Python for Data Science Advanced: Fundamentals of Machine Learning

Name: Python for Data Science Advanced: Fundamentals of Machine Learning
Author: Nizar Dajani

Learn about the different types of machine learning and its history

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Course: On-Demand

Hello, my name is Nizhar Dajani, and in these lessons, you will learn about the fundamentals of machine learning, its history, the different types, as well as the machine learning process and data properties. You will also learn about some of Python's popular machine learning modules, like scikit, pytorch, and tensorflow. Now in this lesson, you will learn the fundamentals of machine learning. So let's begin by defining what machine learning is in a very simple way. Machine learning is like teaching a computer to be really smart and learn things on its own, just like you and I learn new things. For example, imagine you have a robot friend and you want to teach it to recognize different fruits, like apples and bananas. At first, you show the robot many pictures of apples and bananas, and you tell it which one is which. The robot looks at the pictures and tries to find things that are the same in all the apple pictures and different from the banana pictures. So it might notice that apples are usually red or green and they have a round shape, while bananas are usually yellow and long. So machine learning is like teaching the robot to figure things out on its own by showing it many examples and it learns from these examples and can make smart guesses even when it sees something new. And just like you and I learn from our experiences and can recognize things and get better at doing so, machine learning helps computers do the same with the help of lots of data and some special tricks. Now I will share with you some definitions of machine learning from various sources. The first definition is machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. This quoted definition is by Arthur Samuel, who was one of the early researchers and pioneers in machine learning. He highlights the idea that machine learning allows computers to learn on their own without having every detail programmed by a human. The next definition is a computer program is set to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T as measured by P improves with experience E. So this definition is by Tom Mitchell, who is another pioneer in machine learning and he emphasizes that machine learning is about computers improving their performance on tasks through learning from data. The next definition is by Microsoft and it says machine learning is the practice of using algorithms to analyze and interpret data, discover patterns, and make more informed decisions or predictions. This definition by Microsoft simplifies the concept by focusing on using algorithms to analyze data and make better decisions. And last but not least, the following definition is from Google. Machine learning is the study of algorithms and statistical models that computer systems use to perform tasks without using explicit instructions, relying on patterns and inference instead. So as you can see, Google's definition highlights the reliance on patterns and inference rather than explicit instructions. So all these definitions capture the essence of machine learning as a field focused on enabling computers to learn and make decisions from data, often without explicit programming. It also involves the development of algorithms and models that can improve a computer's performance through experience and data analysis. Now in the field of machine learning, the terms, algorithms, and models are commonly heard and commonly used. They are closely related, but they refer to two different concepts. So I wanted to clarify both of them first. So the computer learns from data using the algorithm and creates a model to help it do things like making predictions. And on a more formal definition, an algorithm in machine learning refers to the specific set of rules, procedures, or computational steps that a machine learning system follows to learn from data and make predictions or decisions. We can also say that machine learning algorithms are responsible for the learning process itself. So in essence, algorithms are the core computational engines that enable machine learning systems to generalize from data and make predictions on new, unseen data. And for the formal definition of a model, we can say that a model in machine learning is the output or result of running a specific machine learning algorithm on a data set. It represents the learned patterns or relationships within the data. In other words, a model is a mathematical or computational representation of the underlining patterns in the training data that the algorithm has learned. This representation can be in the form of equations, graphs, decision boundaries, or neural network architectures depending on the algorithm used. And once a model is trained, it can be used to make predictions or classifications on new unseen data. For example, a trained image classification model can identify objects in images similar to the apples and bananas example I used earlier. So models are what enable machine learning systems to perform tasks and solve real world problems by leveraging the knowledge extracted from the training data. So in summary, we can say that machine learning algorithms are the techniques and procedures used to learn from data, while models are the learned representations of data that are created by running an algorithm on a data set. Models encapsulate the knowledge acquired during the learning process and are used for making predictions or decisions on new data. Different algorithms can produce different types of models, each suited to specific types of tasks and data sets. So in this lesson, you learned about the fundamentals of machine learning, stay tuned for the next lesson where you will learn about the history of machine learning. Thanks for watching.

Advanced Provider : Nizar Dajani 9 Lessons · 59m in English

Course Description

Machine learning is like teaching a computer to learn things on its own, just like a person learns new things. Imagine you have a robot friend and you want to teach it to distinguish between different fruits, such as apples and bananas. At first, you show the robot many pictures of apples and bananas and explain which one is which. The robot then looks at the pictures and tries to find things that are the same in all the apple pictures, and how they are different from the banana pictures. Machine learning teaches the robot to figure things out on its own by showing it many examples. It then learns from these examples and can make smart guesses even when it sees something that it’s never encountered before. Machine learning can be categorized into three main types based on the learning approach—we have supervised learning, unsupervised learning, and reinforcement learning in supervised learning. The robot is trained on a labeled data set where each example in the training data is associated with a target output. The goal is to learn a mapping from inputs to outputs, allowing the robot to make predictions or classifications on new unseen data.

In this KnowledgeCity course on Python for Data Science Advanced: Fundamentals of Machine Learning, you’ll learn about the fundamentals of machine learning, its history, and the different types. You’ll also learn about the machine learning process and data properties. Then we’ll cover some of Python's popular machine learning modules like SciKit, PyTorch, and TensorFlow.

What You'll Learn

Identify the different types of machine learning, including supervised, unsupervised, and reinforcement learning
Understand the history of machine learning
Explain the machine learning process and data properties
Distinguish how a labeled training data set maps inputs to target outputs for predictions and classifications
Explore Python's popular machine learning modules including Scikit-learn, PyTorch, and TensorFlow

Key Takeaways

Machine learning teaches a computer to learn from many examples and make smart guesses about data it has never encountered before.
Machine learning can be categorized into three main types based on the learning approach: supervised learning, unsupervised learning, and reinforcement learning.
In supervised learning, the model is trained on a labeled data set where each example is associated with a target output, learning a mapping from inputs to outputs.
The course covers the fundamentals of machine learning, its history, the machine learning process, and data properties.
Scikit-learn, PyTorch, and TensorFlow are popular Python modules for machine learning covered in this course.

Frequently Asked Questions

What will I learn in this course?

You'll learn about the fundamentals of machine learning, its history, and the different types, as well as the machine learning process and data properties. The course also covers popular Python machine learning modules like Scikit-learn, PyTorch, and TensorFlow.

What are the main types of machine learning covered?

The course covers three main types of machine learning based on the learning approach: supervised learning, unsupervised learning, and reinforcement learning.

Which Python machine learning libraries does this course cover?

It covers some of Python's popular machine learning modules, including Scikit-learn, PyTorch, and TensorFlow.

What skills does this course help build?

This course helps build skills in Data Interfaces, Machine Learning, Machine Learning Methods, and PyTorch (Machine Learning Library).

How is supervised learning explained in this course?

In supervised learning, the model is trained on a labeled data set where each example in the training data is associated with a target output, allowing it to learn a mapping from inputs to outputs and make predictions or classifications on new unseen data.