Python Uncertainty Handling and Error Management

#Day22 of my Data Science and Machine Learning journey at Skill Shikshya Today I explored tools that are essential for data processing and analysis in Python. What I learned today: ✔️ Regular expressions for pattern matching and text processing ✔️ NumPy arrays, the core of numerical computing in Python ✔️ How arrays differ from lists and why they are faster for large datasets Regular expressions help clean and extract information from text efficiently. NumPy arrays are foundational for almost every data science and machine learning project. Mastering them now will make working with data much smoother. Keeping up the momentum. #100DaysOfLearning #Python #DataScience #MachineLearning #SkillShikshya #LearningJourney

🚀 Just pushed a new machine learning implementation to GitHub! I built **Multiple Linear Regression from scratch** using **vectorized gradient descent in Python/NumPy** to compare performance with traditional loops. Vectorization makes ML code *dramatically faster* by leveraging optimized C/Fortran kernels and SIMD instructions! 🧠💡 :contentReference[oaicite:1]{index=1} 💻 Repository: https://lnkd.in/gppzrgrn 📌 Highlights: ✅ Fully vectorized linear regression training ✅ Gradient descent implemented from first principles ✅ Demonstrated performance improvement over loop‑based code ✅ Clear explanation and concepts inside README If you're learning ML fundamentals or want to see how vectorization boosts efficiency in numerical code, check it out! #MachineLearning #Python #NumPy #GradientDescent #Vectorization #DataScience #MLfromScratch #ANDREWNG

This week, I deepened my understanding of the fundamentals of Data Science by learning Python functions, NumPy, and Pandas. As someone without an IT background, this experience challenged me to think more logically and systematically. I learned how functions in Python help structure programs into reusable components, making code more efficient and easier to maintain. Through NumPy, I gained insight into how numerical data is processed faster using arrays, vectorization, and broadcasting. Meanwhile, Pandas introduced me to working with Series and DataFrames, enabling effective data exploration and analysis using functions such as head(), info(), and describe(). Although the process was challenging, it has motivated me to continue improving my skills step by step. Our learning progress has been collectively summarized in the slides attached below. These slides provide an overview of our shared learning journey. Digital Skola #DigitalSkola #LearningProgressReview #DataScience #Python #NumPy #Pandas #LearningJourney

I've added brief overviews of Python methods to my new textbook chapters on Bayesian modeling and causal inference. This wraps up my initial drafts of the new chapters for the second edition, and ensures that every methodology outlined in R also has information on Python alternatives. It's been a productive 'between jobs' period for me over the past few weeks. I'll now move to handling feedback and tweaking and refining content over the next few months before submitting the print version. Please submit any feedback via the github repo. https://lnkd.in/epcP5CpN #analytics #statistics #datascience #rstats #python #peopleanalytics #ai #technology

#Day19 of my Data Science and Machine Learning journey at Skill Shikshya Today I went deeper into exception handling in Python. This is one of those topics people ignore until their code breaks in real projects. What I learned today: ✔️ Try and except blocks to handle runtime errors safely ✔️ Raise to create custom exceptions when something goes wrong ✔️ Why proper error handling makes programs more stable and easier to debug If you do not handle errors properly, your program will crash at the worst possible time. Learning this now is necessary, not optional. Consistency over speed. #100DaysOfLearning #Python #DataScience #MachineLearning #SkillShikshya #LearningJourney

🚀 First Steps into Machine Learning Today, I built my first Linear Regression model from scratch using Python. Instead of relying on libraries, I implemented the least squares method to calculate the slope and intercept, generated predicted values, and visualized the fitted regression line against real data using scatter plots. I also analyzed prediction errors (residuals) by plotting error distributions and evaluating the model using Residual Sum of Squares (RSS) to better understand how well the model fits the data. This hands-on approach helped me understand how regression models work under the hood, not just how to call a function. Excited to keep learning and building 🚀 #MachineLearning #DataScience #Python #LinearRegression #LearningByDoing #ALXAfrica

Today’s ML Learning: 1.Explored cross-validation to evaluate models more reliably and avoid overfitting. 2.Practiced hyperparameter tuning using GridsearchCV to find optimal model settings. 3.also Implemented random Forest and compared performance using cross-validated results. also Hands-on practice helped me understand how model selection and tuning improve real-world performance. here is the link to my Github and my code in jupyternotebook: https://lnkd.in/eDGUpgMc #MachineLearning #DataScience #RandomForest #GridSearchCV #CrossValidation #Python #LearningByDoing

Hello guys! Excited to kick off my new ML course: ML In Action I just published Day 1: Descriptive Statistics — a hands-on, code-first lesson where you can see exactly how to analyze a dataset using Python: Calculate mean, median, and mode Check variance and standard deviation Explore range, percentiles, quartiles, and interquartile range (IQR) This is the first lesson of a daily ML coding series, aimed at helping learners build practical skills by running real code themselves. 📌 Check it out here: ML In Action Course #MachineLearning #Python #DataScience #MLProjects #LearningByDoing #Coding

📊 ANOVA Analysis in Python Performed ANOVA on J.P. Morgan historical stock data to study whether Close prices vary significantly across years using statsmodels. Guided by Prof. Dr James Daniel Paul P #Python #ANOVA #StatsModels #JPMorgan #BusinessAnalytics #FinanceAnalytics #DataScience #MBA

🧱 Building the foundation, one line of code at a time. Dive into the basics of Machine Learning with Simple Linear Regression! 📉 In this mini-project, I used Python to predict housing prices based on square footage. It’s one thing to call a function, but it’s another to verify the math ($y = mx + c$) behind the library. Seeing the Best Fit Line plotting perfectly through the data points is always satisfying. Tech Stack: Python, Scikit-Learn, Pandas, Matplotlib. kaggle notebook:https://lnkd.in/gaZb8yZK #MachineLearning #DataScience #Python #LinearRegression #CodingJourney #AI