The best way to learn ML? Stop using libraries. I challenged myself to build linear regression using only NumPy and pandas. No sklearn. No model.fit(). No shortcuts. The result: 3 days of debugging, 4 major bugs, and one working model. I documented everything in a new Medium article: The math behind gradient descent (explained simply) Why feature scaling saved my model from exploding The dummy variable trap I almost fell into How I fixed R² = -6660 (yes, negative six thousand) If you're learning data science, this will save you hours of frustration. Read the full story: [https://lnkd.in/gvEu6-fM] Code on GitHub: [https://lnkd.in/gQUsAfzD] #DataScience #MachineLearning #Python #100DaysOfCode

𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐏𝐲𝐭𝐡𝐨𝐧 𝐥𝐨𝐨𝐤𝐬 𝐞𝐚𝐬𝐲—𝐮𝐧𝐭𝐢𝐥 𝐲𝐨𝐮 𝐬𝐭𝐚𝐫𝐭. At the beginning, it’s mostly: --> syntax errors --> indentation issues --> code that doesn’t run Nothing works the way you expect. But with consistency, things start to click. 𝐘𝐨𝐮 𝐛𝐞𝐠𝐢𝐧 𝐭𝐨 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝: --> data structures (lists, dictionaries) --> loops and conditions ,functions --> working with libraries like pandas and numpy And slowly… 𝐘𝐨𝐮 𝐦𝐨𝐯𝐞 𝐟𝐫𝐨𝐦 𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐜𝐨𝐝𝐞 𝐭𝐨 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐬𝐨𝐥𝐯𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬. Most people quit in the confusing phase.The ones who don’t are the ones who improve. 𝐈𝐟 𝐲𝐨𝐮'𝐫𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐏𝐲𝐭𝐡𝐨𝐧 𝐫𝐢𝐠𝐡𝐭 𝐧𝐨𝐰, 𝐟𝐨𝐜𝐮𝐬 𝐨𝐧: Basics → Logic → Practice → Libraries That’s the real path. Save this if you're on your Python journey. Navya sri Kurapati🧑💻 #Python #LearnPython #DataAnalytics #DataScience #AI

Data Cleaning is only half the battle. Are you Engineering your features? In Step 2 of the Machine Learning pipeline, many beginners stop at data cleaning. While removing NaNs and dropping irrelevant rows is essential, the real magic happens during Feature Engineering. While working on my recent Price Prediction project, I realized that the raw data rarely tells the full story. To build a high-performing model, you have to create features that capture the "why" behind the numbers. I focused on three key areas for this preprocessing script: 📈 Moving Averages: Capturing trends over time. 📉 Volatility: Accounting for market fluctuations and risk. 🕒 Lag Features: Giving the model a "memory" of previous price points. Clean data gets you a working model. Engineered features get you a winning model. Check out the snippet of my preprocessing logic below! 👇 #MachineLearning #DataScience #Python #FeatureEngineering #PredictiveAnalytics

Stop using Pandas for your production pipelines! Most of the data teams switched to Polars for core processing, especially for large datasets and pipelines. You should start using it too! Here is why you should use Polars 🐻❄️: 🔺2-10x faster than Pandas 🔺2-5x less RAM usage 🔺Lazy API (allows the query optimizer to reorder operations for maximum efficiency) When should you stay on Pandas 🐼? ▪️Standard tools compatibility: Many libs (like scikit-learn, PyTorch, ...) are still integrated with Pandas; if you use Polars, you will have to convert the dataframe to Pandas in the lib usage step ▪️Small datasets (less than ~100MB): Using Polars with those small datasets can be slower (overhead by Polars' multi-threading ⚡Quick Summary:  For production, large datasets, or high performance is required ➡️ Use Polars For research, educational work, or quick exploration ➡️ Use Pandas #DataEngineering #Python #Polars #ETL

🚀 Machine Learning With Python From Scratch — Part 2! This time we level up from single to Multiple Variable Linear Regression — and we also cover something that most beginners skip but is super important in real life: saving your model with Pickle. Multiple Variable Linear Regression is the same idea as single variable, but instead of using one input to predict an output, you use several. In this example I predicted an employee's salary based on: --Years of experience --Test score --Interview score But before even touching the model, the data had to be cleaned: Experience was stored as words ("five", "seven"), had to convert them to numbers Some values were missing, handled with median filling That's the part nobody talks about. Real data is messy. Cleaning it is half the job. And once the model is trained, what do you do with it? You save it using Pickle, so you never have to retrain it again. 🔗 Full notebook + dataset + detailed explanation on GitHub: 👉 https://lnkd.in/dC5Pzygv If you're just getting into ML, follow along, I'm building this series from the ground up, one concept at a time. #MachineLearning #Python #DataScience #LinearRegression #Pickle #DataCleaning #GitHub #BeginnerML #100DaysOfCode

