**Machine learning (ML)** is a subset of <u>artificial intelligence</u> (AI) that involves the development of algorithms and models which enable computers to learn from data and improve their performance on a task over time. Instead of being explicitly programmed to perform a specific task, a machine learning system uses data to identify patterns, make predictions, or make decisions.

## Key Elements of ML

Machine learning is a fairly comprehensive field of computer science with many branches. Becoming a specialist in this field is worth having a strong mathematical base and abstract thinking.

That is why, to start studying Machine Learning, we strongly recommend you to get familiar with <a 
href="https://codefinity.com/courses/tracks/a20274fa-bf44-456d-ab04-b53755ae6a08"
target="_blank"
style="color: #ff8a00; font-weight: 600; text-decoration: none;"
onmouseover="this.style.color='#ff0000'"
onmouseout="this.style.color='#d27204'">Foundations of Machine Learning Track</a>

<h2 style="font-weight: 800; font-size: 26px; margin-top: 20px; margin-bottom: 5px;">Requirements</h2>
<a 
href="https://codefinity.com/courses/tracks/cec141ba-8616-4736-9d4e-666151aea4a2"
target="_blank"
style="color: #ff8a00; font-weight: 600; text-decoration: none;"
onmouseover="this.style.color='#ff0000'"
onmouseout="this.style.color='#d27204'">Python Data Analysis and Visualization</a><br>

<h2 style="font-weight: 800; font-size: 26px; margin-top: 20px">Track plan:</h2>

<!DOCTYPE html>
<html>
<head>
  <style>
    .pills {
      margin-left: 0;
      list-style: none;
      counter-reset: li;
      font-family: "Gill Sans";
    }
    .pills li {
      padding: 10px 0;
      max-width: 300px;
      position: relative;
      left: 1.5em;
      margin-bottom: 0.75em;
      padding-left: 1em;
      background: #E3DEDC;
      transition: background 0.3s ease;
      cursor: pointer;
      border: none;
      text-align: left;
      outline: none;
    }
    .pills li:hover {
      background: #D3CFCF;
    }
    .pills li a {
      display: block;
      text-decoration: none;
      color: inherit;
    }
    .pills li:before {
      padding: 10px 0;
      position: absolute;
      top: 0;
      bottom: 0;
      left: -1.5em;
      width: 1.875em;
      text-align: center;
      color: white;
      font-weight: bold;
      background: #ff8a00;
      border-bottom-left-radius: 70em;
      border-top-left-radius: 70em;
      counter-increment: li;
      content: counter(li);
    }
  </style>
</head>
<body>
<ol class="pills">
  <li><a href="https://codefinity.com/courses/v2/a65bbc96-309e-4df9-a790-a1eb8c815a1c" target='_blank'>ML Introduction with scikit-learn</a></li>
  <li><a href="https://codefinity.com/courses/v2/b22d1166-efda-45e8-979e-6c3ecfc566fc" target='_blank'>Linear Regression with Python</a></li>
  <li><a href="https://codefinity.com/courses/v2/b71ff7ac-3932-41d2-a4d8-060e24b00129" target='_blank'>Classification with Python</a></li>
  <li><a href="https://codefinity.com/courses/v2/268f387f-1889-4fe2-8a2a-fe303599df5f" target='_blank'>Cluster Analysis</a></li>
  <li><a href="https://codefinity.com/courses/v2/92a59fcb-df68-4d1c-a625-86ff343660db" target='_blank'>Ensemble Learning</a></li>

</ol>

</body>
</html>

Now we will briefly describe all the courses on the track and consider how you can apply the gained knowledge in practice.

Foundations of Machine Learning Track Overview

What is Machine Learning?

Key Elements of ML

Applications of ML