Tech With Tim: Python Skills You NEED Before Machine Learning TL;DR Get your Python game on point before tackling ML! This video breaks down everything from core Python and data handling to essential software-engineering tools, optional math refreshers, and then ramps up into machine-learning foundations, deep learning, real-world projects—and even a bonus LLM section. By the end you’ll know exactly what to practice and how to showcase it. Along the way you’ll find two beginner-friendly DataCamp tracks (with an exclusive 25% off link) and an invite to DevLaunch’s hands-on mentorship, where you build real projects and actually land that dream data job. 🚀 #Python #MachineLearning #SoftwareEngineer Watch on YouTube https://lnkd.in/gFAgY78v

⭐ Excited to share my Random Forest practical 🧠, I implemented this powerful ensemble algorithm using Python 🐍 (Scikit-learn). It was amazing to see how multiple Decision Trees work together through majority voting to improve accuracy, reduce overfitting, and balance bias-variance 🌿. Hands-on experiments like this make learning truly insightful, showing how ensemble methods turn raw data into reliable predictions 💡. Guided by Ashish Sawant Sir. 🔗 GitHub: https://lnkd.in/ez_NstrZ 📁 Google Drive: https://lnkd.in/ezXFx_py #RandomForest #MachineLearning #DataScience #AI #Python #EnsembleLearning #DataDriven #MLPracticals #LearningByDoing

👉 🚀 Hands-on Machine Learning Project: Linear Regression 🧠 Excited to share my latest project — Linear Regression Model built in Python (Jupyter Notebook)! 🎯 In this project, I explored how to predict house prices based on house size using one of the most fundamental algorithms in Machine Learning — Linear Regression. This project helped me understand: ✅ How the model finds the best-fit line ✅ The relationship between features and target variables ✅ How to visualize and interpret predictions 🔗 Check out my full project on GitHub: 👉 https://lnkd.in/dM6f7ik8 #MachineLearning #DataScience #Python #LinearRegression #GitHub #DataAnalytics #AI #LearningByDoing #WomenInTech #CareerGrowth

🚀 Today, I explored some more about NumPy! NumPy is the backbone of numerical computing in Python, and it’s incredible how much we can achieve with just a few lines of code. 💻✨ Efficient array and matrix manipulations Powerful mathematical and statistical functions Essential for data science, ML, and AI projects Some more about what I tried: Calculated matrix determinants and inverses Practiced matrix multiplication and element-wise operations Explored reshaping and stacking arrays for better data handling Excited to keep building my Python and data skills with practical hands-on examples! #Python #NumPy #DataScience #MachineLearning #LearningJourney

⚔️ Python vs AI: Who’s Winning the Future? 🤖🐍 AI is stealing the spotlight everywhere — writing code, analyzing data, even building apps. So… is AI replacing Python? Not so fast. Python is still the language behind most AI systems. From TensorFlow to PyTorch, AI literally runs on Python’s back. It’s simple, readable, and has the largest developer army out there. AI may be smart — but Python taught it how to think. 🧠 Who wins the battle? Maybe AI creates the ideas… But Python still writes the rules. 💻🔥 #Python #AI #MachineLearning #DeepLearning #Programming #TechTrends

Just finished building my first real-world Machine Learning model using a Kaggle dataset on student performance 🎓 Explored how factors like parental education, test prep, and lunch type influence math scores, and trained a linear regression model with ~87% accuracy! Every line of code taught me something new about turning data into insight 📊 #MachineLearning #DataScience #Kaggle #Python #LearningJourney 🗣️What is Linear Regression? Linear Regression is one of the simplest yet most powerful algorithms in Machine Learning. It’s used to predict a continuous value (like a score, price, or temperature) by finding a linear relationship between the input features and the target output.

🎥 Built Linear Regression from Scratch — No Libraries, Just Logic! Instead of going blindly with built-in functions, I wanted to really understand what happens behind the scenes. So I implemented Linear Regression with Gradient Descent using pure math and Python — writing my own cost function, gradients, and optimizer. No shortcuts, no scikit-learn… just math turning into motion. Watching the loss curve flatten and the line fit perfectly was pure satisfaction 🤓 Here’s a quick video of the model learning step-by-step! #MachineLearning #DataScience #Python #GradientDescent #LinearRegression #MLFromScratch #AI #LearningByDoing #MathematicsForML

Day 10 – PYTHON VARIABLES 🧠🐍 (MY TechRise cohort 2.0 journal). Today in my TechRise Cohort 2 journey, I learned about Python Variables — the building blocks of every program! Variables are like containers that hold data, and I explored different data types such as integers, floats, strings, booleans, and even complex numbers. I also practiced data type conversion in Python using simple code examples. Here’s a quick snippet from my learning: a = 10 k = float(a) p = complex(a) print(k) print(p) Every new lesson makes Python more exciting and practical for real-world AI and Machine Learning applications. 🚀 #TechRiseCohort2 #Python #AI #MachineLearning #CodingJourney #DigitalSkills

A mini project about Supervised Learning, applied it by predicting house prices using the California Housing Dataset from Kaggle. Tools: Python, Pandas, Scikit-learn, Matplotlib Steps: Cleaned and visualized the dataset Trained a Linear Regression model Evaluated using mean squared error and r2 score Achieved an RMSE of 69,297.72 and visualized predictions vs actual prices. GitHub: https://lnkd.in/d8CkpV_b #MachineLearning #DataScience #Python #LearningJourney #AI

Transitioning from full-stack development to AI wasn’t as straightforward as I imagined — until I found the right tool to begin with. In my latest Medium article, I share how Jupyter Notebook became the perfect starting point for exploring Python, AI, and data science, even without prior AI experience. If you’re a developer looking to take your first step into AI, this might help you find your way too. 👉 Check it out: https://lnkd.in/d7sVhsaq #ArtificialIntelligence #Python #JupyterNotebook #DataScience #FullStackDeveloper #TheGeekPlanets