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Rajesh Yerremshetty 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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Naveen Pandey 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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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 - 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 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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10 Tips for Building a Robust Machine Learning Pipeline
A robust machine learning pipeline is essential for developing and deploying effective models. Here are 10 tips to build a robust machine learning pipeline: 1 – Define your problem and set your goals: Before you start building your pipeline, it’s important to define the problem that you are trying to solve and the outcome for…
Read More5 Tricks for Model Interpretability in Machine Learning
Machine learning models are becoming more advanced and complex and in order to understand a machine learning model’s behaviour and improve its performance, it is important to be able to interpret its predictions. In this article we are going to talk about 5 tricks which will help us in interpretability in machine learning. 1 –…
Read More10 Tips for Exploratory Data Analysis in Machine Learning
Exploratory Data Analysis (EDA) is a critical step in the machine learning process. It involves exploring, cleaning, and visualizing data to understand its underlying patterns and relationships. EDA helps to identify potential issues with data quality and select the appropriate machine learning algorithms for the task at hand. In this article, we will discuss ten…
Read MoreHow to Use Transfer Learning in Machine Learning
Transfer learning is a powerful technique used in machine learning where a pre-trained model is used as a starting point for a new model on a related task. Transfer learning can save you a lot of time and resources, as it allows you to leverage the knowledge of an existing model and adapt it to…
Read More10 Tips for Interpreting Machine Learning Models
Machine learning models can be complex and difficult to interpret. However, interpreting these models is crucial for understanding how they make predictions and for building trust in their outputs. Here are 10 tips for interpreting your machine learning models. 1 – Start with a simple model: Simple models like linear regression are easier to interpret…
Read More10 Tips for Debugging Your Machine Learning Models
Debugging is an important part of developing any software application, and it’s no different for machine learning models. Debugging in machine learning involves identifying and resolving errors that occur during the model development and deployment. In this article, we will share ten tips for debugging your machine learning models. 1 – Check Your Data: The…
Read MoreHow to Optimized Hyperparameters in Machine Learning
Firstly, let’s discuss what is the difference between Hyperparameters and parameters. Hyperparameters: These are the parameters which can be arbitrarily set by the data scientist to improve the performance of a machine learning model. In other words, hyperparameters are used to control the learning process of a machine learning algorithm. (eg. number of estimators in…
Read MoreHow to Avoid Overfitting in Machine Learning
Overfitting is a common problem in machine learning where a model performs well on training data, but fails to generalize well to new, unseen data. In this article, we will discuss various techniques to avoid overfitting and improve the performance of machine learning models. 1 – Cross-validation In Cross-validation we can split our dataset in…
Read More5 Tricks to Improve Your Machine Learning Models
Improving Machine Learning model can be challenging sometime. Even after trying all the strategies which you have learned, you would not get that accuracy which you are looking for. You feel irritated and helpless and this is where most of the data scientists give up. In order to become a master data scientist, you have…
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How to Work with Multiindex DataFrames in Pandas
In this blog we are going to talk about how to handle MultiIndex DataFrames in Pandas. As we know that Pandas is a powerful Python library for data analysis and manipulation. MultiIndex DataFrames are DataFrames with multiple levels of indexing, which allow for more complex and nuanced data analysis. We will look at how to…
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5 Pandas Functions for Data Wrangling
Data wrangling is the process of cleaning and transforming raw data into a structured format which can be analyzed. Pandas is a very popular library for data manipulation, offers various functions to make the process of data wrangling easier and more efficient. In this blog, we will discuss five essential Pandas functions for data wrangling…
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How to Transform Data in Python
Pandas is a powerful and popular library for data processing and analysis in Python. It offers a wide range of functions to help transform and reshape data according to specific needs. In this article, we’ll explore some of the most common ways to transform data in Pandas. Creating a DataFrame Let’s start by creating a…
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