Learning5 min readMachine Learning5 min read

Daksh TrehaninTowards AI·Jul 24, 2020Convolutional Neural Networks for DummiesA perfect guide to Convolution Neural Networks — A notification pops on your Social media handle saying, somebody uploaded a picture that might have you in it. Boom! How did it happen?Machine Learning8 min readMachine Learning8 min read

Daksh TrehaninTowards AI·Jul 30, 2020Member-onlyRecurrent Neural Networks for DummiesA perfect guide to Recurrent Neural Networks — You asked Siri about the weather today, and it brilliantly resolved your queries. But, how did it happen? How it converted your speech to the text and fed it to the search engine?Machine Learning9 min readMachine Learning9 min read

Daksh TrehaninTowards AI·Aug 20, 2020Member-onlyReinforcing the Science Behind Reinforcement LearningDummies guide to Reinforcement learning, Q learning, Bellman Equation. — You’re getting bore stuck in lockdown, you decided to play computer games to pass your time. You launched Chess and chose to play against the computer, and you lost! But how did that happen? How can you lose against a machine that came into existence like 50 years ago?Machine Learning8 min readMachine Learning8 min read

Daksh TrehaninTowards AI·Aug 6, 2020Member-onlyUnderstanding LSTMs and GRUsA perfect guide to Long Short term Memory & Gated Recurrent Units. — In my last article, I have introduced Recurrent Neural Networks and the complications it carries. To combat the drawbacks we use LSTMs & GRUs. Recurrent Neural Networks for Dummies A perfect guide to Recurrent Neural Networksmedium.com The obstacle, Short-term Memory Recurrent Neural Networks are confined to short-term memory. If a long sequence is fed to the network, they’ll have a hard time remembering the information and…Machine Learning8 min readMachine Learning8 min read