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What Is Machine Learning? | What Is Machine Learning And How Does It Work? | Simplilearn

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What is Machine Learning?

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Machine Learning
Artificial Intelligence vs Machine Learning vs Deep Learning
How does Machine Learning work?
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What Is Machine Learning? | What Is Machine Learning And How Does It Work? | Simplilearn

This presentation on Machine Learning will help you understand what is Machine Learning, Artificial Intelligence vs Machine Learning vs Deep Learning, how does Machine Learning work, types of Machine Learning, Machine Learning pre-requisites and applications of Machine Learning. Machine learning is a core sub-area of artificial intelligence. Machine Learning is a technique which uses statistical methods enabling machines to learn from their past data. it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. While the concept of machine learning has been around for a long time, the ability to apply complex mathematical calculations to big data has been gaining momentum over the last several years. Now, let us get started and understand the concept of Machine Learning in detail.

Below topics are explained in this "What is Machine Learning?" presentation:
1. Machine Learning
- What is Machine Learning
2. Artificial intelligence vs Machine Learning vs Deep Learning
3. How does Machine Learning work?
4. Types of Machine Learning
5. Machine Learning pre-requisites
6. Applications of Machine Learning

Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.

What skills will you learn from this Machine Learning course?

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

1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems.

We recommend this Machine Learning training course for the following professionals in particular:
1. Developers
2. Information Architects
3. Analytics Professionals
4. Graduates

Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course

This presentation on Machine Learning will help you understand what is Machine Learning, Artificial Intelligence vs Machine Learning vs Deep Learning, how does Machine Learning work, types of Machine Learning, Machine Learning pre-requisites and applications of Machine Learning. Machine learning is a core sub-area of artificial intelligence. Machine Learning is a technique which uses statistical methods enabling machines to learn from their past data. it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. While the concept of machine learning has been around for a long time, the ability to apply complex mathematical calculations to big data has been gaining momentum over the last several years. Now, let us get started and understand the concept of Machine Learning in detail.

Below topics are explained in this "What is Machine Learning?" presentation:
1. Machine Learning
- What is Machine Learning
2. Artificial intelligence vs Machine Learning vs Deep Learning
3. How does Machine Learning work?
4. Types of Machine Learning
5. Machine Learning pre-requisites
6. Applications of Machine Learning

Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.

What skills will you learn from this Machine Learning course?

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

1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems.

We recommend this Machine Learning training course for the following professionals in particular:
1. Developers
2. Information Architects
3. Analytics Professionals
4. Graduates

Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course

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What Is Machine Learning? | What Is Machine Learning And How Does It Work? | Simplilearn

  1. 1. What is Machine Learning?
  2. 2. Machine Learning Artificial Intelligence vs Machine Learning vs Deep Learning How does Machine Learning work? What’s in it for you? Types of Machine Learning What is Machine Learning? Machine Learning Pre-requisites Applications of Machine Learning
  3. 3. Today, let me tell you what is Machine Learning!
  4. 4. Machine Learning works on the development of computer programs that can access data and use it to automatically learn and improve from experience!
  5. 5. What is Machine Learning?
  6. 6. 1 Play your favorite music Order pizza from Dominos Voice control your home Request rides from Uber Amazon Echo - relies on Machine Learning, and with more data, it becomes more accurate!
  7. 7. Have you ever wondered the difference between AI, Machine Learning and Deep Learning?
  8. 8. Artificial Intelligence Machine Learning Deep Learning A technique which enables machines to mimic human behavior
  9. 9. Artificial Intelligence Machine Learning Deep Learning A technique which enables machines to mimic human behavior • IBM Deep Blue Chess • Electronic Game Characters
  10. 10. Artificial Intelligence Machine Learning Deep Learning A technique which uses statistical methods, enabling machines to learn from their past data
  11. 11. Artificial Intelligence Machine Learning Deep Learning A technique which uses statistical methods, enabling machines to learn from their past data • IBM Watson • Google Search Algorithm • Email Spam Filter
  12. 12. Artificial Intelligence Machine Learning Deep Learning Subset of Machine Learning, composing algorithms that allow a model to train itself and perform tasks
  13. 13. Artificial Intelligence Machine Learning Deep Learning Subset of Machine Learning, composing algorithms that allow a model to train itself and perform tasks • Alpha Go • Natural Speech Recognition
  14. 14. Now, let’s see how Machine Learning works?
  15. 15. Training data Train the ML algorithm Processing New input data ML Algorithm Prediction Re-train the algorithm Result
  16. 16. Let’s see the types of Machine Learning!
  17. 17. Supervised Learning Unsupervised Learning
  18. 18. Supervised Learning Unsupervised Learning
  19. 19. Supervised Learning Trained ModelKnown Data Unknown Data New ResponseML Algorithm It’s an apple! Processing
  20. 20. Algorithms Linear Regression Random Forest Polynomial Regression Logistic Regression Decision Trees K-Nearest Neighbours Naïve Bayes
  21. 21. Supervised Learning Unsupervised Learning
  22. 22. Unsupervised Learning Unknown Data Response I can see a pattern Trained ModelML Algorithm Processing
  23. 23. Algorithms Singular Value Decomposition Fuzzy Means Partial Least Squares K-means Clustering Apriori Hierarchical Clustering Principal Component Analysis
  24. 24. Machine Learning Pre-requisites!
  25. 25. Computer Science Fundamentals and Programming
  26. 26. Computer Science Fundamentals and Programming Intermediate Statistical Knowledge
  27. 27. Computer Science Fundamentals and Programming Intermediate Statistical Knowledge Linear Algebra and Intermediate Calculus
  28. 28. Computer Science Fundamentals and Programming Intermediate Statistical Knowledge Linear Algebra and Intermediate Calculus Data Wrangling and Cleaning
  29. 29. Some Applications of Machine Learning
  30. 30. Instance Segmentation
  31. 31. Object Detection Instance Segmentation
  32. 32. Number Plate Detection
  33. 33. Applications of Machine Learning
  34. 34. Applications of Machine Learning
  35. 35. Automatic Translation
  36. 36. Applications of Machine Learning
  37. 37. Summary What is Machine learning Machine learning workflow Type of Machine learning Applications of machine learningMachine learning pre-requisites

Notes de l'éditeur

  • Remove title case
  • To describe how classification is one thing, which is easy and basic function of ML, but to an advanced level it can also achieve object segmentation and we can talk about how is it important
  • We can talk about how ML is used in number plate detection and why is it significant
  • How ML has made life simpler by introducing translations`

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