NumPy Performance Comparison and Array Creation Techniques

🚀 NumPy Series – Day 2 Creating NumPy Arrays & Understanding Their Power Today I focused on the core foundation of NumPy: Arrays. Why NumPy arrays are important NumPy arrays are the backbone of numerical computing in Python. They are faster than Python lists, memory-efficient, and support vectorized operations. That’s why they are widely used in Machine Learning, Data Science, and AI. What I learned today Creating NumPy arrays I learned how to create 1D and 2D arrays using Python lists. One important rule: all elements must have the same data type. Creating arrays without loops NumPy allows creating arrays filled with zeros, ones, or custom values. Identity matrices, number ranges, and evenly spaced values can be created easily. Understanding array properties I explored how to check an array’s shape, size, number of dimensions, and data type. Changing data types I learned how to explicitly define data types and convert between float and integer to optimize memory usage. Reshaping and flattening arrays Arrays can be reshaped into different dimensions. Multi-dimensional arrays can also be flattened into a single dimension. Key takeaway NumPy makes data handling faster, cleaner, and more efficient without writing complex loops. Day 2 completed. Continuing my NumPy learning journey 🚀 #NumPy #Python #DataScience #MachineLearning #AI #LearningInPublic #LinkedIn #PythonDeveloper

Day 1 of building my foundation towards becoming an AI/ML Engineer 🚀 I’ve started with the basics that actually matter in the long run: • Python fundamentals • NumPy for numerical thinking • Pandas for understanding real-world data • Seaborn for visualizing patterns clearly Instead of rushing into models, I want to first get comfortable with how data behaves, how it’s cleaned, and how insights are extracted. Focusing on fundamentals now to avoid shortcuts later. Excited to learn, build, and share this journey step by step. #AI #MachineLearning #Python #DataAnalysis #LearningJourney

No Frameworks. Just Math. I recently stepped back from high-level frameworks like TensorFlow to build a Neural Network entirely from scratch using only Python and NumPy. My goal wasn't to reinvent the wheel, but to truly understand how it turns. What I built: • A Multi-Layer Perceptron (MLP) for diabetes prediction. • Manual implementation of Backpropagation (calculating gradients via the Chain Rule). • A custom Gradient Descent optimizer. The Reality: Writing the code was the easy part. The real challenge was debugging the math when my loss curve wouldn't converge. It forced me to dig deep into how matrix dimensions align and why derivative stability matters so much in optimization. It was a humbling experience that gave me a much deeper appreciation for the tools we use every day. You can check out my implementation here: 👇 [https://lnkd.in/dScEJUwv] #DataScience #Python #MachineLearning #DeepLearning #Coding #Growth

Why I wrote 50 lines of code when import sklearn takes three. I’ve been building Linear Regression from scratch to really get an intuition of how these algorithms work. Here is the realization that hit me: The Learning Rate is not absolute; it is relative to the scale of your data. Because I was using raw salary data ($100k+), the gradients were massive. Multiplying a massive gradient by 0.01 still resulted in a huge step size, causing the algorithm to overshoot the minima entirely. This is exactly why libraries like Scikit-Learn emphasize preprocessing pipelines. Without normalizing or standardizing your features, Gradient Descent is fighting the scale of your own data. Abstraction is great for productivity, but implementation is essential for intuition. #MachineLearning #Algorithms #DataScience #ArtificialIntelligence #Python #ScikitLearn

🚀 Built Logistic Regression From Scratch (No ML Libraries!) Today I implemented a Logistic Regression model from scratch using Python & NumPy to truly understand how classification models work. 📌 Key concepts I explored: Sigmoid function which is an activation function of this model. Gradient Descent for updating weights and bias. Cross-Entropy / Log Loss as the loss function for classification. It is used as a classification model. (Probability lies between 0 and 1). Note : Mean Squared Error can't be used as the loss function for logistic regression since it causes gradient descent with multiple local minima. I’d really appreciate feedback or suggestions on my approach — especially around optimization or best practices. Always open to learning! 🙏 Github repository link : https://lnkd.in/gfsDug8U #MachineLearning #LogisticRegression #FromScratch #Python #NumPy #DataScience #AI #LearningByDoing #Placements

💻 LinkedIn Post — Day 10: Road to Data Science Journey 🚀 Day 10 — NumPy Basics Today’s focus was on building strong foundations in NumPy, the backbone of numerical computing in Python. Here’s what I learned: ✅ Understanding what NumPy is and why it’s faster & more efficient than Python lists. ✅ Creating arrays using zeros(), ones(), arange(), and linspace(). ✅ Exploring shape, dimensions, reshaping, and indexing/slicing for efficient data handling. ✅ Grasping why NumPy is essential for data science, ML, and deep learning projects. 💡 Key Takeaways: Building strong foundations in NumPy is crucial before moving into machine learning. Vectorized operations and array manipulation make data handling faster and more efficient. Day 10 done ✅, excited to continue step by step in the Road to Data Science Journey! #DataScience #Python #NumPy #MachineLearning #DeepLearning #ContinuousLearning #RoadToDataScience

🚀 Learning NumPy – Array Fundamentals Recently, I worked on understanding how NumPy arrays are created and structured for efficient numerical computing. I explored creating NumPy arrays using Python lists and then moved on to generating arrays from scratch using methods like zeros, ones, identity matrices, ranges, and evenly spaced values. These approaches make data handling faster and more reliable compared to traditional Python lists. I also covered essential array properties such as shape, size, dimensions, and data types—key concepts that help in writing optimized and error-free code. Another important part was learning how to change data types for better memory management and how to reshape and flatten arrays when working with real-world datasets. 📄 I’ve attached a PDF/PPT with well-structured code examples and explanations for easy understanding and quick reference. Sharing my learning journey step by step and building a strong foundation in Python and Data Science. #NumPy #Python #DataScience #MachineLearning #AI #LearningInPublic #ContinuousLearning

My latest Machine Learning project involved Python and Logistic Regression. 🔍 Project: BBC News Classification 📊 Goal: Classify news articles as short or long based on description length 💡 What I learned: • How Machine Learning works end-to-end • Feature engineering and data preprocessing • Train/test split and model evaluation • Logistic Regression fundamentals • Visualizing predictions and errors This project helped me understand the difference between creating a model, training it, and evaluating its performance. 🔗 GitHub: https://lnkd.in/dqRPSjZQ #MachineLearning #Python #DataScience #LearningByDoing #AI

Day 13 of #30DaysOfPython: The Power of List Comprehension ⚡ Today was about writing "Pythonic" code. In Data Science, processing speed and code readability are paramount. I moved beyond standard loops to master List Comprehension. I implemented a Data Cleaning Pipeline that handles complex transformations in a single line of code, focusing on: 🧹 Efficient Filtering: Removing "noise" and erroneous values from raw sensor datasets. 📐 Vectorized Transformations: Performing mathematical conversions across entire lists instantly. 📖 Readability: Reducing boilerplate code to make the logic cleaner and more maintainable. It’s not just about writing less code; it’s about writing better, faster, and more professional code. 📂 View the cleaned script: https://lnkd.in/gNEUAqPS #Python #CleanCode #DataScience #MachineLearning #AI #BuildInPublic #30DaysOfPython