Rebuilding consistency in Machine Learning fundamentals

🗓 Day 34 of my #BackToFlow journey to rebuild consistency — back to Machine Learning basics with Simple Linear Regression 📈 Today’s focus: Introduction to Simple Linear Regression → One of the simplest yet most powerful ML algorithms that models the relationship between two variables. Understanding the Equation → Form: y = mx + c Where m = slope (how much y changes with x), and c = intercept (where the line crosses y-axis). Explored how this line is used to predict outcomes and minimize error between predicted and actual values. Even though it’s a beginner-friendly algorithm, it’s the foundation for more complex regression and ML models. 🚀 #MachineLearning #LinearRegression #DataScience #LearningJourney #BackToFlow #Consistency

Mastering Machine Learning-One Visual at a Time Just shared this structured visual roadmap of Machine Learning that helped me connect the dots across algorithms, learning types, and real-world applications. From classic models like Linear Regression and K-Means to advanced techniques like Transformers, GANs, and Q-Learning, this diagram lays out the ML universe in one glance.

🗓 Day 31 of my #BackToFlow journey to rebuild consistency— stepping into the world of Machine Learning 🤖 Today’s focus: Types of ML Techniques → Supervised, Unsupervised, and Reinforcement Learning. Equation of a Line → Revisiting the basics (y = mx + c) to understand decision boundaries. 3D Visualization → Extending linear equations into 3D space for multiple features. Hyperplane → The foundation of separating classes in higher dimensions (key for algorithms like SVM). It feels great to finally move from statistics & feature engineering into the core ML concepts. This is where the math meets real-world problem-solving. 🚀 #MachineLearning #DataScience #BackToFlow #LearningJourney #Consistency

🌳 "From roots to branches, Decision Trees make learning in ML feel natural." On Day 32 of my ML journey, I started exploring Decision Trees — one of the most intuitive yet powerful algorithms. Excited to dive deeper into how they split data, reduce impurity, and make predictions step by step. 🚀 #MachineLearning #Day32 #DecisionTrees #MLJourney #100DaysOfML #LearningInPublic

Diving deeper into the math powering Machine Learning! Over the past few weeks, I’ve explored some challenging yet fascinating topics that form the core of optimization and dimensionality reduction in ML: 📚 Topics I worked on: 🔹 Singular Value Decomposition (SVD) 🔹 Definiteness of a Matrix 🔹 Principal Component Analysis (PCA) 🔹 Optimization (Unconstrained & Constrained) 🔹 Gradient Descent 🔹 Lagrangian Multipliers & KKT Conditions Understanding these concepts made me realize how much ML relies on mathematical rigor — from reducing dimensions with PCA to solving optimization problems using constraints and gradients. Every equation I solved gave me a clearer picture of what’s really happening under the hood of ML algorithms. 🚀 #MachineLearning #Optimization #PCA #MathForML #DataScience #MLJourney

When I first started learning machine learning, I was always looking for practical projects that explained both the code and the thought process behind it. That’s why I created this beginner-friendly repo: "𝑻𝒊𝒕𝒂𝒏𝒊𝒄 𝑳𝒐𝒈𝒊𝒔𝒕𝒊𝒄 𝑹𝒆𝒈𝒓𝒆𝒔𝒔𝒊𝒐𝒏 𝑳𝒆𝒄𝒕𝒖𝒓𝒆" In this project, I walk step by step through: - 𝗛𝗼𝘄 𝘁𝗼 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝗮𝗻𝗱 𝗰𝗹𝗲𝗮𝗻 𝗱𝗮𝘁𝗮. - 𝗛𝗼𝘄 𝘁𝗼 𝗱𝗲𝗰𝗶𝗱𝗲 𝘄𝗵𝗶𝗰𝗵 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 𝗮𝗻𝗱 𝘄𝗵𝘆. - 𝗛𝗼𝘄 𝘁𝗼 𝗮𝗽𝗽𝗹𝘆 𝗹𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗼𝗻 𝗮 𝗿𝗲𝗮𝗹 𝗱𝗮𝘁𝗮𝘀𝗲𝘁. - 𝗛𝗼𝘄 𝘁𝗼 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝗿𝗲𝘀𝘂𝗹𝘁𝘀. My goal was to show not just the mechanics of machine learning, but also how to behave with data when you approach a problem for the first time. This is not a deep or advanced project, it is written in a teaching style for 𝗯𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀 who want a practical starting point in ML. You can find the repo here: https://lnkd.in/djzAjGVb #MachineLearning #LogisticRegression #MLProjects #FeatureSelection #DataPreprocessing #ModelEvaluation #TeachingMachineLearning #PracticalML

