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.
Active learning is a machine learning technique where the learner is able to interactively query the oracle (e.g. a human) to obtain labels for new data points in an effort to learn more accurately from fewer labeled examples. The learner selects the most informative samples to be labeled by the oracle, such as samples closest to the decision boundary or where models disagree most. This allows the learner to minimize the number of labeled samples needed, thus reducing the cost of training an accurate model. Suggested improvements include querying batches of samples instead of single samples and accounting for varying labeling costs.
This document summarizes a seminar presentation on machine learning. It defines machine learning as applications of artificial intelligence that allow computers to learn automatically from data without being explicitly programmed. It discusses three main algorithms of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labelled training data, unsupervised learning finds patterns in unlabelled data, and reinforcement learning involves learning through rewards and punishments. Examples applications discussed include data mining, natural language processing, image recognition, and expert systems.
This document provides an overview of machine learning. It begins with an introduction and definitions, explaining that machine learning allows computers to learn without being explicitly programmed by exploring algorithms that can learn from data. The document then discusses the different types of machine learning problems including supervised learning, unsupervised learning, and reinforcement learning. It provides examples and applications of each type. The document also covers popular machine learning techniques like decision trees, artificial neural networks, and frameworks/tools used for machine learning.
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Machine learning involves programming computers to optimize performance using example data or past experience. It is used when human expertise does not exist, humans cannot explain their expertise, solutions change over time, or solutions need to be adapted to particular cases. Learning builds general models from data to approximate real-world examples. There are several types of machine learning including supervised learning (classification, regression), unsupervised learning (clustering), and reinforcement learning. Machine learning has applications in many domains including retail, finance, manufacturing, medicine, web mining, and more.
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
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.
Supervised Machine Learning With Types And TechniquesSlideTeam
Supervised Machine Learning with Types and Techniques is for the mid level managers giving information about what is supervised machine learning, its types, how supervised machine learning, its advantages. You can also know the difference between Supervised and Unsupervised Machine learning to understand supervised machine learning in a better way for business growth. https://bit.ly/3ewivHm
Deep learning is a type of machine learning that uses neural networks inspired by the human brain. It has been successfully applied to problems like image recognition, speech recognition, and natural language processing. Deep learning requires large datasets, clear goals, computing power, and neural network architectures. Popular deep learning models include convolutional neural networks and recurrent neural networks. Researchers like Geoffry Hinton and companies like Google have advanced the field through innovations that have won image recognition challenges. Deep learning will continue solving harder artificial intelligence problems by learning from massive amounts of data.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
Machine learning and its applications was a gentle introduction to machine learning presented by Dr. Ganesh Neelakanta Iyer. The presentation covered an introduction to machine learning, different types of machine learning problems including classification, regression, and clustering. It also provided examples of applications of machine learning at companies like Facebook, Google, and McDonald's. The presentation concluded with discussing the general machine learning framework and steps involved in working with machine learning problems.
The presentation provides an overview of machine learning, including its history, definitions, applications and algorithms. It discusses how machine learning systems are trained and tested, and how performance is evaluated. The key points are that machine learning involves computers learning from experience to improve their abilities, it is used in applications that require prediction, classification and pattern detection, and common algorithms include supervised, unsupervised and reinforcement learning.
Here are the key calculations:
1) Probability that persons p and q will be at the same hotel on a given day d is 1/100 × 1/100 × 10-5 = 10-9, since there are 100 hotels and each person stays in a hotel with probability 10-5 on any given day.
2) Probability that p and q will be at the same hotel on given days d1 and d2 is (10-9) × (10-9) = 10-18, since the events are independent.
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: 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.
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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.
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What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and 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 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
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AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
The Presentation answers various questions such as what is machine learning, how machine learning works, the difference between artificial intelligence, machine learning, deep learning, types of machine learning, and its applications.
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.
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.
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
This document provides an overview of machine learning concepts and techniques. It discusses supervised learning methods like classification and regression using algorithms such as naive Bayes, K-nearest neighbors, logistic regression, support vector machines, decision trees, and random forests. Unsupervised learning techniques like clustering and association are also covered. The document contrasts traditional programming with machine learning and describes typical machine learning processes like training, validation, testing, and parameter tuning. Common applications and examples of machine learning are also summarized.
