• # Math Heavy Topics in Data Science!￼

1. Support-vector machine Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic […]

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• # Analyzing Diabetes Patterns amongst Indians, A Beginner’s Guide to Pearson’s Correlation Coefficient, Deep Learning in Cyber Security & Much More!

1. Juicing out the Diabetes Patterns amongst Indians using Machine Learning The data indicates an increase of 266% in the population of diabetics is going to be witnessed by developing countries. The score of the training model was a magnificent 100% which means it classified all the elements correctly as is evident as a result […]

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• # Interview with a Kaggle Master & More

1. Exclusive Interview with 2x Kaggle Master Gilles Vandewiele! “I think one of the nice things about the data science field is that it is so multi-disciplinary and that anyone who aspires to become a data scientist can do so.” – Gilles Vandewiele Golden words! As a beginner in data science, this quote gives me […]

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• # Top 5 Data Science & Analytics Online Courses

There is an increasing demand for data science experts in different industries today. In this data-driven economy, it is only natural for data to be a valuable asset in the efficient working of an organization. However, finding a job in this sector requires you to have a set of certain skills and some solid educational […]

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• # Resources to learn Linear Regression

Linear regression shows the linear relationship between the independent(predictor) variable i.e.Linear regression is a quiet and the simplest statistical regression method used for predictive analysis in machine learning. How a Math equation is used in building a Linear Regression model? Do you know that this one equation helps in building a linear regression model in the machine learning world? Yes, you heard it right.From the school days, we have come across the equation of the straight line.

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• # Basics of Data Science using Python

Basics of Data Science using Python After going through the topic above, what are the questions that come to your mind? It must be, why python? How can we use python to implement data science? And, what are the advantages and disadvantages? We will be answering all these questions and we are also going to talk about the libraries that we can use to implement data science. 1. NumPy NumPy arrays are similar to Python’s built-in list type in some ways, but NumPy arrays provide much more efficient storage and data operations as the arrays grow in size. 2. SciPy It is built on top of the Numpy library, which provides more extensions for finding scientific mathematical formulae such as Matrix Rank, Inverse, polynomial equations, LU Decomposition, and so on. 3. Pandas Pandas is a pillar library in any data science workflow because it allows you to perform data processing, wrangling, and munging. 4. Matplotlib This library is built on NumPy arrays and includes several plots such as line charts, bar charts, histograms, and so on. 5. Scikit Learn Scikit-learn is by far one of the most important Python libraries for machine learning, as it allows you to create machine learning models while also providing utility functions for data preparation, post-model analysis, and evaluation.. 6. TensorFlow TensorFlow is a software library that uses data flow graphs to perform numerical computations. 7. Keras It was created with the goal of allowing for quick experimentation .Keras is a Python library that is widely used for deep learning model training. 8. PyTorch PyTorch takes these tensors and makes it simple to move them to GPUs for faster processing during neural network training.

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• # 10 DATA SCIENCE SPECIALIZATIONS

It is absolutely normal that we come across a growing number of specializations within the field of Data Science. This will help you in understanding in which area you want to work eventually. 1. DATA MINING Data mining, also known as knowledge discovery in data (KDD) is the process of finding and extracting anomalies and discovering patterns in large data sets to predict outcomes. In simple terms, the main aim of data mining is to extract information with intelligent methods and transform the information into a comprehensible structure. 2. DATA VISUALIZATION It provides a convenient way of understanding trends, outliers, and patterns in data. Data visualization is the domain that deals with the graphic representation of data through visual elements like- charts, graphs, maps, and other visualization tools. 3. DATA PROCESSING We can also call it manipulation of data by computers inclusive of output formatting or transformation. Data processing is when you collect data and transform data into useful information. 4. DATA CONSULTANCY It also involves educating companies or clients about various aspects of data technology. They provide a wide range of methods that optimizes business intelligence by leveraging existing data. 5. MARKET DATA ANALYTICS Through it, we can identify the strengths, weaknesses, opportunities, and potential threats of a company. It looks into the depths of consumer segments, buying patterns, competition, and the economic environment. 6. CYBERSECURITYRTY DATA ANALYSIS These experts produce intelligence to improve the security and privacy of data of an organization from external and internal threats. Cybersecurity uses data science to protect software and devices from cyberattacks. 7. DATA ARCHITECTURE Data architecture refers to how an organization collects, store, transform, distribute and use data. These days it is important for organizations to have centralized data architecture in accordance with industry standards. 8. DATA ENGINEERING Their primary job is to design, manage and optimize the flow of data with databases throughout the organization. It is the practice of designing and building data systems for collecting, storing, and analyzing data at scale. 9. BUSINESS INTELLIGENCE AND DATA ANALYTICS It describes the strategies, technologies, and tools companies further use to obtain important business information. Data analytics ad data analytics are their subsets that provide data management solutions to understand contemporary data and gain relevant insights. 10. COGNITIVE MACHINE LEARNING Cognitive computing systems work with humans and provide them with advice in making informed decisions. It intends to use the best algorithm and come up with an accurate action/result.

