Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; 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. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in 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.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
The world today is evolving and so are the needs and requirements of people. Furthermore, we are witnessing a fourth industrial revolution of data.
Machine Learning has revolutionized industries like medicine, healthcare, manufacturing, banking, and several other industries. Therefore, Machine Learning has become an essential part of modern industry.
This slide will try to communicate via pictures, instead of going technical mumbo-jumbo. We might go somewhere but slide is full of pictures. If you dont understand any part of it, let me know.
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; 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. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in 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.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
The world today is evolving and so are the needs and requirements of people. Furthermore, we are witnessing a fourth industrial revolution of data.
Machine Learning has revolutionized industries like medicine, healthcare, manufacturing, banking, and several other industries. Therefore, Machine Learning has become an essential part of modern industry.
This slide will try to communicate via pictures, instead of going technical mumbo-jumbo. We might go somewhere but slide is full of pictures. If you dont understand any part of it, let me know.
List of top Machine Learning algorithms are making headway in the world of data science. Explained here are the top 10 of these machine learning algorithms - https://www.dezyre.com/article/top-10-machine-learning-algorithms/202
YouTube: https://youtu.be/LzaWrmKL1Z4
** Python Data Science Training: https://www.edureka.co/python **
In this PPT on “Reinforcement Learning Tutorial” you will get an in-depth understanding about how reinforcement learning is used in the real world. I’ll be covering the following topics in this session:
Introduction to Machine Learning
What is Reinforcement Learning?
Reinforcement Learning with an analogy
Reinforcement Learning process
Reinforcement Learning Counter-Strike example
Reinforcement Learning Definitions
Reinforcement Learning Concepts
Markov’s Decision Process
Understanding Q-Learning
Demo
Check out our Python Training Playlist: https://goo.gl/Na1p9G
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This presentation on "Supervised and Unsupervised Learning" will help you understand what is machine learning, what are the types of Machine learning, what is supervised machine learning, types of supervised machine learning, what is unsupervised learning, types of unsupervised learning and what are the differences between supervised and unsupervised machine learning. In supervised learning, the model learns from a labeled data whereas in unsupervised learning, model trains itself on unlabeled data. Now, let us get started and understand supervised and unsupervised learning and how they are different from each other.
Below are the topics explained in this supervised and unsupervised learning in Machine Learning presentation-
1. What is Machine Learning
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
2. Supervised Learning
- Types of Supervised Learning
3. Unsupervised Learning
- Types of Unsupervised Learning
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in 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.
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 modeling.
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 a 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 neighbors, 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
Learn more at: https://www.simplilearn.com/
This describes the supervised machine learning, supervised learning categorisation( regression and classification) and their types, applications of supervised machine learning, etc.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
A small informative presentation on machine learning.
It contains the following topics:
Introduction to ML
Types of Learning
Regression
Classification
Classification vs Regression
Clustering
Decision Tree Learning
Random Forest
True vs False
Positive vs Negative
Linear Regression
Logistic Regression
Application of Machine Learning
Future of Machine Learning
Slidedeck from our seminar about Machine Learning (07/11/2014)
Topics covered:
- What is Machine Learning?
- Techiques (clustering, classification, ...)
- Tools (Mahout, R, Spark MlLib, Weka, ...)
- Practical example of Machine Learning applications
- How to embed Machine Learning in software development
- Demo's
Machine Learning Ml Overview Algorithms Use Cases And ApplicationsSlideTeam
"You can download this product from SlideTeam.net"
Machine Learning ML Overview Algorithms Use Cases and Applications is for the mid level managers giving information about Machine Learning, how Machine Learning works, Machine Learning algorithms and its use cases. You can also learn the difference between Machine learning vs Traditional programming to understand how to implement machine learning in a better way for business growth. https://bit.ly/2ZaVSG9
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
Valencian Summer School 2015
Day 2
Lecture 11
The Future of Machine Learning
José David Martín-Guerrero (IDAL, UV)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
List of top Machine Learning algorithms are making headway in the world of data science. Explained here are the top 10 of these machine learning algorithms - https://www.dezyre.com/article/top-10-machine-learning-algorithms/202
YouTube: https://youtu.be/LzaWrmKL1Z4
** Python Data Science Training: https://www.edureka.co/python **
In this PPT on “Reinforcement Learning Tutorial” you will get an in-depth understanding about how reinforcement learning is used in the real world. I’ll be covering the following topics in this session:
Introduction to Machine Learning
What is Reinforcement Learning?
