Understanding Linear Regression with Python from Scratch

Sometimes, the best way to understand how a machine works is by observing it in its simplest form. Last weekend, I spent some time building a tabular Q-Learning simulation from scratch using Python—without any heavy AI libraries—to observe how a digital entity learns to navigate its environment purely through trial, error, and a penalty system. One of the most interesting takeaways from this experiment wasn't the final result, but rather the process of watching the state-value heatmap form in real-time. It mathematically demonstrates that behaviors like risk aversion and route optimization do not need to be explicitly programmed. Instead, they emerge naturally when the machine is allowed to make wrong decisions, hit boundaries, and experience the penalties. I've documented a short observation on the value of letting machines make mistakes in my latest piece. (Link to the full article is in the first comment below 👇) #MachineLearning #ReinforcementLearning #DataScience #Python #DataAnalytics

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗧𝗿𝗶𝗰𝗸 #2 When exploring a dataset, don’t start with modeling. Start by understanding the data shape and missing values. In Pandas, this one line gives a quick overview: df.isna().sum() It helps you instantly see which columns need cleaning before analysis or machine learning. Small steps like this save a lot of time later. #DataScience #MachineLearning #Python #Pandas #LearningInPublic #DataAnalytics

Python is powerful… if you know how to use it. At FastLearner our course covers the basics variables, control flow, functions and shows you how to work with NumPy, Pandas, Matplotlib & Seaborn, all through real-world simulations. Learn by doing. Learn to apply. Learn to succeed. at https://fastlearner.ai/ #PythonForData #ScientificComputing #FastLearner #PracticalLearning #Upskill

𝗧𝗵𝗶𝘀 𝗦𝗶𝗺𝗽𝗹𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗛𝗮𝗯𝗶𝘁 𝗦𝗮𝘃𝗲𝘀 𝗛𝗼𝘂𝗿𝘀 Before writing any model code, print basic stats of your dataset. mean median min / max You’ll catch strange values, scaling issues, and data errors early. Five minutes of sanity checks can save hours of debugging later. #DataScience #MachineLearning #DataAnalytics #Python #AI #LearningInPublic

#Python + #Agents starts in less than a week! Join us in a new series of 6 livestreams where we'll explore the fundamental concepts for creating AI agents in Python using the #MicrosoftAgentFramework. This series is for anyone who wants to understand how agents work, including how they make tool calls, how they use memory and context, and how to build workflows on top of them. Over two weeks, we'll delve into the practical building blocks that define the real-world behavior of an agent. 🔗 Register here: https://msft.it/6049QVIQP See you in class! #MSFTAdvocacy

Excited to announce the start of my machine learning blog! This will explore a range of ideas, from underlying theory to practical applications, highlighting concepts important for a modern machine learning researcher. First post: Building a multiprocessing DataLoader from scratch. I break down PyTorch's DataLoader class by building a simplified version, focusing on how Python's multiprocessing module enables parallel data loading whilst training the model. You'll see how multiprocessing queues coordinate between worker processes and the main training loop—and why this matters for your training pipeline. Using a toy dataset, I compare single-process vs. multiprocess loading, ultimately showing how even a simple implementation can lead to massive improvements in loading time (over 6 times faster!). Link to the blog: [https://lnkd.in/eg6abKWg] #pytorch #machinelearning #ML #deeplearning #python

#Python + #Agents starts in less than a week! Join us in a new series of 6 livestreams where we'll explore the fundamental concepts for creating AI agents in Python using the #MicrosoftAgentFramework. This series is for anyone who wants to understand how agents work, including how they make tool calls, how they use memory and context, and how to build workflows on top of them. Over two weeks, we'll delve into the practical building blocks that define the real-world behavior of an agent. 🔗 Register here: https://msft.it/6048QntCm See you in class! #MSFTAdvocacy

#Python + #Agents starts in less than a week! Join us in a new series of 6 livestreams where we'll explore the fundamental concepts for creating AI agents in Python using the #MicrosoftAgentFramework. This series is for anyone who wants to understand how agents work, including how they make tool calls, how they use memory and context, and how to build workflows on top of them. Over two weeks, we'll delve into the practical building blocks that define the real-world behavior of an agent. 🔗 Register here: https://msft.it/6042QVNjk See you in class! #MSFTAdvocacy

#Python + #Agents starts in less than a week! Join us in a new series of 6 livestreams where we'll explore the fundamental concepts for creating AI agents in Python using the #MicrosoftAgentFramework. This series is for anyone who wants to understand how agents work, including how they make tool calls, how they use memory and context, and how to build workflows on top of them. Over two weeks, we'll delve into the practical building blocks that define the real-world behavior of an agent. 🔗 Register here: https://msft.it/6040Qnevw See you in class! #MSFTAdvocacy

🚀 Campus Placement Prediction System (Machine Learning + GUI) Built an end-to-end ML system to predict student placement probability using Python. 🔹 Applied data preprocessing and categorical encoding 🔹 Implemented Random Forest classifier 🔹 Evaluated using accuracy score & confusion matrix 🔹 Used predict_proba() for confidence estimation 🎥 A short demo video of the working GUI is attached below. 🛠 Tech Stack: Python | Pandas | Scikit-learn | Random Forest | Tkinter 📂 GitHub Repository:  https://lnkd.in/ghg8_wQ9 Open to feedback and suggestions. #MachineLearning #DataScience #Python #RandomForest #StudentProject