Just don’t make it angry
07 November 2022
I made a robot that can see using sound. #shorts
03 November 2022
Best night of his life
12 October 2022
What happens when robots die?
11 October 2022
Pomodoro robot! This is a work in progress but too cute not to share
30 September 2022
Build your own web server using a Raspberry Pi Pico W using Phew.
28 August 2022
Yukon & Omnibot 3000
Pico W Toothbrush
Whats new in Python 3.13a
Maker Faire Rome 2023
Data Manipulation with Pandas and Numpy
Computer Vision on Raspberry Pi with CVZone
Learn how to program SMARS with Arduino
Build a SMARS Robot in Fusion 360
Python for beginners
Create Databases with Python and SQLite3
KevsRobots Learning Platform
60% Percent Complete
By Kevin McAleer, 3 Minutes
Machine Learning is a method of data analysis that automates analytical model building. Python, with its strong set of libraries like Scikit-learn, makes it a great language for Machine Learning. This lesson will introduce the basics of machine learning and how to use the Scikit-learn library for predictive modeling.
Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
There are three major types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
Supervised Learning: The algorithm learns from labeled data. Given a set of training examples, the algorithm tries to find the best model to relate the inputs to the output.
Unsupervised Learning: The algorithm learns from unlabeled data and finds hidden patterns or intrinsic structures in the input data.
Reinforcement Learning: The algorithm learns to perform an action from experience. It is trained to make specific decisions by rewarding and punishing behaviors.
Scikit-learn is one of the most popular open-source machine learning libraries for Python. It provides a range of supervised and unsupervised learning algorithms.
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import svm
# Load dataset
iris = datasets.load_iris()
# Split dataset into training set and test set
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.4, random_state=0)
# Create a model
clf = svm.SVC(kernel='linear', C=1)
# Train the model using the training sets
# Predict the response for test dataset
y_pred = clf.predict(X_test)
# Print the predictions
In this lesson, you’ve learned about the basics of machine learning, including an understanding of different types of machine learning. We also introduced the Scikit-learn library and demonstrated how it can be used for predictive modeling. Machine learning is a powerful tool for data analysis and predictive modeling.