Headline: Stop wasting time cleaning data manually. Body: Spent hours cleaning a dataset of user feedback today. It was messy—typos, missing values, different formats. I realized I was approaching it like a textbook exercise, not a engineer. Thinking: If I do this again, I’m wasting time. Solution: I created a Python pipeline that automatically handles missing data, maps common typos, and standardizes formats using Pandas. Real Result: Cut data cleaning time from hours to few minutes. #Programming #Productivity #MLOps #qurateHq #thriveabia

🚀 Day 11 - Pandas Series Mastered (90 Days GenAI Engineer Revision) Today’s focus was on Pandas Series - the foundation of data analysis in Python. 📊 What I learned: Creating Series (from lists, dictionaries, with custom index & name) Key attributes: size, dtype, name, index, values Useful methods: head(), tail(), sample(), value_counts() Statistical analysis: mean, median, mode, std, describe() Indexing techniques: slicing, label-based, fancy indexing Real-world operations: boolean filtering, arithmetic operations, apply() 🏏 Real Example: Analyzed a cricket player’s score data using Series: Calculated average score Identified highest performance Filtered out ducks (0 runs) using boolean indexing Used value_counts() to check consistency 💡 Key Insight: "You truly understand Pandas when you work with real data, not just theory." 📂 GitHub:https://lnkd.in/gDJHGieS Uploaded a complete, well-structured file → day11_pandas_series/series_complete.py On to the next concept tomorrow 🚀 #Day11 #Python #Pandas #DataAnalysis #GenAIEngineer #90DaysChallenge #LearningByDoing

Day 2/15 — Creating Your First NumPy Arrays Yesterday you saw why NumPy is faster than Python lists. Today you actually start using it. NumPy arrays are the core structure used for numerical computation, data science, and machine learning. Unlike Python lists, NumPy arrays are designed to handle large amounts of data efficiently. Today you learned: • How to create arrays using np.array() • Converting Python lists into NumPy arrays • Checking array type using type() • Understanding dimensions using .ndim • Creating arrays from basic user input These fundamentals are important because every dataset you work with in machine learning will eventually be converted into NumPy arrays. Once your data is in array form, you can perform fast mathematical operations on entire datasets at once. Mini Challenge: Create a NumPy array from this list and print its dimension: [10, 20, 30, 40] Then print: type(array) array.ndim Share your output in the comments. I’m sharing 15 days of NumPy fundamentals — building the core math foundation for Data Science and Machine Learning. Next up: Specialized array initializers like zeros, ones, arange, and linspace. Working with arrays and inspecting values becomes easier in PyCharm by JetBrains, especially with variable explorers and debugging tools. Follow for the full NumPy learning series. Like • Save • Share with someone learning Data Science. #NumPy #Python #DataScience #MachineLearning #LearnPython #Coding #Programming #Developers #JetBrains #PyCharm

Advanced pandas tricks that make you 10x faster at data wrangling. Most people learn pandas basics and stop. This free notebook covers what comes after. → MultiIndex: hierarchical indexing for complex datasets → .pipe() — chain custom functions into your workflow → Method chaining: write entire analyses in one readable block → Memory optimization: reduce DataFrame memory by 70%+ → Vectorized operations: why your for loop is 100x slower → Performance patterns the documentation buries If your pandas code has more than 2 for loops, this notebook will change how you write it. Every trick has before/after benchmarks. See the speed difference yourself. Free: https://lnkd.in/g7HsJfGy Day 3/7. #Python #Pandas #DataAnalyst #DataScience #DataWrangling #Performance #FreeResources #DataAnalytics

Built a Rainfall Prediction model and deployed it live. Here is what actually happened behind the scenes Decision Tree gave me 100% training accuracy. I got excited. Then I checked the test score and realised the model had just memorised the data. It learned nothing real. Naive Bayes gave me 73.9% on both train and test. Consistent That is the one I deployed. 3 models trained. 1 deployed. 1 lesson — a consistent score beats a perfect score every time live app here: https://lnkd.in/d-xaufug Full project and code: https://lnkd.in/d_d2Tx7R Akarsh Vyas Tanishq Vyas #DataScience #MachineLearning #Python #Streamlit #BuildInPublic #MLProject