Reinforcement Learning with an analogy
Reinforcement Learning process
Reinforcement Learning Counter-Strike example
Reinforcement Learning Definitions
Reinforcement Learning Concepts
Markov’s Decision Process
Understanding Q-Learning
Demo
Check out our Python Training Playlist: https://goo.gl/Na1p9G
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This presentation on "Supervised and Unsupervised Learning" will help you understand what is machine learning, what are the types of Machine learning, what is supervised machine learning, types of supervised machine learning, what is unsupervised learning, types of unsupervised learning and what are the differences between supervised and unsupervised machine learning. In supervised learning, the model learns from a labeled data whereas in unsupervised learning, model trains itself on unlabeled data. Now, let us get started and understand supervised and unsupervised learning and how they are different from each other.
Below are the topics explained in this supervised and unsupervised learning in Machine Learning presentation-
1. What is Machine Learning
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
2. Supervised Learning
- Types of Supervised Learning
3. Unsupervised Learning
- Types of Unsupervised Learning
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in 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.
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 modeling.
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 a 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 neighbors, 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
Learn more at: https://www.simplilearn.com/
This describes the supervised machine learning, supervised learning categorisation( regression and classification) and their types, applications of supervised machine learning, etc.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
A small informative presentation on machine learning.
It contains the following topics:
Introduction to ML
Types of Learning
Regression
Classification
Classification vs Regression
Clustering
Decision Tree Learning
Random Forest
True vs False
Positive vs Negative
Linear Regression
Logistic Regression
Application of Machine Learning
Future of Machine Learning
Slidedeck from our seminar about Machine Learning (07/11/2014)
Topics covered:
- What is Machine Learning?
- Techiques (clustering, classification, ...)
- Tools (Mahout, R, Spark MlLib, Weka, ...)
- Practical example of Machine Learning applications
- How to embed Machine Learning in software development
- Demo's
Machine Learning Ml Overview Algorithms Use Cases And ApplicationsSlideTeam
"You can download this product from SlideTeam.net"
Machine Learning ML Overview Algorithms Use Cases and Applications is for the mid level managers giving information about Machine Learning, how Machine Learning works, Machine Learning algorithms and its use cases. You can also learn the difference between Machine learning vs Traditional programming to understand how to implement machine learning in a better way for business growth. https://bit.ly/2ZaVSG9
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
Valencian Summer School 2015
Day 2
Lecture 11
The Future of Machine Learning
José David Martín-Guerrero (IDAL, UV)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Big Data Expo 2015 - Big 4 Data BonaparteBigDataExpo
Sinds 2010 gebruikt het Nederlands Forensisch Instituut (NFI) Bonaparte voor slachtofferidentificatie. Deze software is speciaal voor het NFI ontwikkeld door Big4Data. Big4Data is specialist op het gebied van Machine Learning en Kunstmatige Intelligentie technieken en ontwikkelt modellen waarmee ‘Big Data’ vraagstukken beantwoord kunnen worden.
In deze keynote zal Willem Burgers, ontwikkelaar van Bonaparte, belichten hoe de software tot stand is gekomen en hoe de technieken die zijn ontwikkeld voor artificieel redeneren, worden gebruikt om het biologische verwantschapsmodel (op basis van DNA) te modelleren. Tot slot zal hij ingaan op enkele van de zaken waarvoor Bonaparte is gebruikt door het NFI; onder meer MH17 (2014), Tripoli (2010), de Vaatstra zaak (2012) zullen worden besproken.
Accelerating Machine Learning with Cognitive Calibration - Kalpesh Balar, CoseerWithTheBest
Machine learning is one of the most prominent components of technology and development. See how cognitive calibration accelerates machine learning along with the benefits, challenges, and products.
