- Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed by using example data. It is a form of artificial intelligence.
- There are three main types of machine learning: supervised learning where examples are labeled, unsupervised learning where unlabeled examples reveal inherent groupings of data, and reinforcement learning where an agent learns from trial and error using rewards.
- Machine learning has many applications including web search, computational biology, finance, robotics, and social networks. It involves collecting and preparing data, developing models, and evaluating models to make predictions on new data.
1. The document discusses machine learning types including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of applications like spam filtering, recommendations, and fraud detection.
2. Key challenges in machine learning are discussed such as poor quality data, lack of training data, and imperfections when data grows.
3. The difference between data science and machine learning is explained - data science is a broader field that includes extracting insights from data using tools and models, while machine learning focuses specifically on making predictions using algorithms.
This document provides an introduction to machine learning and data science. It discusses key concepts like supervised vs. unsupervised learning, classification algorithms, overfitting and underfitting data. It also addresses challenges like having bad quality or insufficient training data. Python and MATLAB are introduced as suitable software for machine learning projects.
Machine learning is a subset of artificial intelligence, which provides machines the ability to learn automatically and improve from experience without being explicitly programmed.
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
The document discusses machine learning, providing definitions and examples. It outlines the history and development of machine learning, describes common applications like image and speech recognition. It also covers different types of machine learning including supervised, unsupervised, and reinforcement learning. Challenges in machine learning like data quality issues and overfitting/underfitting are addressed. Popular programming languages for machine learning like Python, Java, C/C++ are also listed.
Machine learning is a subset of artificial intelligence focused on developing algorithms and models that enable computers to learn from data without being explicitly programmed. There are three main types of machine learning: supervised learning which uses labeled training data, unsupervised learning which finds patterns in unlabeled data, and reinforcement learning where a computer agent learns to maximize rewards through trial and error interactions with an environment.
This document provides an overview of machine learning, including definitions, types, steps, and applications. It defines machine learning as a field that gives computers the ability to learn without being explicitly programmed. The document outlines the main types of machine learning as supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. It also describes the typical steps in a machine learning process as gathering data, preparing data, choosing a model, training, evaluation, and prediction. Examples of machine learning applications discussed include prediction, image recognition, natural language processing, and personal assistants. Popular machine learning languages and packages are also listed.
1. The document discusses machine learning types including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of applications like spam filtering, recommendations, and fraud detection.
2. Key challenges in machine learning are discussed such as poor quality data, lack of training data, and imperfections when data grows.
3. The difference between data science and machine learning is explained - data science is a broader field that includes extracting insights from data using tools and models, while machine learning focuses specifically on making predictions using algorithms.
This document provides an introduction to machine learning and data science. It discusses key concepts like supervised vs. unsupervised learning, classification algorithms, overfitting and underfitting data. It also addresses challenges like having bad quality or insufficient training data. Python and MATLAB are introduced as suitable software for machine learning projects.
Machine learning is a subset of artificial intelligence, which provides machines the ability to learn automatically and improve from experience without being explicitly programmed.
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
The document discusses machine learning, providing definitions and examples. It outlines the history and development of machine learning, describes common applications like image and speech recognition. It also covers different types of machine learning including supervised, unsupervised, and reinforcement learning. Challenges in machine learning like data quality issues and overfitting/underfitting are addressed. Popular programming languages for machine learning like Python, Java, C/C++ are also listed.
Machine learning is a subset of artificial intelligence focused on developing algorithms and models that enable computers to learn from data without being explicitly programmed. There are three main types of machine learning: supervised learning which uses labeled training data, unsupervised learning which finds patterns in unlabeled data, and reinforcement learning where a computer agent learns to maximize rewards through trial and error interactions with an environment.
This document provides an overview of machine learning, including definitions, types, steps, and applications. It defines machine learning as a field that gives computers the ability to learn without being explicitly programmed. The document outlines the main types of machine learning as supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. It also describes the typical steps in a machine learning process as gathering data, preparing data, choosing a model, training, evaluation, and prediction. Examples of machine learning applications discussed include prediction, image recognition, natural language processing, and personal assistants. Popular machine learning languages and packages are also listed.
Artificial Intelligence with Python | EdurekaEdureka!
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* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
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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.
Machine Learning is a subset of artificial intelligence that allows computers to learn without being explicitly programmed. It uses algorithms to recognize patterns in data and make predictions. The document discusses common machine learning algorithms like linear regression, logistic regression, decision trees, and k-means clustering. It also provides examples of machine learning applications such as face detection, speech recognition, fraud detection, and smart cars. Machine learning is expected to have an increasingly important role in the future.
