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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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Language Detection Project using Machine Learning
Speech recognition is a natural language processing task that requires identifying the language of a text or document. Using machine learning for speech recognition was a difficult task a few years ago due to the lack of much data on language, but now that data is readily available, several powerful machine learning models are already available. So, if you want to learn how to train machine learning models for speech recognition, this article is for you. This…
Read MoreCounting Objects in An Image
Counting objects in an image is the job of computer vision. There are many Python computer vision libraries available for this task. However, this article describes a very simple approach to counting objects in images using Python. How to count objects in an image using Python? Counting objects in an image is a computer vision task. There are many image processing libraries available for this task. B. OpenCV, TensorFlow, PyTorch, Scikit-image, and cvlib. You probably haven’t heard much about Python’s cvlib library. This is a very simple, advanced, and easy-to-use…
Read MoreProduct Demand Prediction Project Using Machine Learning
You must have learned that demand for a product change as the price of the product changes. To give a real-life example, if a product is not needed, demand decreases when price increases, and demand increases when price decreases. If you want to know how to use machine learning to predict product demand, this article…
Read MoreCalories Burnt Prediction Project using Machine Learning
In this article, we will learn how to develop a machine learning model using Python which can predict the number of calories a person has burnt during a workout based on some biological measures. You can download Calories dataset from here and Exercise dataset from here. we will import all the necessary libraries and also warnings which we take care…
Read MoreExplanation for AI and Data Science by ChatGPT AI
Question to ChatGPT: Explain AI Artificial intelligence (AI) is the ability of a computer program or a machine to simulate human intelligence, including the ability to reason, learn, and solve problems. AI can be applied to a wide range of field, including robotics, natural language processing, computer vision, and machine learning. The goal of AI…
Read MoreImportant Machine Learning Concepts Part – 2
Ensemble Learning Training multiple models with different parameters to solve the same problem. A/B Testing Statistical way of comparing 2+ techniques to determine which technique performs better and also if difference in statistically significant. Baseline Model Simple model/heuristic used as reference point for comparing how well a model is performing. Bias Prejudice or favourite towards…
Read MoreImportant Machine Learning Concepts Part – 1
Features Input data/variables used by the ML model. Feature Engineering Transforming input features to be more useful for the models. e.g., mapping categories to buckets, normalizing between -1 and 1, removing null. Train/Eval/Test Training is data used to optimize the model, evaluation is used to asses the model on new data during training, test is…
Read MoreWhat is selection Bias?
Selection bias is a kind of error that occurs when the researcher decides who is going to be studied. It is usually associated with research where the selection of participants isn’t random. It is sometimes referred to as the selection effect. It is the distortion of statistical analysis, resulting from the method of collecting samples.…
Read MoreWhat is a confusion matrix?
The confusion matrix is a 2×2 table that contains 4 outputs provided by the binary classifier. Various measures, such as error-rate, accuracy, specificity, sensitivity, precision and recall are derived from it. Confusion matrix. A dataset used for performance evaluation is called a test data set. It should contains the correct labels and predicted labels. The…
Read MoreWhat is the ROC curve?
The ROC curve is a graph between False positive rate on the x axis and True positive rate on the y axis. True positive rate is the ratio of True positives to the total number of positive samples. False positive rate is the ratio of False positives to the total number of negative samples. The…
Read MoreWhat do you understand by true positive rate and false-positive rate?
True Positive rate (TRP) is the ratio of True Positives to True Positives and False Negatives. It is the probability that an actual positive will test as positive. TPR = TP / TP + FN The False Positive Rate (FPR) is the ratio of the False Positives to all the positives (True positives and false…
Read More3 Concepts Every Data Scientist Must Know Part – 3
1. What is the significance of sampling? Name some techniques for sampling? For analyzing the data, we cannot proceed with the whole volume at once for large datasets. We need to take some samples from the data which can represent the whole population. While making a sample out of complete data, we should take the…
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