In this post, we will embark on a journey to understand the fundamentals of Machine Learning using Python. This is designed for beginners who wish to delve into the fascinating world of AI and data science.
Machine learning is a subset of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide.
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.
Unsupervised learning is a type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision.
Data preprocessing is a data mining technique that involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors. Data preprocessing is a proven method of resolving such issues.
Model evaluation aims to estimate the generalization accuracy of a model on future (unseen/out-of-sample) data. Methods for this are divided into 2 groups: in-sample and out-of-sample. In-sample methods estimate the error rate after training the model. Out-of-sample methods, split the data into a training and test set, the model is trained on the training set and evaluated on the test set. Afterwards the error rate on the test set is interpreted as the generalization error.
Python, along with libraries like Scikit-learn and Pandas, provides a robust and versatile platform for the implementation of machine learning algorithms. Here's an example of how to implement the K-Nearest Neighbors algorithm, a simple yet powerful algorithm used for both classification and regression.
# Import libraries
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
# 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=0.3)
#Create KNN Classifier
knn = KNeighborsClassifier(n_neighbors=5)
#Train the model using the training sets
knn.fit(X_train, y_train)
#Predict the response for test dataset
y_pred = knn.predict(X_test)
# Model Accuracy
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
This code creates a KNN classifier that is trained on a set of labeled iris data. The model is then used to predict the class of iris flowers in a test set, and the accuracy of these predictions is output.
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