Machine learning(ML) is the scientific study of algorithms and statistical models that computer systems used to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “Training Data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential customer of machine learning.
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data
Machine learning, deep learning, and artificial intelligence are summarized. Machine learning involves using algorithms to learn from data and make predictions without being explicitly programmed. Deep learning uses neural networks with many layers to learn representations of data with multiple levels of abstraction. Artificial intelligence is the broader field of building intelligent machines that can think and act like humans. Supervised, unsupervised, semi-supervised and reinforcement learning techniques are described along with common applications such as classification, clustering, recommendation systems, and game playing.
Machine Learning course in Chandigarh Joinasmeerana605
The machine learning process is iterative. Data collection and preparation are crucial. Feature engineering transforms raw data into meaningful representations. Model selection involves trying different algorithms. Training exposes the model to data and allows it to learn. We evaluate how well it performs on new data before finally deploying it for predictions.Join Machine Learning course in Chandigarh.
Machine Learning with Python- Methods for Machine Learning.pptxiaeronlineexm
The document discusses various machine learning methods for building models from data including supervised learning methods like classification and regression as well as unsupervised learning methods like clustering and dimensionality reduction. It also covers semi-supervised learning and reinforcement learning. Supervised learning uses labeled training data to learn relationships between inputs and outputs while unsupervised learning discovers patterns in unlabeled data.
This document provides an overview of data science and machine learning. It defines data science as using techniques to extract insights from data, including data cleaning, analysis, visualization, and predictive modeling. Machine learning is described as a subset of artificial intelligence that uses algorithms to enable computers to learn from and make predictions based on data, categorizing techniques into supervised, unsupervised, and reinforcement learning. The document also outlines common machine learning steps like data preprocessing, feature engineering, model selection, evaluation, and deployment.
It's Machine Learning Basics -- For You!To Sum It Up
Machine learning is a branch of artificial intelligence that uses data and algorithms to enable computers to learn and improve at tasks without being explicitly programmed. There are three main types of machine learning: supervised learning which uses labeled training data, unsupervised learning which finds patterns in unlabeled data, and reinforcement learning where an agent learns from trial-and-error interactions with an environment. Machine learning is important because it allows automation through data-driven pattern recognition, enables personalization at scale, and accelerates scientific discovery through analysis of massive and complex datasets.
Machine learning builds prediction models by learning from previous data to predict the output of new data. It uses large amounts of data to build accurate models that improve automatically over time without being explicitly programmed. Machine learning detects patterns in data through supervised learning using labeled training data, unsupervised learning on unlabeled data to group similar objects, or reinforcement learning where an agent receives rewards or penalties to learn from feedback. It is widely used for problems like decision making, data mining, and finding hidden patterns.
what-is-machine-learning-and-its-importance-in-todays-world.pdfTemok IT Services
Machine Learning is an AI method for teaching computers to learn from their mistakes. Machine learning algorithms can “learn” data directly from data without using an equation as a model by employing computational methods.
https://bit.ly/RightContactDataSpecialists
Introduction AI ML& Mathematicals of ML.pdfGandhiMathy6
Machine learning uses probability theory to deal with uncertainty that arises from noisy data, limited data sets, and ambiguity. Probability theory provides a framework to quantify and manipulate uncertainty. It allows optimal predictions given available information, even if that information is incomplete. Key concepts in probability theory for machine learning include defining sample spaces and events, calculating probabilities, working with joint, conditional, and independent probabilities, and using Bayes' rule. These concepts help machine learning algorithms make inferences from data.
Innovations in technology has revolutionized financial services to an extent that large financial institutions like Goldman Sachs are claiming to be technology companies! It is no secret that technological innovations like Data science and AI are changing fundamentally how financial products are created, tested and delivered. While it is exciting to learn about technologies themselves, there is very little guidance available to companies and financial professionals should retool and gear themselves towards the upcoming revolution.
In this master class, we will discuss key innovations in Data Science and AI and connect applications of these novel fields in forecasting and optimization. Through case studies and examples, we will demonstrate why now is the time you should invest to learn about the topics that will reshape the financial services industry of the future!
AI in Finance
This document provides lecture notes on machine learning. It begins with an introduction to machine learning, defining it as programming computers to optimize performance using example data or past experience. It describes the basic components of the learning process as data storage, abstraction, generalization, and evaluation. It then discusses different learning models, including logical models using Boolean expressions, geometric models using concepts like lines/planes or distance, and probabilistic models using probability. It outlines several applications of machine learning and different types of learning including supervised, unsupervised, and reinforcement learning.
