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
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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 - Introduction an...
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 ======
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