Understanding PCA for Dimensionality Reduction

Learn the basics of Principal Component Analysis (PCA).

  1. Standardize data:
def standardize(data):
    mean = sum(data) / len(data)
    std = (sum((x-mean)**2 for x in data) / len(data)) ** 0.5
    return [(x-mean)/std for x in data]
  1. Calculate covariance matrix:
def covariance_matrix(data):
    n = len(data[0])
    return [[sum(data[i][k]*data[j][k] for k in range(n)) for j in range(n)] for i in range(n)]

Read more: PCA Algorithm

Setting Up Log Rotation

Learn how to set up log rotation on Linux.

  1. Create logrotate config:
sudo nano /etc/logrotate.d/myapp
  1. Sample configuration:
/var/log/myapp/*.log {
    daily
    rotate 7
    compress
    missingok
    notifempty
}
  1. Test configuration:
sudo logrotate -d /etc/logrotate.d/myapp

Read more: Logrotate Documentation

Implementing K-Nearest Neighbors

Learn how to implement the K-Nearest Neighbors algorithm.

  1. Calculate Euclidean distance:
import math

def euclidean_distance(p1, p2):
    return math.sqrt(sum((x-y)**2 for x,y in zip(p1,p2)))
  1. Find nearest neighbors:
def get_neighbors(train, test, k):
    distances = [(euclidean_distance(test, t), t) for t in train]
    distances.sort(key=lambda x: x[0])
    return [d[1] for d in distances[:k]]

Read more: KNN Algorithm

Basic IPTables Configuration

Learn basic IPTables configuration for Linux firewalls.

  1. List current rules:
sudo iptables -L -v -n
  1. Allow SSH:
sudo iptables -A INPUT -p tcp --dport 22 -j ACCEPT
  1. Block IP:
sudo iptables -A INPUT -s 192.168.1.100 -j DROP
  1. Save rules:
sudo iptables-save > /etc/iptables/rules.v4

Read more: IPTables Documentation

Understanding Decision Trees

Learn the basics of decision trees in machine learning.

  1. Calculate entropy:
import math

def entropy(p):
    return -p * math.log2(p) - (1-p) * math.log2(1-p)
  1. Calculate information gain:
def information_gain(parent, children):
    total = sum(len(c) for c in children)
    return entropy(parent) - sum((len(c)/total)*entropy(c) for c in children)

Read more: Decision Trees

Setting Up Nginx Web Server

Learn how to set up Nginx web server on Linux.

  1. Install Nginx:
sudo apt install nginx
  1. Start Nginx:
sudo systemctl start nginx
  1. Check status:
sudo systemctl status nginx
  1. Test configuration:
sudo nginx -t

Read more: Nginx Documentation

Basic Sentiment Analysis

Learn how to perform basic sentiment analysis using Python.

  1. Create sentiment dictionary:
sentiment = {
    'good': 1,
    'bad': -1,
    'great': 2,
    'terrible': -2
}
  1. Analyze text:
def analyze(text):
    words = text.lower().split()
    return sum(sentiment.get(word, 0) for word in words)
  1. Test analysis:
print(analyze("This is a great day!"))

Read more: Sentiment Analysis

Getting Started with Docker

Learn how to set up and use Docker on Linux.

  1. Install Docker:
sudo apt install docker.io
  1. Start Docker service:
sudo systemctl start docker
  1. Run test container:
sudo docker run hello-world
  1. List containers:
sudo docker ps -a

Read more: Docker Documentation

Implementing Linear Regression

Learn how to implement linear regression using Python’s math library.

  1. Calculate mean:
def mean(values):
    return sum(values) / float(len(values))
  1. Calculate variance:
def variance(values, mean):
    return sum((x-mean)**2 for x in values)
  1. Calculate covariance:
def covariance(x, mean_x, y, mean_y):
    return sum((x[i]-mean_x)*(y[i]-mean_y) for i in range(len(x)))

Read more: Linear Regression

Automating Tasks with Cron Jobs

Learn how to automate tasks using cron jobs on Linux.

  1. Edit crontab:
crontab -e
  1. Add a daily backup job:
0 2 * * * /path/to/backup.sh
  1. List current jobs:
crontab -l
  1. Remove all jobs:
crontab -r

Read more: Cron Documentation