This document provides an introduction to machine learning, covering various topics. It defines machine learning as a branch of artificial intelligence that uses algorithms and data to enable machines to learn. It discusses different types of machine learning, including supervised, unsupervised, and reinforcement learning. It also covers important machine learning concepts like overfitting, evaluation metrics, and well-posed learning problems. The history of machine learning is reviewed, from early work in the 1950s to recent advances in deep learning.
Machine learning basics by akanksha baliAkanksha Bali
This document provides an introduction to machine learning, including definitions of machine learning, why it is needed, and the main types of machine learning algorithms. It describes supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. For each type, it provides examples and brief explanations. It also discusses applications of machine learning and the differences between machine learning and deep learning.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Artificial Intelligence with Python | EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
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.
Machine Learning is a subset of artificial intelligence that allows computers to learn without being explicitly programmed. It uses algorithms to recognize patterns in data and make predictions. The document discusses common machine learning algorithms like linear regression, logistic regression, decision trees, and k-means clustering. It also provides examples of machine learning applications such as face detection, speech recognition, fraud detection, and smart cars. Machine learning is expected to have an increasingly important role in the future.
Machine learning(ML) is the scientific study of algorithms and statistical models that computer systems used to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “Training Data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential customer of machine learning.
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data
Machine learning, deep learning, and artificial intelligence are summarized. Machine learning involves using algorithms to learn from data and make predictions without being explicitly programmed. Deep learning uses neural networks with many layers to learn representations of data with multiple levels of abstraction. Artificial intelligence is the broader field of building intelligent machines that can think and act like humans. Supervised, unsupervised, semi-supervised and reinforcement learning techniques are described along with common applications such as classification, clustering, recommendation systems, and game playing.
Machine Learning course in Chandigarh Joinasmeerana605
The machine learning process is iterative. Data collection and preparation are crucial. Feature engineering transforms raw data into meaningful representations. Model selection involves trying different algorithms. Training exposes the model to data and allows it to learn. We evaluate how well it performs on new data before finally deploying it for predictions.Join Machine Learning course in Chandigarh.
Machine Learning with Python- Methods for Machine Learning.pptxiaeronlineexm
The document discusses various machine learning methods for building models from data including supervised learning methods like classification and regression as well as unsupervised learning methods like clustering and dimensionality reduction. It also covers semi-supervised learning and reinforcement learning. Supervised learning uses labeled training data to learn relationships between inputs and outputs while unsupervised learning discovers patterns in unlabeled data.
This document provides an overview of data science and machine learning. It defines data science as using techniques to extract insights from data, including data cleaning, analysis, visualization, and predictive modeling. Machine learning is described as a subset of artificial intelligence that uses algorithms to enable computers to learn from and make predictions based on data, categorizing techniques into supervised, unsupervised, and reinforcement learning. The document also outlines common machine learning steps like data preprocessing, feature engineering, model selection, evaluation, and deployment.
It's Machine Learning Basics -- For You!To Sum It Up
Machine learning is a branch of artificial intelligence that uses data and algorithms to enable computers to learn and improve at tasks without being explicitly programmed. There are three main types of machine learning: supervised learning which uses labeled training data, unsupervised learning which finds patterns in unlabeled data, and reinforcement learning where an agent learns from trial-and-error interactions with an environment. Machine learning is important because it allows automation through data-driven pattern recognition, enables personalization at scale, and accelerates scientific discovery through analysis of massive and complex datasets.
Machine learning builds prediction models by learning from previous data to predict the output of new data. It uses large amounts of data to build accurate models that improve automatically over time without being explicitly programmed. Machine learning detects patterns in data through supervised learning using labeled training data, unsupervised learning on unlabeled data to group similar objects, or reinforcement learning where an agent receives rewards or penalties to learn from feedback. It is widely used for problems like decision making, data mining, and finding hidden patterns.
what-is-machine-learning-and-its-importance-in-todays-world.pdfTemok IT Services
Machine Learning is an AI method for teaching computers to learn from their mistakes. Machine learning algorithms can “learn” data directly from data without using an equation as a model by employing computational methods.
https://bit.ly/RightContactDataSpecialists
Introduction AI ML& Mathematicals of ML.pdfGandhiMathy6
Machine learning uses probability theory to deal with uncertainty that arises from noisy data, limited data sets, and ambiguity. Probability theory provides a framework to quantify and manipulate uncertainty. It allows optimal predictions given available information, even if that information is incomplete. Key concepts in probability theory for machine learning include defining sample spaces and events, calculating probabilities, working with joint, conditional, and independent probabilities, and using Bayes' rule. These concepts help machine learning algorithms make inferences from data.