Kalpesh Balar, Coseer
MusicMood - Machine Learning in Automatic Music Mood Prediction Based on Song...Sebastian Raschka
In this talk, I present a machine learning approach to build a music recommendation system that can identify happy songs in a music library based on song lyrics. Also, this presentation covers a general introduction to predictive modeling, naive Bayes classification, and text classification.
Slide presentasi ini dibawakan oleh Imron Zuhri dalam acara Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDO pada tanggal 14 Mei 2016.
Expert Session delivered during Workshop on
Image Processing and Machine Learning for Pattern Recoginition on 11th July 2016 at
University Institute of Engineering and Technology, Chandigarh
Applying Machine Learning and Artificial Intelligence to BusinessRussell Miles
Machine Learning is coming out of the halls of Academia and straight into the arms of those businesses looking for a competitive edge.
This session by the experts of GoDataScience.io on machine learning is designed to give a high level overview of the field of machine learning for business consumers covering:
- What Machine Learning is
- Where it came from
- Why we need it
- Why now
- How to make it real with the various toolkits and processes.
AI&BigData Lab.Руденко Петр. Automation and optimisation of machine learning ...GeeksLab Odessa
23.05.15 Одесса. Impact Hub Odessa. Конференция AI&BigData Lab
Руденко Петр (Инженер-программист, Datarobot) Automation and optimisation of machine learning pipelines on top of Apache Spark
В компании Datarobot мы занимаемся автоматизированным построением точных предсказательных моделей. Помимо непосредственного обучения модели, важную роль во всем процессе играет препроцессинг данных (feature selection/normalization/transformation). В своем докладе я поделюсь нашим опытом использования платформы Apache Spark и в частности новыми ml API, которые предоставляют функционал для построения пайплайнов (Pipeline), поиска оптимальных значений гиперпараметров моделей (Crossvalidation).
Подробнее:
http://geekslab.co/
https://www.facebook.com/GeeksLab.co
https://www.youtube.com/user/GeeksLabVideo
Machine Learning: Artificial Intelligence isn't just a Science Fiction topicRaúl Garreta
In this presentation I show a brief introduction to Machine Learning and its applications. I also present two cloud platforms for Machine Learning: Microsoft Azure for Machine Learning and MonkeyLearn.
Evolved from the study of pattern recognition and computational learning theory in AI, It gives computers the ability to learn without being explicitly programmed
To know more, do more: Contact us
http://www.extentia.com/contact-us
This presentation is the part of the webinar conducted by CloudxLab. This was the free session on Machine Learning.
Cloudxlab conducts such webinars very frequently and to make sure you never miss the future webinar update, please see the 'Events' section at CloudxLab.com
This course will enable students to
Define machine learning and understand the basic theory underlying machine learning.
Differentiate supervised, unsupervised and reinforcement learning
Understand the basic concepts of learning and decision trees.
Understand neural networks and Bayesian techniques for problems appear in machine learning
Understand the instant based learning and reinforced learning
Perform statistical analysis of machine learning techniques.
After studying this course, students will be able to
Choose the learning techniques and investigate concept learning
Identify the characteristics of decision tree and solve problems associated with
Apply effectively neural networks for appropriate applications
Apply Bayesian techniques and derive effectively learning rules
Evaluate hypothesis and investigate instant based learning and reinforced learning
Reinforcement Learning In AI Powerpoint Presentation Slide Templates Complete...SlideTeam
Showcase how machines are built to perform intelligent tasks by using our content-ready Reinforcement Learning In AI PowerPoint Presentation Slide Templates Complete Deck. Take advantage of these artificial intelligence PowerPoint visuals, and describe how machine learning models are trained to make sequences of decisions in a complex environment. Showcase the types of artificial intelligence such as deep learning, machine learning. Explain the concept of machine learning which delivers predictive models based on the data fed into machine learning algorithms. Take the assistance of our visually attention-grabbing reinforcement learning PowerPoint templates and discuss the effective uses of artificial intelligence in various areas such as supply chain, human resources, fraud detection, knowledge creation, research, and development, etc. You can also present the usage of AI in healthcare. This includes treatment, diagnosis, training and research, early detection, etc. Explain the working of machine learning by downloading our attention-grabbing supervised learning PowerPoint presentation. https://bit.ly/3kQBnEZ
Automating fetal heart monitor using machine learningTamjid Rayhan
This is a webinar held by the IEEE student branch of University of Chittagong. This talks about how a beginner can gain expert level knowledge in Machine learning and deep learning using online resources. It focuses on how the presentar solved a biomedical engineering problem using Machine learning. Also gives reference to many interesting references to advices given by the leaders of Machine learning field.
