Introduction to Artificial Intelligence and Machine Learning

This is the ‘Introduction to Artificial Intelligence and Machine Learning’ tutorial, which is part of the Machine Learning course offered by Simplilearn. In this tutorial, we will learn about Machine Learning, Machine Learning benefits, and various Machine Learning applications.

Objectives

On completion of this Machine Learning tutorial, you will learn how to:

  • Define Artificial Intelligence (AI) and understand its relationship with data
  • Define Machine Learning (ML) and understand its relationship with Artificial Intelligence
  • Understand Machine Learning approach and its relationship with data science
  • Identify the application

Definition of Artificial Intelligence

Artificial Intelligence refers to intelligence displayed by machines that simulate human and animal intelligence.

Data Economy

Let us quickly understand the importance of Data in brief.

  • The world is witnessing the real-time flow of all types of structured and unstructured data from social media, communication, transportation, sensors, and devices.
  • International Data Corporation (IDC) forecasts that 180 zettabytes of data will be generated by 2025.
  • This explosion of data has given rise to a new economy known as the Data Economy.
  • Data is the new oil that is precious but useful only when cleaned and processed.
  • There is a constant battle for ownership of data between enterprises to derive benefits from it.

The Emergence of Artificial Intelligence

The data economy with its vast reservoir is enabling unprecedented innovation in data sciences, the field which deals with extracting useful information and insights from the available data.

Data science is going toward a new paradigm where one can teach machines to learn from data and derive a variety of useful insights. This is known as Artificial Intelligence.

Artificial Intelligence in Practice

Given below in this Machine Learning tutorial are a few areas where AI is used widely.

  • Self-driving cars
  • Applications like Siri that understand and respond to human speech
  • Google’s AlphaGo AI has defeated many Go champions such as Ke Jie
  • Implementing AI in chess
  • Amazon ECHO product (home control chatbot device)
  • Hilton using Connie – concierge robot from IBM Watson

Data Facilitates Artificial Intelligence Products

Amazon pulls in data from its user database to recommend products to users. This functionality helps bring in more users. More users generate even more data that help enhance the recommendations even further.

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What is Machine Learning?

The capability of Artificial Intelligence systems to learn by extracting patterns from data is known as Machine Learning.

Machine Learning Benefits

Let us look at some of the benefits in this Machine Learning tutorial.

  • Powerful Processing
  • Better Decision Making & Prediction
  • Quicker Processing
  • Accurate
  • Affordable Data Management
  • Inexpensive
  • Analyzing Complex Big Data

Features of Machine Learning

Let us look at some of the features given below in this Machine Learning tutorial.

  • Machine Learning is computing-intensive and generally requires a large amount of training data.
  • It involves repetitive training to improve the learning and decision making of algorithms.
  • As more data gets added, Machine Learning training can be automated for learning new data patterns and adapting its algorithm.

Example: Learning from new spam words or new speech (also called incremental learning)

Artificial Intelligence and Machine Learning

Machine Learning is an approach or subset of Artificial Intelligence that is based on the idea that machines can be given access to data along with the ability to learn from it.

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Traditional Programming vs. Machine Learning Approach

Given below is an overview of Traditional Vs Machine Learning.

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Traditional programming relies on hard-coded rules.

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Machine Learning relies on learning patterns based on sample data. 

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As you go from rule-based systems to the deep learning ones, more complex features and input-output relationships become learnable.

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The Relationship between Data Science and Machine Learning

Let us understand the relationship between Data Science and Machine Learning.

  • Data Science and Machine Learning go hand in hand. Data Science helps evaluate data for Machine Learning algorithms
  • Data science is the use of statistical methods to find patterns in the data.
  • Statistical machine learning uses the same math and techniques as data science.
  • These techniques are integrated into algorithms that learn and improve on their own.
  • Machine Learning facilitates Artificial Intelligence as it enables machines to learn from the patterns in data.

Machine Learning Techniques

Given below are some techniques in this Machine Learning tutorial.

  1. Classification
  2. Categorization
  3. Clustering
  4. Trend analysis
  5. Anomaly detection
  6. Visualization
  7. Decision making

Machine Learning Algorithms

Let us understand Machine Learning Algorithms in detail.

  • Machine Learning can learn from labeled data (known as supervised learning) or unlabelled data (known as unsupervised learning).
  • Machine Learning algorithms involving unlabelled data, or unsupervised learning, are more complicated than those with the labeled data or supervised learning
  • Machine Learning algorithms can be used to make decisions in subjective areas as well.