Innovations in technology has revolutionized financial services to an extent that large financial institutions like Goldman Sachs are claiming to be technology companies! It is no secret that technological innovations like Data science and AI are changing fundamentally how financial products are created, tested and delivered. While it is exciting to learn about technologies themselves, there is very little guidance available to companies and financial professionals should retool and gear themselves towards the upcoming revolution.
In this master class, we will discuss key innovations in Data Science and AI and connect applications of these novel fields in forecasting and optimization. Through case studies and examples, we will demonstrate why now is the time you should invest to learn about the topics that will reshape the financial services industry of the future!
AI in Finance
This document provides lecture notes on machine learning. It begins with an introduction to machine learning, defining it as programming computers to optimize performance using example data or past experience. It describes the basic components of the learning process as data storage, abstraction, generalization, and evaluation. It then discusses different learning models, including logical models using Boolean expressions, geometric models using concepts like lines/planes or distance, and probabilistic models using probability. It outlines several applications of machine learning and different types of learning including supervised, unsupervised, and reinforcement learning.
This document provides an introduction to machine learning, covering various topics. It defines machine learning as a branch of artificial intelligence that uses algorithms and data to enable machines to learn. It discusses different types of machine learning, including supervised, unsupervised, and reinforcement learning. It also covers important machine learning concepts like overfitting, evaluation metrics, and well-posed learning problems. The history of machine learning is reviewed, from early work in the 1950s to recent advances in deep learning.
Machine learning basics by akanksha baliAkanksha Bali
This document provides an introduction to machine learning, including definitions of machine learning, why it is needed, and the main types of machine learning algorithms. It describes supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. For each type, it provides examples and brief explanations. It also discusses applications of machine learning and the differences between machine learning and deep learning.
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3. INTRODUCTION TO MACHINE
LEARNING
Machine learning is programming computers to optimize a performance criterion
using example data or past experience.
There is no need to “learn” to calculate payroll
Learning is used when:
• Human expertise does not exist (navigating on Mars),
• Humans are unable to explain their expertise (speech recognition)
• Solution changes in time (routing on a computer network)
• Solution needs to be adapted to particular cases (user biometrics)
3
4. • Machine Learning is the field of study that gives computers the capability to learn without
being explicitly programmed. ML is one of the most exciting technologies that one would have
ever come across. As it is evident from the name, it gives the computer that which
• Machine learning is an application of Artificial Intelligence (AI) that provides systems the
ability to automatically learn and improve from experience without being explicitly
programmed.
• Machine learning focuses on the development of computer programs that can access data
and use it learn for themselves. makes it more similar to humans: The ability to learn.
• Machine learning is actively being used today, perhaps in many more places than one would
expect.
4
INTRODUCTION TO MACHINE
LEARNING
6. KEY TERMINOLOGY
Labels
A label is the thing we're predicting—the y variable in simple linear
regression. The label could be the future price of wheat, the kind of animal
shown in a picture, the meaning of an audio clip, or just about anything.
Features
A feature is an input variable—the x variable in simple linear regression.
A simple machine learning project might use a single feature, while a more
sophisticated machine learning project could use millions of features,
specified as: x1,x2,...xN
Models
A model defines the relationship between features and label. For example,
a spam detection model might associate certain features strongly with
"spam".
6
7. GROWTH OF MACHINE
LEARNING
Machine learning is preferred approach to
– Speech recognition, Natural language processing
– Computer vision
– Medical outcomes analysis
– Robot control
– Computational biology
This trend is accelerating
– Improved machine learning algorithms
– Improved data capture, networking, faster computers
– Software too complex to write by hand
– New sensors / IO devices
7
8. APPLICATIONS
• Web search
• Computational biology
• Finance
• E-commerce
• Space exploration
• Robotics
• Information extraction
• Social networks
• Debugging software
8
10. LEARNING PHASE
In Learning Phase, the machine learns through the discovery of patterns.
This discovery is made thanks to the data. One crucial part of the data
scientist is to choose carefully which data to provide to the machine.
The list of attributes used to solve a problem is called a feature
vector. You can think of a feature vector as a subset of data that is used
to tackle a problem.
The machine uses some fancy algorithms to simplify the reality and
transform this discovery into a model.
Therefore, the learning stage is used to describe the data and summarize
it into a model.
10
12. • When the model is built, it is possible to test how powerful it is on never-seen-
before data.
• The new data are transformed into a features vector, go through the model and
give a prediction.
• This is all the beautiful part of machine learning.There is no need to update the
rules or train again the model.
• You can use the model previously trained to make inference on new data.
12
Inference Phase
14. SUPERVISED MACHINE
LEARNING
• The process of algorithm learning from the training dataset can be thought of as a teacher supervising
the learning process.
