Andrew Ng ML Course: Titanic Survival Prediction Model

✅ Numpy arrays.... Today in our Python class at FIT – Future Innovative Technology, we explored NumPy arrays and learned some really interesting concepts. We covered: • Arrays in NumPy • 2D Arrays • Array Dimensions • Array Shapes It was exciting to understand how NumPy helps in handling data efficiently and how multidimensional arrays work. Learning these concepts is making programming feel more practical and powerful, especially for data science and AI. Every day I’m discovering something new, and this journey of learning Python and AI is becoming more interesting and enjoyable. #Python #NumPy #AI #MachineLearning #LearningJourney #FutureInnovativeTechnology

🚀 Day 43/100 – Python, Data Analytics & Machine Learning Journey 🤖 Started Module 3: Machine Learning 📚 Today I learned: 7. Train Test Split 8. Correlation 9. Feature Selection Machine Learning is the core of AI systems, and I’m excited to explore algorithms, models, and real-world applications in the coming days. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

🚀 Built my first end-to-end Machine Learning pipeline! Using the Titanic dataset, I implemented data preprocessing, feature engineering, Logistic Regression, and a Scikit-learn pipeline. The project is structured like a real ML workflow and available on GitHub. Excited to keep building! Github link : [https://lnkd.in/d9AE3vVS] #MachineLearning #Python #ScikitLearn #DataScience #MLProjects #100DaysOfML

Python becomes much easier when you focus on the right areas—building GUI applications with Tkinter, exploring data science using NumPy, Pandas, Matplotlib, Seaborn, SciPy, Plotly, Bokeh, and Dask, and stepping into artificial intelligence with OpenCV, OpenAI, and Scikit-learn. Start simple, stay consistent, and you’ll gradually turn concepts into real skills. #python #coding #datascience #ai #learnpython #programming #pherochainai

Leveling up my ML workflow with this quick Scikit-Learn Cheat Sheet 📊🤖 A handy guide for training, evaluating, and tuning machine-learning models efficiently! #MachineLearning #ScikitLearn #Python #DataScience #AI #MLAlgorithms #LearningEveryday

🚀 Day 1: NumPy? Today I started learning NumPy, one of the most important libraries in Python for numerical computing. NumPy allows us to work with large datasets using arrays instead of traditional lists. It is faster, more efficient, and widely used in data science, machine learning, and AI. 💡 Key takeaway: NumPy improves performance and makes complex calculations simple. #Python #NumPy #DataScience #LearningJourney

Machine Learning Project: Book Recommender System I built a Book Recommendation System using Collaborative Filtering. The system suggests similar books based on user ratings. 🔹Built using: Python Pandas Scikit-learn Streamlit 🔹 Features: • User-Book Rating Matrix • Cosine Similarity • KNN Model • Interactive Streamlit UI 🌐 Live Demo: https://lnkd.in/ghuZ7PMH 💻 GitHub Repository: https://lnkd.in/g-Y_stfp #MachineLearning #DataScience #Python #Streamlit #AIProjects

🚀 Day 2 of my AI & Data Science Journey Today I learned some important basics of Python 🐍 • What are Data Types (int, float, string, boolean) • How to use Variables to store values • Different types of Operators • Type Casting (converting one data type into another) Slowly understanding how coding actually works 💻 Small steps, but moving forward every day 📈 #Day2 #Python #LearningJourney #DataScience #Beginner #Consistency #AI

Temporal.io is emerging as a top choice for building reliable agentic workflows in production. I've been working through their AI cookbook examples and putting together a reference repo as I go: https://lnkd.in/gnwP-a6k #Temporal #TemporalIO #AgenticAI #AIAgents #Python #LLM #WorkflowAutomation

Recently started exploring Python in the AI ecosystem. One thing I really like about Python is how quickly you can move from idea to implementation. Example: A simple model predicting output from input data. from sklearn.linear_model import LinearRegression X = [[1], [2], [3]] y = [2, 4, 6] model = LinearRegression() model.fit(X, y) print(model.predict([[4]])) Just a small experiment, but it shows how machines can learn relationships from data. Excited to keep learning and building more with Python and AI. #Python #AI #MachineLearning #DeveloperLife