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AI Basics Week 2: Mastering Machine Learning | Fundamentals, Supervised Learning 🤖📊

Join us in Week 2 of our #ailearningjourney as we explore the fundamentals of #machinelearning, dive deep into #supervisedlearning, and experiment with basic algorithms using #python. Don't miss out on these essential concepts and practical examples! 🤖📊 #ai #ml #aiforbeginners #mlalgorithms #learnunlearnrelearn #lrnunlrnrlrn Chapters: 00:00 Learn the fundamentals of machine learning 00:15 Dive deeper into supervised learning 00:37 Classification, Regression 00:50 Experiment with basic machine learning algorithm using Python and scikit-learn libraries - K-NN algorithm 01:41 Step 1: Install required Python libraries 01:45 Step 2: Load the dataset 02:30 Step 3: Split the dataset 03:16 Step 4: Train a k-NN model 03:37 Step 5: Evaluate the model 04:41 Next week: Deep Learning 04:47 References for further understanding 🌐 Join the discussion in the comments and let us know what you're most excited to learn about AI! 👇👇👇 🤩🤩🤩 SUBSCRIBE to our YouTube channel for more learning videos 👉 https://tinyurl.com/4wvtc2cu References for Further Understanding: Coursera's Machine Learning Course by Andrew Ng: A comprehensive course covering various machine learning algorithms, including k-NN, with practical coding exercises. https://www.coursera.org/learn/machin... DataCamp's k-NN Classification Tutorial: A detailed guide on implementing k-NN for classification with practical examples. https://www.datacamp.com/community/tu... scikit-learn Documentation on k-NN: Detailed information and examples on implementing k-NN from the official scikit-learn documentation. https://scikit-learn.org/stable/modul... A Beginner’s Guide to the Top 10 Machine Learning Algorithms https://www.kdnuggets.com/a-beginner-... These resources will help you gain a deeper understanding of the k-NN algorithm and how to apply it in practical scenarios using Python and scikit-learn. Here are some additional references I used to prepare this video: Python download url: https://www.python.org/downloads/wind... Jupiter notebook(I installed Anaconda): https://docs.jupyter.org/en/latest/in... Scikit-Learn Tutorial 01 - Introduction and Jupyter Notebooks    • Scikit-Learn Tutorial 01 - Introducti...   Python example: ======Begin code ====== Install required Python libraries pip install numpy pandas scikit-learn from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score Load the iris dataset iris = datasets.load_iris() X = iris.data y = iris.target Split the dataset into training and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) Initialize the k-NN classifier knn = KNeighborsClassifier(n_neighbors=3) Train the classifier knn.fit(X_train, y_train) Predict on the test data y_pred = knn.predict(X_test) Calculate the accuracy of the classifier accuracy = accuracy_score(y_test, y_pred) print(f"Accuracy: {accuracy}") ======End of code ====== ✔️ Follow us on Instagram 👉   / lrnunlrnrlrn   ✔️ Find us on Facebook 👉   / 61559724324967   ✔️ Join us on YouTube 👉   / @lrnunlrnrlrn   Videos:    / @lrnunlrnrlrn   About:    / @lrnunlrnrlrn