Taro (YC S22)’s Post

Completed the course: Supervised Learning with scikit-learn from DataCamp

🚀 Excited to share a new milestone! 🚀 I’ve just completed the Introduction to Machine Learning course, where I learned the fundamentals of data manipulation, visualization, and basic machine learning algorithms using some key libraries: 📊 Pandas for data analysis and manipulation 🔢 NumPy for numerical computing 📉 Matplotlib for data visualization 🤖 Scikit-learn for building and evaluating machine learning models It’s been a rewarding journey, and I’ve documented everything in a GitHub repository where you can check out the Jupyter Notebooks and see how much I’ve learned. 🔗 Check it out here: https://lnkd.in/d-Pg5Zc4 Looking forward to diving deeper into the world of data science and machine learning! 💻 #MachineLearning #DataScience #Python #Pandas #NumPy #Matplotlib #ScikitLearn #JupyterNotebooks #LearningJourney Thanks to Zero To Mastery Academy.

I've just finished Supervised Learning with scikit-learn in DataCamp!!

I just completed the "Supervised Learning with scikit-learn" course on DataCamp!

🐼 Struggling with messy Pandas code? You're not alone! Check out the article titled "Pandas: From Messy To Beautiful" by Anna Zawadzka(https://lnkd.in/dvB_zFv2) on Towards Data Science. She shares tips and pitfalls to help you streamline your DataFrame scripting for cleaner, more reliable code. 🚫 Don'ts: Learn about common pitfalls like mutability and output arguments that can lead to confusion and errors in your code. ✅ Do's: Discover strategies to reduce modifications, group similar operations, and enhance reusability, all while ensuring your code remains clean and infallible. 🔍 Testability: Dive into the importance of testing your code and see practical examples of unit tests that ensure your functions perform as expected. 👩💻 These insights may help you structure your scripts with confidence and clarity. Read the full article for a deep dive into these coding strategies and take your Pandas skills to the next level! 📊💡 https://lnkd.in/d_jJmSFY #Pandas #DataScience #CodingTips #CleanCode Image credits: generated with Tengrai Artificial Intelligence

Recap and broaden knowledge in scikit-learn 🎉

I’ve completed the Supervised Learning with scikit-learn Course on DataCamp!

Excited to share that I've just completed a course on Data Structures and Algorithms in C for Beginners! Looking forward to applying these foundational skills to tackle complex problems and optimize solutions. #LearningJourney #DataStructures #Algorithms

I’ve just completed the Supervised Learning with scikit-learn Course on DataCamp! 🚀 Throughout this course, I gained hands-on experience with both classification and regression models, applying them to real-world datasets such as telecom customer churn and advertising expenditure. Here are some key takeaways: 📊 Classification Models: I learned how to build models to classify data, from splitting datasets into training and test sets, to evaluating their accuracy. I also explored how model complexity impacts performance. 📈 Regression Models: I developed regression models to predict sales values and dug deep into metrics like R-squared and root mean squared error. Plus, I used techniques like k-fold cross-validation and regularization to prevent overfitting. 🔧 Model Optimization: Hyperparameter tuning helped me fine-tune both classification and regression models to maximize their performance. Visualizing model performance was a key part of the learning process. ⚙️ Data Preprocessing and Pipelines: Finally, I tackled challenges like missing values, categorical data conversion, and scaling. I also built efficient pipelines to streamline model training and evaluation. I'm excited to apply these new skills in my future data projects!

Obtained New Certification in Data Science From Coding Ninja #codingninjas #datascience Key Takeaways: Intro to data science, programming fundamental, IDEs and spreadsheet, Web Scraping, Extracting data from APIs etc..!