• The possible outcomes are already known and training data is also labeled with correct answers.
• The algorithm generates a function that maps inputs to desired outputs.
• One standard formulation of the supervised learning task is the classification problem: the learner is
required to learn (to approximate the behavior of) a function which maps a vector into one of several
classes by looking at several input-output examples of the function.
14
15. Suppose we have input variables x and an output variable y and we applied an
algorithm to learn the mapping function from the input to output such as − Y = F(X)
Now, the main goal is to approximate the mapping function so well that when we
have new input data (x), we can predict the output variable (Y) for that data.
Mainly supervised leaning problems can be divided into the following two kinds of
problems −
1. Classification − A problem is called classification problem when we have the
categorized output such as “black”, “teaching”, “non-teaching”, etc.
2. Regression − A problem is called regression problem when we have the real
value output such as “distance”, “kilogram”, etc.Decision tree, random forest, knn, logistic
regression are the examples of supervised machine learning algorithms.
15
SUPERVISED MACHINE
LEARNING
16. • This algorithms do not have any supervisor to provide any sort of guidance.
• That is why unsupervised machine learning algorithms are closely aligned with
what some call true artificial intelligence
• we have input variable x, then there will be no corresponding output variables
as there is in supervised learning algorithms.
• In unsupervised learning there will be no correct answer and no teacher for the
guidance. Algorithms help to discover interesting patterns in data.
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Unsupervised Machine Learning
17. Unsupervised learning problems can be divided into the following
two kinds of problem −
1. Clustering − In clustering problems, we need to discover the inherent
groupings in the data. For example, grouping customers by their
purchasing behavior.
2. Association − A problem is called association problem because such
kinds of problem require discovering the rules that describe large
portions of our data. For example, finding the customers who buy
both x and y.
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Unsupervised Machine Learning
18. REINFORCEMENT
MACHINE LEARNING
• Reinforcement Learning is a feedback-based Machine learning technique in which an agent
learns to behave in an environment by performing the actions and seeing the results of
actions. For each good action, the agent gets positive feedback, and for each bad action,
the agent gets negative feedback or penalty.
• The agent learns automatically using feedbacks without any labeled data, unlike supervised
learning.
• There is no labeled data, so the agent is bound to learn by its experience only. RL solves a
specific type of problem where decision making is sequential, and the goal is long-term,
such as game-playing, robotics, etc.
• The agent interacts with the environment and explores it by itself. The primary goal of an
agent in reinforcement learning is to improve the performance by getting the maximum
positive rewards.
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19. REINFORCEMENT MACHINE LEARNING
• Agent learns from trail and error
• Environment where the Agents moves.
• Actions where all possible steps that the agent
can take.
• States where current condition returned by the
environment
• Reward where an instant return from the
environment.
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20. ISSUES IN MACHINE LEARNING
• Understanding Which Processes Need Automation
• Beginning Without Good Data
• Inadequate Infrastructure
• Implementation
• Lack of Skilled Resources
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21. APPLICATIONS OF
MACHINE LEARNING
• Virtual Personal Assistants
• Predictions while Commuting
• Videos Surveillance
• Social Media Services
• Email Spam and Malware Filtering
• Online Customer Support
• Search Engine Result Refining
• Product Recommendations
• Online Fraud Detection
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23. Building Machine Learning applications is an iterative process that
involves a sequence of steps. To build an ML application, follow
these general steps:
• Frame the core ML problem(s) in terms of what is observed and what answer you want
the model to predict.
• Collect, clean, and prepare data to make it suitable for consumption by ML model
training algorithms. Visualize and analyze the data to run sanity checks to validate the
quality of the data and to understand the data.
• Often, the raw data (input variables) and answer (target) are not represented in a way
that can be used to train a highly predictive model. Therefore, you typically should
attempt to construct more predictive input representations or features from the raw
variables.
• Feed the resulting features to the learning algorithm to build models and evaluate the
quality of the models on data that was held out from model building.
• Use the model to generate predictions of the target answer for new data instances.
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STEPS OF MACHINE
LEARNING PROCESS
24. REFERENCES
E Books-
Peter Harrington “Machine Learning In Action”,
DreamTech Press
Ethem Alpaydın, “Introduction to Machine Learning”, MIT
Press
Video Links-
https://www.youtube.com/watch?v=BRMS3T11Cdw&list=PL3pGy4
HtqwD2a57wl7Cl7tmfxfk7JWJ9Y
https://www.youtube.com/watch?v=EWmCkVfPnJ8&list=PL3pGy4H
tqwD2a57wl7Cl7tmfxfk7JWJ9Y&index=3
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