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Essential Data Structures in Python

Zero to Python Hero – Part 5/10: Essential Data Structures in Python: Lists, Tuples, Sets & Dictionaries

The fundamental way of storing, accessing and manipulating of data in python is data structures. Python provides an convenient and adaptable collection of objects to store and data and sort it in different ways, be it a list, a tuple,...

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Top 5 Skills Every Engineer Should Learn in 2026

The world of engineering is changing faster than ever before. Technologies that were once futuristic like artificial intelligence, machine learning, and cloud computing are now driving industries forward. By 2026, the engineers who thrive won’t just be the one who...

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Zero2 to Python Hero

Zero to Python Hero - Part 4/10 : Control Flow: If, Loops & More (with code examples)

A major element of any programming language is the capability to take decisions and repeat them -this is the so-called control flow. Control flow is a feature available in Python that enables us to have the control of how code...

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Zero to Python Hero

Zero to Python Hero - Part 3/10 : Understanding Type Casting, Operators, User Input and String formatting (with Code Examples)

Type Casting & Checking What is Type Casting? Type casting (also called type conversion) is the process of converting a value from one data type to another. It’s like translating between different languages  – sometimes you need to convert a number to...

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Dynamic Programming with Reinforcement Learning

Dynamic Programming in Reinforcement Learning: Policy and Value Iteration

The core topic of reinforcement learning (RL) Dynamic Programming in RL: Policy and Value Iteration Explained provides fundamental solutions to resolve Markov Decision Processes (MDPs). This piece teaches about Policy Iteration and Value Iteration alongside their mechanisms as well as...

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Latest Articles

Outlier Detection methods in Machine Learning

Objective An outlier is an individual point of data that is distant from other points in the dataset. It is an anomaly in the dataset that may be caused by a range of errors in capturing, processing or manipulating data. Outliers in the data may cause problem during model fitting as it may inflate the…

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Missing Values Treatment methods in Machine Learning

Delete Missing Value Rows Missing values can be handled by  deleting the rows or columns having null values. If columns have more than half of the rows as null then the entire columns can be dropped. The rows which are having one or more columns values as null can also dropped. Pros: A model trained…

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NumPy for Data Science – Part 3

Arithmetic Operations in NumPy Arrays In NumPy there are multiple functions which we can use to perform the arithmetic operation, we will be looking them one by one. The add() function can also be used to perform the same operation. The subtract() function can also be used to perform the same operation. The multiply() function…

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NumPy for Data Science – Part 2

Create NumPy Arrays with Random Numbers Data Types in NumPy Arrays Shape and Reshaping In NumPy Arrays Shape of an Array The shape of an array is the number of elements in each dimension. Get the Shape of an Array NumPy arrays have an attribute called shape that returns a tuple with each index having the number…

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NumPy for Data Science – Part 1

What is NumPy Array? An array is a grid of values and it contains information about the raw data, how to locate an element, and how to interpret an element. Numpy vs Python List Advantages of using NumPy Arrays over Python List: Let’s look at the example of NumPy Array and Python List. Importance of…

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Restaurant Recommendation System using Machine Learning

In this article we are going to discuss about the Restaurant Recommendation System. it is an application that recommends similar restaurants to a customer according to the customer’s taste. We will learn how to build a restaurant recommendation system. This article will take you through how to build a restaurant recommendation system using Machine Learning.…

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Hotel Sentiment Analysis using NLP

Whenever we are trying to find hotels for vacation or travel, we always prefer a hotel known for its services. The simplest way to find out whether a hotel is right for you or not is to find out what people are saying about the hotel who have stayed there before. Now it’s very difficult…

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Difference between Pandas .at and .iat Function

.at The .at and .iat index accessors are analogous to .loc and .iloc. The difference being that they will return a numpy.ndarray when pulling out a duplicate value, whereas .loc and .iloc return a Series: .iat .iat is similar to [] indexing. Because it tries to support both positional and label based indexing, I advise…

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Top 10 Pandas Functions

1 – To Read CSV and Excel files. These Functions will be used in almost every Project, They are used to read a CSV or an excel file to pandas DataFrame format. 2 – Columns Function. When we have a big dataset with many columns it will be difficult to see all columns, hence we…

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Difference between Pandas .iloc and .loc function

The optimized data access methods are accessed by indexing off of the .loc and .iloc attributes. These two attributes allow label-based and position-based indexing respectively. When we perform an index operation on the .iloc attribute, it does lookup based on index position (in this case pandas behaves similar to a Python list). DataFrame operation: .loc…

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Hierarchical clustering for Machine Learning

Hierarchical clustering is another unsupervised machine learning algorithm, which is used to group the unlabeled datasets into a cluster. Hierarchical Clustering creates clusters in a hierarchical tree-like structure (also called a Dendogram) as it creates a subset of similar data in a tree-like structure in which the root node corresponds to the entire data, and…

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Difference between Data Science and Machine Learning

Data Science Data science is a field that studies data and how to extract meaning from it, using a series of methods, algorithms, systems, and tools to extract insights from structured and unstructured and unstructured data. That knowledge then gets applied to business, government, and other bodies to help drive profits, innovate products and services…

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