When the AIs failures send us back to our own societal biasesClément DUFFAU
From recruitment to car driving, we all know success and failures examples with AI. Let's focus on failing cases due to your own societal biases and how to taggle these new challenges
Supervised learning is a fundamental concept in machine learning, where a computer algorithm learns from labeled data to make predictions or decisions. It is a type of machine learning paradigm that involves training a model on a dataset where both the input data and the corresponding desired output (or target) are provided. The goal of supervised learning is to learn a mapping or relationship between inputs and outputs so that the model can make accurate predictions on new, unseen data.v
Similar to "Introduction to Machine Learning and its Applications" at sapthgiri engineering college bangalore (20)
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
21. Guess the Features?
• Medical Diagnosis
– Predict whether a patient will survive
• Features?
– Heart rate
– Systolic blood pressure
– White blood cell count
– Age
– ….
22
22. Guess the Features?
• Document Categorization
– Sports, Politics, Entertainment, …
• Features?
– Part of speech tags (noun, verb, etc)
– Word Counts
abacus abandon abbreviate zebra zygotic
12 0 1 ……….. 5 0
23
23. Guess the Features?
• Image Annotation
– Car or Not a car
• Features?
– RGB pixels.
– Circle detection
– Edge detection
– Corner detection
– …
Car
Not Car
24
30. Guess the Type of Learning?
• Given a bank customer’s profile, should I sanction
him/her a loan?
– Supervised Learning
• Given an audio track, separate the singer’s voice
from the background music.
– Unsupervised Learning
• Automatically group your personal collection of
photographs in Picasa into categories.
– Unsupervised Learning
• Given a patient’s X-ray image, diagnose if he has
cancer.
– Supervised Learning 32
31. Recap
• Introduction
– Why Machine Learning?
– What is Machine Learning?
• Feature Engineering
• Types of Learning
– Supervised Learning
– Unsupervised Learning
33
42. Reinforcement Learning
• No explicit training data set.
• Nature provides reward for each of the learners actions.
• At each time,
Learner has a state and chooses an action.
Nature responds with new state and a reward.
Learner learns from reward and makes better
decisions.
Learner Nature
Action
Reward
New State 49
46. DARPA Grand Challenge
• International competition for building autonomous
ground vehicles.
• Conducted by Defense Advanced Research Projects
Agency (DARPA) of United States.
• Challenge was to build a driverless car that can navigate
through a difficult track without human intervention.
53
48. DARPA Grand Challenge
• DARPA Grand Challenge 2005 was won by the
Stanford Racing Team.
• Supervised learning used to control speed and
identify obstacles.
• Unsupervised learning used to find the path.
55
49. What have we learnt?
• What is Machine Learning?
• Feature Engineering
• Types of Learning
– Supervised Learning
– Unsupervised Learning
– Reinforcement Learning
• Supervised Learning
– Classification
– Regression
• Interesting Applications
60
50. Resources
• Free online course by Andrew Ng
in coursera.org
– https://www.coursera.org/#course/ml
• E-learning course in NPTEL:-
– V. Susheela Devi and M. Narasimha Murty
– http://www.nptel.iitm.ac.in/
61
51. • Books on Machine Learning
– “Pattern Recognition and
Machine Learning” by
Christopher M Bishop
62
Resources
52. What do I need to know to get started with
Machine Learning?
• Linear Algebra
– To exploit structure in data
• Probability and Statistics
– To account for uncertainty in data
• Optimization
– To decide from a set of alternatives
63
53. Topics in Summer School
• Natural Language Processing
• Probability & Applications in ML
• Linear Algebra
• Optimization
• Support Vector Machine
• Matrix Factorization
• Computer Vision
64