Predict Mobile Prices with Apache Spark Machine Learning

☔ Will it rain tomorrow in Australia? Build a Machine Learning model with Apache Spark to find out! 🌦️ https://lnkd.in/dtDJXqbt #MachineLearning #DataScience #ApacheSpark #BigData #Python #AI #100DaysOfCode

🦒 Classify 7 animal types using Machine Learning with Apache Spark! 🌟 Dive in: https://lnkd.in/dfb5hMvq #MachineLearning #DataScience #ApacheSpark #BigData #Python #AI #100DaysOfCode

Exploring the power of Python in Data Science. Understanding how data can be cleaned, analyzed, and visualized effectively. Working with tools like NumPy, Pandas, and Matplotlib. Focusing on building strong fundamentals step by step. Learning how to turn raw data into meaningful insights. Consistency and practice are driving the progress. Excited for what’s ahead in this journey. #Python #DataScience #DataAnalytics #MachineLearning #LearningJourney #TechSkills #AI

📌 Building Robust Credit Scoring Models with Python 🗂 Category: DATA SCIENCE 🕒 Date: 2026-04-07 | ⏱️ Read time: 24 min read A Practical Guide to Measuring Relationships between Variables for Feature Selection in a Credit Scoring. #DataScience #AI #Python

#Python Python + Pandas → Data Manipulation Python + Scikit-learn → ML Engineering Python + TensorFlow → Deep Learning Python + Matplotlib → Data Visualization Python + Seaborn → Advanced Charts Python + BeautifulSoup → Web Scraping Python + FastAPI → Performance APIs Python + SQLAlchemy → DB Access Python + OpenCV → Computer Vision

Python Basics for Machine Learning I’ve uploaded a video covering the core Python data structures used in machine learning: • Lists • Tuples • Sets • Dictionaries These concepts are essential for handling data and writing efficient ML code. This video is part of my Advanced Machine Learning with LLM series, focused on building strong foundations before moving into complex topics. https://lnkd.in/gSg6rBKM #Python #MachineLearning #DataStructures #LLM #AI #Learning

Want to build your first machine learning model? Start with Scikit-learn. 🤖 Scikit-learn is the most beginner-friendly and widely used machine learning library in Python — and for good reason. Here is what makes it special: 1️⃣ Clean, consistent API that is easy to learn 2️⃣ Covers everything from regression to clustering to classification 3️⃣ Used by data scientists at companies of every size worldwide I am currently working with Scikit-learn as part of my Data Science and analytics studies and it has made machine learning feel genuinely accessible.      #ScikitLearn #MachineLearning #Python #DataScience #AI #Analytics #Tech

Day 28 of My AI & Data Science Journey Today I learned about Strings in Python 🔹 What I explored: ✔ Creating and accessing strings ✔ String slicing ✔ Common string methods Useful Methods: • lower() / upper() • strip() • replace() • split() Strings are very important for data preprocessing and text analysis. Learning step by step and staying consistent #Python #AI #DataScience #CodingJourney

Apache Spark Commands Cheat Sheet | Spark SQL, Core, MLlib & Streaming Download Link: https://lnkd.in/dTXQ_Txi #DataEngineering #BigData #ApacheSpark #DataScience #Python #AI #100DaysOfCode

PyCaret is a low-code Python library that makes machine learning much faster and easier. With just a few lines of code, you can handle preprocessing, compare models, and tune performance in a single workflow. It supports tasks like classification, regression, clustering, and time-series analysis, making it a practical choice for many real-world projects. The book Simplifying Machine Learning with PyCaret by Giannis Tolios is currently available for free: https://lnkd.in/eVFjfGKQ The book guides you step by step through typical PyCaret use cases, from setting up experiments to building, evaluating, and deploying models. It includes practical examples and clear explanations to help you apply PyCaret effectively in real projects. If you want a structured and hands-on introduction to PyCaret, this is a great resource. #machinelearning #python #datascience #ai #pycaret #lowcode #mlworkflow #datatools #analytics #statistics