Part of the journey is the end ~ Robert Downey Jr.

But I believe the journey has just begun. WWCD Mentorship program gave me a great opportunity to be mentored, to be heard, to be understood, and to be helped. And I wish to take that experience to help more people in whichever way I can.

On this note, let’s dive right into my experience of Week 5. You can read more about my previous experiences here: Week 1, Week 2, Week 3, and Week 4.

Week 5 was majorly focussed on “Creating Your Presence”. We started off the call…

After an amazing Week-3, we stepped into Week-4 of the mentorship program. Week 4 was all about interviews and how to prepare for them. This week’s mentorship call was extremely fruitful because our mentor, Saumya Singh, shared some valuable resources for interview preparation.

At the beginning of our call, Saumya inspired all of us with the interview experience she shared and put forward some tips to ace them. She also shared a specially curated company-specific question bank that will definitely help us. They consisted of Leetcode problems, shared by her peers. …

So we’re three weeks into the Mentorship Program and so far it’s been great. You can read more about my enriching experience here- Week 1, Week 2.

In Week 3, we dived more into Open Source and how one can contribute to it.

Our mentor, Saumya Singh told us that in open source, it is just not the project that helps you in skill-building, but the networking is also vital and beneficial. It allows you access to opportunities you might not be able to find on your own. …

After an enlightening week, we moved on to Week 2 of WWCD Mentorship Program 3.0. In Week 1, we discussed various programs and opportunities like GSSoC, GHC, etc. We also talked about contributing to open source and how welcoming and rewarding the open-source community is. You can read about my Week-1 experience here.

Week-2 was aimed towards internship and placement opportunities. Our mentor, Saumya Singh, started by telling us about Leetcode. She suggested that for understanding DSA concepts, one can refer GeeksforGeeks and Youtube and for practicing questions, Leetcode. She talked about using Leetcode efficiently. She was kind enough to…

“ Regardless of our title or years of experience, we can learn from each other. Through mentoring and by being open to learn we can reach our ultimate potential.” –Lily Benjamin

Recently, I came across a post from Women Who Code Delhi. Women Who Code is an international nonprofit organization dedicated to inspiring women to excel in technology careers. They were inviting applications for their Mentorship Program 3.0. Through their LinkedIn page, I learned about great mentors who have done great work in their respective fields. I applied to the program and fortunately, my application was accepted. …

Linear Regression is one of the first algorithms you will learn when you begin your journey into the fields of Data Science and Machine Learning. Through this blog, we will not only understand what Linear Regression is but also implement the algorithm from scratch.

Now, before we dive into it, let us first understand what Regression is.

In simple terms, Regression is a method used to determine the relationship between a dependent variable and one or more independent variables. It lets you understand patterns in the data.

The independent variables are also known as features and the dependent variable is…

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What is a Confusion Matrix and why do we need it?

Suppose that we have a simple binary classification problem: Given some datasets, our model should indicate whether the person has heart disease or not. Now we train a model and it's time to evaluate it. But how can we evaluate our model’s performance? How effective is our model? Better the effectiveness, better the performance. What is a good metric for doing so? It is where the Confusion matrix comes into play.

Through this blog, I aim to clear all your confusion about “Confusion Matrices”.

Let us first consider what it means for the model to perform well. If the model…

Machine learning is a data science technique used to extract patterns from data, allowing computers to identify related data, and forecast future outcomes, behaviors, and trends.

Before we start breaking the above statement down, let us first understand how machine learning is different from traditional programming.

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In Traditional Programming, we provide hard-coded rules and data to the computer to get output or results or answers. In other words, the set of rules by which the computer has to operate on the data are provided beforehand.

Whereas in Machine Learning, we provide the data and results or answers to the computer…

Somya Maheshwari

learning, growing.

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