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.
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.
In a world of data explosion, the rate of data generation and consumption is on the increasing side, there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection,
but making ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make a futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
Machine learning enables machines to learn from data and make predictions without being explicitly programmed. There are different types of machine learning problems like supervised learning (classification and regression), unsupervised learning (clustering), and reinforcement learning. Machine learning works by collecting data, preprocessing it, extracting features, selecting a model, training the model, evaluating it, and deploying it. Some common machine learning algorithms discussed are linear regression, logistic regression, and decision trees. Linear regression finds a linear relationship between variables to make predictions while logistic regression is used for classification problems.
This slide provides an overview of some of the core concepts related to building machine learning models. Machine learning is a branch of computer science that aims to make computers learn from data without being explicitly programmed. Learning problems can be classified into three main types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves learning a function that maps inputs to outputs, given a set of labeled examples. Unsupervised learning involves finding patterns or structure in unlabeled data. Reinforcement learning involves learning how to act or behave in an environment, given feedback or rewards from the environment.
Other important concepts related to machine learning include generalization, overfitting, representation, features, models, evaluation, optimization, bias-variance tradeoff, and Occam's razor. Generalization refers to the ability of a machine learning model to perform well on new or unseen data, not just on the training data. Overfitting occurs when a model fits the training data too closely, resulting in poor generalization. Representation refers to the way of encoding or describing the input and output data for a machine learning problem. Features are the attributes or characteristics of the input data that are used for learning. Models are the mathematical or computational structures that represent or approximate the function that maps inputs to outputs. Evaluation involves measuring the performance or accuracy of a machine learning model on a given data set. Optimization involves finding the best or optimal parameters or settings for a machine learning model that minimize the error or maximize the accuracy on the training data. Bias-variance tradeoff refers to the balance between model complexity and generalization ability. Occam's razor is a principle that favors simpler explanations or models when competing hypotheses explain the data equally well.
Understanding these core concepts is crucial for anyone who wants to learn and apply machine learning in practice. This slide provides a concise summary of these concepts and can serve as a useful reference for beginners and experts alike.
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 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 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.
1. The document summarizes a seminar on machine learning presented by Amit Kumar to the Rajkiya Engineering College.
2. It discusses key machine learning concepts like supervised learning techniques of classification and regression, as well as unsupervised learning techniques like clustering.
3. Applications of machine learning discussed include virtual assistants, social media services, image recognition, and medical diagnosis.
This document provides an overview of machine learning presented by Mr. Raviraj Solanki. It discusses topics like introduction to machine learning, model preparation, modelling and evaluation. It defines key concepts like algorithms, models, predictor variables, response variables, training data and testing data. It also explains the differences between human learning and machine learning, types of machine learning including supervised learning and unsupervised learning. Supervised learning is further divided into classification and regression problems. Popular algorithms for supervised learning like random forest, decision trees, logistic regression, support vector machines, linear regression, regression trees and more are also mentioned.
Choosing a Machine Learning technique to solve your needGibDevs
This document discusses choosing a machine learning technique to solve a problem. It begins with an overview of machine learning and popular approaches like linear regression, logistic regression, decision trees, k-means clustering, principal component analysis, support vector machines, and neural networks. It then discusses important considerations like knowing your data, cleaning your data, categorizing the problem, understanding constraints, choosing an algorithm, and evaluating models. Programming languages like Python and libraries, datasets, and cloud support resources are also mentioned.
In a world of data explosion, the rate of data generation and consumption is on the increasing side,
there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection but making an ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
Unit 1 - ML - Introduction to Machine Learning.pptxjawad184956
1. Machine learning involves using algorithms to learn from data and make predictions without being explicitly programmed. It includes supervised learning (classification and regression), unsupervised learning (clustering and association), and reinforcement learning.
2. Learning models can be divided into logical models (using logical expressions), geometric models (using geometry of data), and probabilistic models (using probability). Common algorithms include decision trees, k-nearest neighbors, Naive Bayes, and k-means clustering.