🚀 Learning Update: Mastered K-Nearest Neighbors (KNN)! Over the past few days, I’ve been diving deep into one of the simplest yet powerful machine learning algorithms — K-Nearest Neighbors (KNN). It’s been exciting to see how such an intuitive approach can be applied to both classification and regression problems. 🔹 Classification: I explored how KNN predicts the class of a new data point by looking at the majority class of its nearest neighbors. It was great to see how the choice of k (number of neighbors) directly impacts the performance — too small k risks overfitting, while too large k may underfit. 🔹 Regression: I also implemented KNN for regression, where predictions are based on the average values of the nearest neighbors. This gave me hands-on insights into performance metrics like R² score, MAE, and MSE, which are more suitable than accuracy in regression. 🔹 Distance Metrics & Search Algorithms: I learned how KNN uses different distance metrics like Euclidean and Manhattan, and how performance can be optimized with Ball Tree and KD Tree for faster neighbor searches. Finally, I applied GridSearchCV to systematically tune hyperparameters (like k) and achieve better results. 💡 Key takeaway: KNN is simple to understand and implement, yet highly effective for many problems when tuned properly. Excited to move forward and continue my ML journey with more advanced algorithms! #MachineLearning #KNN #DataScience #LearningJourney

This week, I started experimenting with machine learning to predict stock prices. Here’s what I’ve learned so far: ✅ Cleaning and preparing the OHLCV data (Open, High, Low, Close, Volume) took way more time than coding the model. ✅ Simple baselines like “yesterday’s price = today’s prediction” are surprisingly strong — a reminder that ML isn’t always about complex models. ✅ Next, I’ll test LSTM networks vs. gradient boosting to see which handles time series patterns better. Why am I doing this? 👉 To sharpen my data science skills. 👉 To connect my interest in finance with hands-on machine learning. 👉 To share my journey and get feedback from this amazing community. 💡 Question for you: If you had 10 years of stock data, would you start with classical ML models or dive straight into deep learning? #MachineLearning #Finance #DataScience #LearningInPublic #BuildInPublic

If you’re someone transitioning into Data Science or trying to understand the math behind Machine Learning and want to see it come alive with clear explanations and practical code, I highly recommend this free, open-source YouTube course by Jon Krohn: 👉 Mathematics for Machine Learning – Jon Krohn https://lnkd.in/gEtqVjhp What makes it stand out: ✅ Easy-to-follow explanations of core mathematical concepts ✅ Jupyter Notebook simulations for each topic ✅ Practical focus I’ve personally gained a much clearer understanding of techniques for model optimization and how math directly powers machine learning. If you’re exploring Data Science or making a career transition, this free and open-source course is a fantastic resource to get started! 🚀 #MachineLearning #DataScience #Mathematics #AI #OpenSource #Learning #JonKrohn

Day 2 of my 100 Days of Machine Learning Yesterday I explained the difference between Classification and Regression. Today I’m diving deeper into Linear Regression, one of the simplest yet most powerful ML algorithms. At its core, Linear Regression fits a line to data points by minimizing errors. (y = mx + c) Ordinary Least Squares (OLS) → the method used to find the best-fit line. Sum of Squared Residuals (SSR) → measures how far predictions are from actual values. The idea is simple: find the line where the SSR is as small as possible. #MachineLearning #LinearRegression #Statistics #DataScience #100DaysOfML