This document provides an introduction to machine learning, including definitions, types of machine learning problems, common algorithms, and typical machine learning processes. It defines machine learning as a type of artificial intelligence that enables computers to learn without being explicitly programmed. The three main types of machine learning problems are supervised learning (classification and regression), unsupervised learning (clustering and association), and reinforcement learning. Common machine learning algorithms and examples of their applications are also discussed. The document concludes with an overview of typical machine learning processes such as selecting and preparing data, developing and evaluating models, and interpreting results.
Index.....................
History of Machine Learning.
What is Machine Learning.
Why ML.
Learning System Model.
Training and Testing.
Performance.
Algorithms.
Machine Learning Structure.
Application.
Conclusion.
----------------------------------------------
THANK YOU
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ A Computer Program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
Machine learning is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
Index.....................
History of Machine Learning.
What is Machine Learning.
Why ML.
Learning System Model.
Training and Testing.
Performance.
Algorithms.
Machine Learning Structure.
Application.
Conclusion.
----------------------------------------------
THANK YOU
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ A Computer Program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
Machine learning is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
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
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/
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
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
Two hour lecture I gave at the Jyväskylä Summer School. The purpose of the talk is to give a quick non-technical overview of concepts and methodologies in data science. Topics include a wide overview of both pattern mining and machine learning.
See also Part 2 of the lecture: Industrial Data Science. You can find it in my profile (click the face)
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
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/
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
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
Two hour lecture I gave at the Jyväskylä Summer School. The purpose of the talk is to give a quick non-technical overview of concepts and methodologies in data science. Topics include a wide overview of both pattern mining and machine learning.
See also Part 2 of the lecture: Industrial Data Science. You can find it in my profile (click the face)
Afternoons with Azure - Azure Machine Learning CCG
Journey through programming languages such as R, and Python that can be used for Machine Learning. Next, explore Azure Machine Learning Studio see the interconnectivity.
For more information about Microsoft Azure, call (813) 265-3239 or visit www.ccganalytics.com/solutions
Improving AI Development - Dave Litwiller - Jan 11 2022 - PublicDave Litwiller
A conversational tour through some things I’ve learned in helping scale-up stage client companies improve their AI development practices, especially where deep neural nets (DNNs) are in use.
Overview of Machine learning concepts – Over fitting and train/test splits, Types of Machine learning – Supervised, Unsupervised, Reinforced learning, Introduction to Bayes Theorem, Linear Regression- model assumptions, regularization (lasso, ridge, elastic net), Classification and Regression algorithms- Naïve Bayes, K-Nearest Neighbors, logistic regression, support vector machines (SVM), decision trees, and random forest, Classification Errors..
Machine Learning 2 deep Learning: An IntroSi Krishan
Provides a brief introduction to machine learning, reasons for its popularity, a simple walk through example and then a need for deep learning and some of its characteristics. This is an updated version of an earlier presentation.
The Art of Intelligence – A Practical Introduction Machine Learning for Orac...Lucas Jellema
Our technology has gotten smart and fast enough to make predictions and come up with recommendations in near real time. Machine Learning is the art of deriving models from our Big Data collections – harvesting historic patterns and trends – and applying those models to new data in order to rapidly and adequately respond to that data. This presentation will explain and demonstrate in simple, straightforward terms and using easy to understand practical examples what Machine Learning really is and how it can be useful in our world of applications, integrations and databases. Hadoop and Spark, real time and streaming analytics, Watson and Cloud Datalab, Jupyter Notebooks, Oracle Machine Learning CS and the Citizen Data Scientists will all make their appearance, as will SQL.
Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things.
Overview of basic concepts related to Data Mining: database, data model, fuzzy sets, information retrieval, data warehouse, dimensional modeling, data cubes, OLAP, machine learning.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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
1. HANDS-ON ONLINE TRAINING
ON DATA SCIENCE
KNOWLEDGE AND SKILLS FORUM
INTRODUCTION TO
MACHINE LEARNING
FAITHFUL ONWUEGBUCHE
OLUWASEUN ODEYEMI
AUGUSTINE OKOLIE
2. WHAT IS MACHINE
LEARNING
• Machine learning is a type of artificial
intelligence (AI) that enables computers to
learn and make decisions without being
explicitly programmed.
