Previous Post (The Art of Data Visualisation) Next Post (Line Chart) Table of Contents 1. Introduction — What is a Scatter Plot? — When to Use a Scatter Plot — Python Libraries with Scatter Plot Functionality 2. Creating Scatter Plots in Python (with examples) — Example 1: Exploring a Positive Correlation — Example…

Next Post (Scatter Plots) Welcome to the world of data visualization! In this blog series, we’ll embark on a journey to explore the fascinating realm of data visualization using Python. Data visualization is a crucial aspect of data science and analysis. …

Reading a research paper about AI and using it to write code for a model or method requires a combination of understanding the paper’s content and translating its concepts into practical code. Here’s a step-by-step guide along with some tips and tricks: 1. Choose the Right Paper: Select a paper…

TensorFlow models are widely used in machine learning applications, and deploying them on resource-constrained devices often involves converting them to TensorFlow Lite (.tflite) format. This guide explores various approaches for converting a TensorFlow .pb model to .tflite, catering to different TensorFlow versions and model formats. Whether you’re using TensorFlow 1.x…

The word “mediocre” in English is used to describe something that is of only average quality, neither very good nor very bad. It’s often used to imply a level of quality that is not particularly impressive or noteworthy. The origins of “mediocre” can be traced back to Latin, specifically to…

Related Article: Understanding Regularization in Machine Learning and Deep Learning Introduction In this blog post, we will delve into this interview question to provide you with a comprehensive answer that will help you stand out in your machine learning and data scientist job interview. But before we dive into the depths…

Table of Contents 1. Introduction 2. Batch Learning 3. Online Learning 4. Comparison: Batch vs. Online Learning 5. When to Use Batch or Online Learning 6. Interview Questions for Data Scientists and Machine Learning Engineers 7. Conclusion 1. Introduction In the field of machine learning, the choice of learning approach can have a profound…

Introduction Formal Explanation: Regularization is a technique used in machine learning to address the problem of overfitting. Overfitting occurs when a model becomes too complex and fits the training data too closely, making it perform poorly on unseen test data. Regularization helps prevent overfitting by adding constraints or penalties to the…

Table of Contents 1. Introduction 2. The Role of Venture Capitalists 3. The Venture Capital Process 4. Exit Strategies 5. Value-Added Services 6. Economic Impact 7. Challenges and Considerations 8. Conclusion 1. Introduction