Examples:

  • Logistic Regression can be used to predict which party will win at the ballots.
  • Naïve Bayes algorithm can separate valid emails from spam.

Get to know about the applications of Machine Learning. Click here for more!

Applications of Machine Learning

Some of the applications of Machine learning mentioned below.

  • Image Processing
  • Robotics
  • Data Mining
  • Video Games
  • Text Analysis
  • Healthcare

The table contains the details of each application in detail.

Applications Uses

Image Processing

  • Image tagging and recognition
  • Self-driving cars
  • Optical Character Recognition (OCR)

Robotics

  • Human simulation
  • Industrial robotics

Data Mining

  • Anomaly detection
  • Grouping and Predictions
  • Association rules

Video Games

  • Some games implement reinforcement learning

Text Analysis

  • Sentiment Analysis
  • Spam Filtering
  • Information Extraction

Healthcare

  • Healthcare Startups

Machine Learning Tutorial Overview

By the end of this Machine Learning tutorial, you will be able to:

  • Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
  • Gain practical mastery of principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
  • Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
  • Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
  • Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning.
  • Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems

Target Audience

There is an increasing demand for skilled Machine Learning Engineers across all industries, making this Machine Learning certification course well-suited for participants at the intermediate level of experience. We recommend this Machine Learning training course for the following professionals in particular:

  • Developers aspiring to be a data scientist or machine learning engineer
  • Analytics managers who are leading a team of analysts
  • Business analysts who want to understand data science techniques
  • Information architects who want to gain expertise in machine learning algorithms
  • Analytics professionals who want to work in machine learning or artificial intelligence
  • Graduates looking to build a career in data science and machine learning
  • Experienced professionals who would like to harness machine learning in their fields to get more insights

Let’s look into the prerequisites below in this Machine Learning Tutorial.

Prerequisites

For this Machine Learning tutorial, you should have:

  • Familiarity with the fundamentals of Python programming
  • The familiarity of basic high school mathematics
  • An understanding of the basics of statistics

The course covers concepts of mathematics & statistics required for machine learning, and we will provide you with a free Python course when you purchase our Machine Learning course.

Lessons Covered

Let’s look into the lessons covered below in this Machine Learning tutorial.

Chapter No.

Chapter Name

Topics

Lesson 1

Introduction to Artificial Intelligence and Machine Learning

In this lesson, you will learn more about

  • Artificial Intelligence
  • Machine Learning
  • Machine Learning algorithms
  • Applications of Machine Learning

Lesson 2

Techniques of Machine Learning

In this lesson, you will learn more about

  • Supervised learning
  • Unsupervised learning
  • Semi-supervised and Reinforcement learning
  • Bias and variance trade-off
  • Representation learning

Lesson 3

Data Preprocessing

In this lesson, you will learn more about

  • Data preparation
  • Feature engineering
  • Feature scaling
  • Datasets
  • Dimensionality reduction

Lesson 4

Math Refresher

In this lesson, you will learn more about

  • Concepts of linear algebra
  • Eigenvalues, eigenvectors, and eigendecomposition
  • Introduction to Calculus
  • Probability and statistics

Lesson 5

Regression

In this lesson, you will learn more about

  • Regression and its types
  • Linear regression: Equations and algorithms

Lesson 6

Classification

In this lesson, you will learn more about

  • Meaning and types of classification
  • Logistic regression
  • K-nearest neighbors
  • Support vector machines
  • Kernel support vector machines
  • Naive Bayes
  • Decision tree classifier
  • Random forest classifier

Lesson 7

Unsupervised learning: Clustering

In this lesson, you will learn more about

  • Clustering algorithms
  • K-means clustering

Key Takeaways

Let us quickly look at what you have learned so far in this Machine Learning tutorial.

  • The explosion of data has given rise to a new economy known as the Data Economy
  • AI refers to the intelligence in machines that simulates human intelligence.
  • The capability of AI systems to learn by extracting patterns from data is known as Machine Learning
  • Statistical machine learning uses the same math and techniques as data science.
  • Artificial intelligence and Machine learning are being increasingly used in various functions such as image processing, text analysis, healthcare, data mining, robotics, and video games.

Conclusion

This concludes “Introduction to Artificial Intelligence and Machine Learning”. The next lesson is  "Techniques of Machine Learning."

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  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

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