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• # 7 Tips to Craft a Perfect LinkedIn Profile

If you have any certifications and licenses or if you have worked on some major project, it is time to show off your work! This is an important section and you should definitely not miss this if you are willing to present yourself as a highly-skilled professional. This will help the recruiter to understand your strengths. One thing to make sure of is that you do not need to put a long list of skills. The about section of your profile should be able to articulately define you as a professional and a person. Don’t make it too short nor make it too long. You also have the option of adding media samples that can quickly access your work portfolio. It doesn’t matter if you are a fresher or an experienced professional, check put some good profile descriptions before writing work experience on LinkedIn. And the bonus part is- that we will be adding some snippets from our website’s founder’s LinkedIn profile as well to help you gain a better understanding of how things work. You can go through your LinkedIn network and endorse connections who genuinely deserve it and this in turn will prompt people to do the same for you. You have to be mindful of the keywords you use as they can make or break your profile. One of the best things you can do for your career is to formulate a strong LinkedIn profile well-suited according to your professional needs. Be active in managing your endorsements and you can also choose which ones to show and which ones to hide. You can take a step further by adding recommendations. A few things that make up for a good summary include- your passion, motivation, skills, experience, fields where you have excelled, and other relevant accomplishments.

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• # Decision Tree From Scratch!! -Part I

Introduction In this blog post, I am going to talk about a powerful supervised learning algorithm that is often used in Machine Learning competitions. It is called the Decision Tree algorithm. It can be used for both classification & regression tasks. In this post, I will discuss the need for tree-based algorithms, the basics of […]

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• # The Power of LinkedIn Networking

LinkedIn is one of the largest social networks which helps you in expanding your professional sphere on the internet. It can be used to find suitable jobs or internships, initiate and strengthen professional relationships and master the skills you need to succeed in your career. Alongside building a network, you can gain access to infinite […]

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• # Logistic Regression in Machine Learning (from Scratch !!)

Introduction In this blog post, I would like to continue my series on “building from scratch.” I will discuss a linear classifier called Logistic Regression. This blog post covers the following topics, Basics of a classifier Decision Boundaries Maximum Likelihood Principle Logistic Regression Equation Logistic Regression Cost Function Gradient Descent Algorithm After the discussion of […]

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• # KNN in Machine Learning (from Scratch !!)

K-Nearest Neighbours: Introduction Birds of a feather flock together. William Turner The above quote perfectly sums up the algorithm that we are going to talk about in this post. KNN stands for K-Nearest Neighbours. It is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. Note: […]

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• # Data Science Starters

So, this post is for someone who wants to learn Data Science but is not able to find where to start from. In this post, I will share some crucial resources (free) that would help you get started with your Data Science journey, Programming So, to get started with Data Science you must be fluent […]

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• # Linear Regression in Machine Learning (from Scratch !!)

Introduction In this post, I will talk about one of the most crucial techniques in Regression Analysis/Machine Learning, called Linear Regression. As per Wikipedia, Regression Analysis is defined as a set of statistical processes used to estimate the strength of the relationship between a dependent variable and an independent variable. The process which tries to […]

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• # How to achieve multiple offers in Data Science as a College Student…

Introduction First of all, I would like to introduce myself. My name is Chitwan Manchanda and I am currently pursuing B.Tech in Mechanical Engineering from Delhi Technological University. I have offers from Simpl, Editorialist YX and Slice for Data Science roles. I was in my penultimate year when the Covid-19 Pandemic hit and we all […]

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• # Top ML Reddit Discussions, NLP Roadmap & Much More!

Top 5 Machine Learning GitHub Repositories & Reddit Discussions Why do we include Reddit discussions in this series? I have personally found Reddit an incredibly rewarding platform for a number of reasons — rich content, top machine learning/deep learning experts taking the time to propound their thoughts, a stunning variety of topics, open-source resources, etc. […]

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• # Data Science Resources, ETL Practices, Beginner’s guide to Seaborn

1. Most Active Data Scientists, Free Books, Notebooks & Tutorials on Github In this article, I’ve listed the most active data scientist on GitHub, so that you can follow & see what are they up to (especially projects). Before moving forward, check out this ~ 2 minutes video on students using Github! Open Source Data […]

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• # Natural Language Processing Usecases with Python

1. Master Natural Language Processing in 2022 with Best Resources As already mentioned earlier, Deep Learning is a subdomain of machine learning. It is far more generalized as it comes up with generalized predictions compared to traditional machine learning due to the introduction of Artificial Neural Networks or ANN. Practicing NLP with Deep Learning is an […]

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