✓ Advance Python Course with Machine and Deep Learning. ✓ Exercise ( Task 02 ). ✓ Statement:- 1) ----- Write a python program that asks the user for the two whole numbers. 2) ----- Calculate the product ( multiply them ). 3) ----- If the product is 1000 or less, the program should show the product. 4) ----- If the product is more than 1000, the program should show the sum ( add them ) instead. #LearningInPublic #CodingNewBie #PythonCourse #Programming #FutureGoals #Coding

✓ Advance Python Course with Machine and Deep Learning. ✓ Exercise ( Task 01 ). ✓ Statement:- 1) ----- Write a python program that asks the user for the two whole numbers. 2) ----- Calculate the product ( multiply them ). 3) ----- If the product is 1000 or less, the program should show the product. 4) ----- If the product is more than 1000, the program should show the sum ( add them ) instead. #LearningInPublic #CodingNewBie #PythonCourse #Programming #FutureGoals #Coding

🚀 New GitHub Project: Gradient Descent Variants with MSE Loss (PYTHON) I recently implemented a small machine learning project that demonstrates three fundamental optimization methods used in training models: • Batch Gradient Descent • Stochastic Gradient Descent (SGD) • Mini-Batch Gradient Descent The project shows how these algorithms minimize the Mean Squared Error (MSE) loss and how different update strategies influence model training and convergence. 🔎 This project focuses on the core ideas behind gradient-based optimization and provides a simple and modular Python implementation that can be used for learning or experimentation. You can explore the implementation here: 🔗 https://lnkd.in/dsXFryTD I enjoy building small focused projects like this to strengthen my understanding of the fundamentals behind machine learning algorithms. #MachineLearning #Optimization #GradientDescent #Python #DataScience #ArtificialIntelligence #GitHub

Day 4 of learning Python in public 🚀 Today I focused on understanding Python Lists and how Python works with collections of data. Key things I learned: • Creating lists and storing multiple values in a single structure • Accessing elements using indexing and negative indexing • Using slicing to retrieve specific ranges of elements • Adding items using append(), insert(), and extend() • Removing items using remove() and pop() • Updating list elements using indexing • Checking if an element exists in a list using the in operator • Sorting lists using sort() and sort(reverse=True) • Important list methods like count(), index(), copy(), and clear() • Working with nested lists and understanding matrix[row][column] access • Using enumerate() to get both index and value while looping • Using zip() to combine multiple lists together • Writing concise transformations using list comprehension Big takeaway: Lists are one of the most fundamental data structures in Python. Understanding how they work makes data manipulation much easier and builds a strong foundation for more advanced concepts. Continuing to strengthen the fundamentals step by step. #Python #DataScience #LearningInPublic #Programming #DataScienceJourney #softwareengineering #AI #MachineLearning

🚀 Day 1 of My Artificial Intelligence Learning Journey Today I started strengthening my Python fundamentals, which are essential for learning Artificial Intelligence and Machine Learning. Here are some concepts I learned today: 🔹 Python Variables – used to store and manipulate data 🔹 Variable Naming Rules – proper naming conventions in Python 🔹 Python Data Types – int, float, string, list, tuple, dictionary, set, boolean 🔹 Strings in Python – text data using single or double quotes 🔹 Variable Scope – local vs global variables 🔹 Python Operators – arithmetic, assignment, comparison, logical, membership, and bitwise operators 📌 Key Takeaway: A strong understanding of Python fundamentals is important before diving deeper into AI and Machine Learning. This is Day 1, and I’m excited to continue learning and sharing my journey. #Python #ArtificialIntelligence #MachineLearning #AIJourney #LearningInPublic

Most beginners ignore this pandas feature in Python! 🐍 If you're learning data science, understanding modern Pandas data types is very important for efficient data analysis. In this short video, you will quickly learn: ✔️ What modern Pandas data types are ✔️ Why they are better than traditional types ✔️ How they help in better data handling Perfect for Python, Data Science, and Machine Learning learners. 💬 Question: Have you used modern data types in Pandas before? Follow TuxAcademy and subscribe to our YouTube channel for more content on AI, Data Science, and Machine Learning. https://lnkd.in/gaipCupJ #Python #DataScience #Pandas #MachineLearning #Programming #TuxAcademy

🚀 New Video: Ridge vs Lasso vs Elastic Net in Python | Regularization Techniques Explained In machine learning, building models that generalize well to unseen data is critical. One of the most powerful ways to control model complexity and prevent overfitting is Regularization. In my latest YouTube video, I explain and demonstrate three widely used regularization techniques: 🔹 Ridge Regression (L2 Regularization) – Shrinks coefficients to reduce model variance and handle multicollinearity. 🔹 Lasso Regression (L1 Regularization) – Performs automatic feature selection by forcing some coefficients to become zero. 🔹 Elastic Net Regression – Combines L1 + L2 penalties, balancing feature selection and coefficient shrinkage for better performance when predictors are correlated. 📊 In this tutorial, you will learn: ✔ The intuition behind regularization ✔ Mathematical differences between Ridge, Lasso, and Elastic Net ✔ When to use each technique ✔ Hands-on implementation using Python and Scikit-Learn 🎥 Watch the full video here: https://lnkd.in/dRriSAj9 This video is especially useful for students, data analysts, and machine learning practitioners who want to strengthen their understanding of regression modeling and feature selection. #MachineLearning #DataScience #Python #ScikitLearn #Regression #Regularization #RidgeRegression #Lasso #ElasticNet #AI #DataAnalytics

Starting my NumPy journey with a simple observation: Python List vs NumPy Array While learning Python, I mostly worked with lists to store data. They are simple and flexible. But after starting NumPy, I noticed that the same data can also be stored in something called a NumPy array. At first glance, both look very similar. But internally they are built for different purposes. Python List • Flexible and easy to use • Can store different data types • Mostly used for general programming tasks NumPy Array • Stores elements of the same type • Optimized for numerical and mathematical operations • Much faster when working with large datasets So, Output should be: <class 'list'> <class 'numpy.ndarray'> This is one of the main reasons why NumPy is widely used in Data Science, Machine Learning, and AI applications. Right now I’ve started exploring NumPy step by step as part of my Python → Data → ML learning journey. Next, I’ll explore multi-dimensional arrays in NumPy. #Python #NumPy #MachineLearning #DataScience #LearningInPublic

🐍 Learning Python – Arithmetic Operations Today, I practiced basic arithmetic operations in Python using variables. 📌 What this program does: ✅ Stores numeric values in variables ✅ Performs addition and subtraction ✅ Prints the calculated results using print() 💡 This helped me understand how Python handles mathematical operations and how variables make calculations easy and reusable. I’m learning Python step by step and strengthening my programming fundamentals for my journey towards Artificial Intelligence and Machine Learning 🚀 Feedback and suggestions are welcome 😊 #Python #PythonBeginner #ArithmeticOperations #LearningPython #CodingJourney #Programming #SoftwareEngineering #AI #MachineLearning

🚀 Day 56/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: • Overfitting and underfitting Today, I focused on understanding overfitting and underfitting, two key challenges in building reliable machine learning models. I learned that underfitting occurs when a model is too simple and cannot capture the underlying patterns in the data, resulting in poor performance on both training and testing data. On the other hand, overfitting occurs when a model is too complex and memorizes the training data, including noise, which leads to high accuracy on training data but poor performance on unseen data. I also explored how model complexity directly impacts performance and why it is important to choose the right model and parameters. Understanding these concepts is essential for building robust models that perform well in real-world scenarios. The learning journey continues as I dive deeper into machine learning concepts 🚀 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience 🚀