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

1. What is Machine Learning?

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. Today, let me tell
you what is Machine
Learning!

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. What is Machine Learning?

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. Have you ever wondered the difference
between AI, Machine Learning and Deep
Learning?

8. Artificial Intelligence
Machine Learning
Deep Learning
A technique which enables
machines to mimic human behavior

9. Artificial Intelligence
Machine Learning
Deep Learning
A technique which enables
machines to mimic human behavior
• IBM Deep Blue Chess
• Electronic Game Characters

10. Artificial Intelligence
Machine Learning
Deep Learning
A technique which uses statistical
methods, enabling machines to
learn from their past data

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. Artificial Intelligence
Machine Learning
Deep Learning
Subset of Machine Learning,
composing algorithms that allow a
model to train itself and perform
tasks

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. Now, let’s see how Machine Learning
works?

15. Training data
Train the ML
algorithm
Processing
New input data ML Algorithm Prediction
Re-train the
algorithm
Result

16. Let’s see the types of Machine
Learning!

17. Supervised Learning
Unsupervised Learning

18. Supervised Learning
Unsupervised Learning

19. Supervised Learning
Trained ModelKnown Data
Unknown Data
New ResponseML Algorithm
It’s an apple!
Processing

20. Algorithms
Linear Regression
Random Forest
Polynomial Regression
Logistic Regression
Decision Trees
K-Nearest Neighbours
Naïve Bayes

21. Supervised Learning
Unsupervised Learning

22. Unsupervised Learning
Unknown Data Response
I can see a pattern
Trained ModelML Algorithm Processing

23. Algorithms
Singular Value
Decomposition
Fuzzy Means
Partial Least
Squares
K-means Clustering
Apriori
Hierarchical Clustering
Principal Component
Analysis

24. Machine Learning
Pre-requisites!

25. Computer Science
Fundamentals and
Programming

26. Computer Science
Fundamentals and
Programming
Intermediate Statistical
Knowledge

27. Computer Science
Fundamentals and
Programming
Intermediate Statistical
Knowledge
Linear Algebra and
Intermediate Calculus

28. Computer Science
Fundamentals and
Programming
Intermediate Statistical
Knowledge
Linear Algebra and
Intermediate Calculus
Data Wrangling and
Cleaning

29. Some Applications of
Machine Learning

30. Instance
Segmentation

31. Object Detection Instance Segmentation

32. Number Plate
Detection

33. Applications of Machine Learning

34. Applications of Machine Learning

35. Automatic
Translation

36. Applications of Machine Learning

37. Summary
What is Machine learning Machine learning workflow Type of Machine learning
Applications of machine learningMachine learning pre-requisites