• Using algorithms that iteratively learn
from data, machine learning allows
computers to find hidden insights without
being explicitly programmed where to
look.
4. MACHINE
LEARNING VS.
STATISTICS
VS.
COMPUTER
SCIENCE
Machine Learning Statistics Computer Science
Objective
Focuses on learning from
data to make predictions or
decisions without being
explicitly programmed. It
prioritizes prediction
accuracy and
generalizability.
Aims to infer properties of
an underlying distribution
from a data sample. It
emphasizes understanding
and interpreting data and
probabilistic models.
Focuses on the
creation and
application of
algorithms to
manipulate, store, and
communicate digital
information.
Methodologie
s
Typically uses complex
models (like neural
networks) and large
amounts of data to train
models for prediction.
Utilizes both supervised
and unsupervised learning
methods.
Often employs simpler,
more interpretable
models. Focuses on
hypothesis testing,
experimental design,
estimation, and
mathematical analysis.
Involves algorithm
design, data
structures,
computation theory,
computer architecture,
software development,
and more.
Validation
Measures model
performance through
methods like cross-
validation and seeks to
improve generalization to
unseen data.
Validates models using
methods such as
confidence intervals, p-
values, and hypothesis
tests to quantify
uncertainty.
Uses formal methods
for verifying
correctness, analyzing
computational
complexity, and
proving algorithmic
bounds.
Primary
Concern
Creating models that can
learn from and make
decisions or predictions
based on data.
Drawing valid conclusions
and quantifying
uncertainty about
observed data and
underlying distributions.
Creating efficient
algorithms and data
structures to solve
computational
problems.
5. TYPES OF MACHINE LEARNING
Three Types of Problems
• Supervised
• Unsupervised
• Reinforcement
6. SUPERVISE
D
• Trained using labeled examples
• Desired output is known
• Methods include classification, regression, etc.
• Uses patterns to predict the values of the label on
additional unlabeled data
• Algorithms:
• Linear regression
• Logistic regression
• K-Nearest Neighbors (KNN)
• Decision Trees and Random Forests
• Support Vector Machines
• Naive Bayes
• Neural Networks
7. UNSUPERVIS
ED
• Used against data that has no historical labels
• Desired output is unknown
• Goal is to explore the data and find some
structure within the data
• Algorithms
• Anomaly detection
• K-means clustering
• Hierarchical clustering
• DBSCAN
• Principal Component Analysis (PCA)
• Neural Networks
8. REINFORCEMEN
T
• Algorithm discovers through trial and error
which actions yield the greatest rewards.
• Three primary components:
• the agent (the learner or decision maker),
• the environment (everything the agent
interacts with)
• actions (what the agent can do).
• Objective: the agent chooses actions that
maximize the expected reward over a given
amount of time.
• Algorithms
• Markov Decision Process
• Q-Learning
• Deep Q Network (DQN)
9. WHY USE IT?
• Machine learning based models can extract patterns from massive
amounts of data which humans cannot do because
• We cannot retain everything in memory or we cannot perform
obvious/redundant computations for hours and days to come up
with interesting patterns.
• “Humans can typically create one or two good models in a week;
machine learning can create thousands of models in a week” (Thomas
H. Davenport)
• Solve problems we simply could not before
10. USE CASES
• Email spam filter
• Recommendation systems
• Self driving car
• Finance
• Image Recognition
• Competitive machines
12. TO GIVE CREDIT, OR NOT TO GIVE CREDIT
• You are asked by your boss while working at Big Bank Inc. to
develop an automated decision maker on whether to give a
potential client credit or not.
13. WHAT IS THE QUESTION?
• What question are we trying to answer here? What problem are
we looking to solve?
14. SELECTING DATA
Feature
• An individual measurable property of a phenomenon being
observed
• Best found through industry experts
15. SELECTING DATA
Feature Extraction
• Feature extraction is a general term for methods of
constructing combinations of the variables to get around
certain problems while still describing the data with sufficient
accuracy.
• Analysis with a large number of variables generally requires a
large amount of memory and computation.
• Reducing the amount of resources required to describe a
large set of data
16. SELECTING DATA
PCA (Principal ComponentAnalysis)
• We have a huge list of different features
• Many of them will measure related properties and so will be
redundant
• Summarize with less features
18. DEVELOPING MODEL
• What is the problem being solved?
• What is the goal of the model?
• Minimize error on the “training” data
• Training data is the data used to train the model (all of it but the part we
removed)
19. DEVELOPING MODEL
Linear Model
• Relationships are modeled using linear predictor functions
whose unknown model parameters are estimated from the data
• Complex way of saying the model draws a line between two
categories (classification) or to estimate a value (regression)
• Linear regression the most common form of linear model
20. DEVELOPING MODEL
Non-linear Model
• A nonlinear model describes nonlinear relationships in
experimental data
• The parameters can take the form of an exponential,
trigonometric, power, or any other nonlinear function
21. DEVELOPING
MODEL
Overfitting vs. Bias in Machine
Learning
Overfitting Bias
Definition
Overfitting occurs when a
model fits the data more
than is warranted. It
captures the noise along
with the underlying
pattern in the data.
Bias is error from
erroneous assumptions in
the learning algorithm.
High bias can cause
an algorithm to miss
relevant relations between
features and
target outputs.
Consequence
Overfitting leads to a
smaller error on the
training data set but
a larger one on unseen
data, reducing the model's
ability to generalize.
High bias often leads
to underfitting, where the
model oversimplifies the
data and doesn't capture
its complexity.
Example
Creating a very complex
decision tree that classifies
each
training instance perfectly,
but performs poorly on
unseen data.
Fitting a quadratic dataset
using a linear model – the
model will consistently fail
to capture the
true relationship and make
errors.
24. DEVELOPING MODEL
Rule Addition
• Minimize error on the “training” data
• AND make sure that error in the “unseen” data is close to error in
the “training” data
25. DEVELOPING A MODEL
• Keep it Simple
• Go for simpler models over more complicated models
• Generally, the fewer parameters that you have to tune the better
• Cross-Validation
• K-fold cross validation is a great way to estimate error on training data
• Regularization
• Can sometimes help penalize certain sources of overfitting.
• LASSO
• Forces the sum of the absolute value of coefficients to be less than a
fixed value
• Effectively choosing a simpler model
26. DEVELOPIN
G A MODEL
Data Snooping or Data Dredging
• It's a form of bias that arises when you make decisions based on
the same data you've used to train and test your model.
• “If a data set has affected any step in the learning process, its
ability to access the outcome has been compromised”
• Experimenting
• Reuse of the same data set to determine quality of model
• Once a data set has been used to test the performance of a
data set, it should be considered contaminated
Source: Learning From Data, pg. 1
Coin: that’s a quarter, that’s a dime
Unsupervised: That’s a cluster, that’s another cluster
Renforcement:
Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns. Or it can find the main attributes that separate customer segments from each other. Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition. These algorithms are also used to segment text topics, recommend items and identify data outliers.
The agent will reach the goal much faster by following a good policy. So the goal in reinforcement learning is to learn the best policy.
In WWII, Allied bombers were key to strategic attacks, yet these lumbering giants were constantly shot down over enemy territory. The planes needed more armor, but armor is heavy. So extra plating could only go where the planes were being shot the most.
A man named Abraham Wald, a Jewish mathematician who’d been locked out of university positions and ultimately fled the persecution in his own home country of Hungary, was brought in to oversee the operation. He started with a simple diagram—the outline of a plane—and he marked bullet holes corresponding to where each returning bomber had been shot. The result was the anatomy of common plane damage. The wings, nose, and tail were blackened with bullet holes, so these were the spots that needed more armor.