3. The learning process involves data storage, abstraction (creating models), generalization (applying knowledge), and evaluation (measuring performance). Machine learning has applications in areas like retail, finance, science, engineering, and artificial intelligence.
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2. What is
Machine Learning (M.L.) ?
Machine learning is an application of artificial
intelligence (AI) which gives devices the ability to
learn from their experiences and improve their self
without doing any coding
5. Machine learning refers to
a class of computer algorithms
that learn from examples
rather than being explicitly
programmed to perform a task.
6. Machine learning is a field which
focuses on the use of data and
algorithms to imitate the way
that humans learn, gradually
improving its accuracy.
7. Machine learning is a field of
study that looks at using
computational algorithms to
turn empirical data into
usable models.
8. Want to detect spam?
Want to forecast stocks?
Want to find out user
preferences?
Want your computer to
recognize you in group photos?
All the answers can be obtained
by using the power of ML.
10. Supervised
• In Supervised learning, you train the machine using
data which is well "labeled."
• It means data is already tagged with the correct
answer.
• It can be compared to learning which takes place in
the presence of a supervisor or a teacher.
• A supervised learning algorithm learns from labeled
training data, helps you to predict outcomes for
unforeseen data.
• One disadvantage of this learning method is that the
dataset has to be hand-labeled either by a Machine
Learning Engineer or a Data Scientist. This is a
very costly process, especially when dealing with
large volumes of data.
11. Unsupervised
• Unsupervised Learning is a machine
learning technique in which the users do
not need to supervise the model.
• Instead, it allows the model to work on its
own to discover patterns and information
that was previously undetected.
• It mainly deals with the unlabeled data.
• Unsupervised learning problems are
grouped into clustering and association
problems.
• The most basic disadvantage of
any Unsupervised Learning is that
it’s application spectrum is limited.
12. Semi-supervised
• Semi-supervised learning is the type of
machine learning that uses a combination of a
small amount of labeled data and a large
amount of unlabeled data to train models.
Intuitively, one may imagine the three types of
learning algorithms as :
• Supervised learning where a student is under
the supervision of a teacher at both home
and school.
• Unsupervised learning where a student has to
figure out a concept himself.
• Semi-Supervised learning where a teacher
teaches a few concepts in class and gives
questions as homework which are based on
similar concepts.
13. Reinforcement
• Reinforcement learning is the training of machine
learning models to make a sequence of decisions.
• In this approach, machine learning models are trained to
make a series of decisions based on the rewards and
feedback they receive for their actions.
• The machine learns to achieve a goal in complex and
uncertain situations and is rewarded each time it
achieves it during the learning period.
• Reinforcement learning is different from supervised
learning in the sense that there is no answer available, so
the reinforcement agent decides the steps to perform a
task.
• The machine learns from its own experiences when there
is no training data set present.
14. Based On Supervised
machine learning
Unsupervised
machine learning
Input Data Algorithms are
trained using
labeled data.
Algorithms are
used against
data which is not
labelled
Computational
Complexity
Supervised
learning is a
simpler method.
Unsupervised
learning is
computationally
complex
Accuracy Highly accurate
and trustworthy
method.
Less accurate
and trustworthy
method.
15. The goal of machine learning is to
develop methods that can automatically
detect patterns in data, and then to use
the uncovered patterns to predict
future data or other outcomes of
interest.
-- Kevin P. Murphy
17. Features
• Features are the fields used as input.
• A feature is one column of the data in your
input set.
• For instance, if you're trying to predict the
type of pet someone will choose, your input
features might include age, home region, family
income, etc.
• Feature means property of your training data.
• A feature is the input you have fed to the
model or system.
• The value of x variable in simple linear
regression are the features.
18. Label
• The output you get from your model
after training is called a label.
• A label is the thing we're predicting.
• For example the value of y variable in
simple linear regression model is the
label.
• Suppose you give your model data like
a person’s age, height, hair length and
then your model predicts whether the
person is male or female. Then male or
female is called the label.
20. Model
• A model is the relationship between features and the
label.
• An ML model is a mathematical model that generates
predictions by finding patterns in your data.
• ML Models generate predictions using the patterns
extracted from the input data.
• A model represents what was learned by a machine
learning algorithm.
• The model is the “thing” that is saved after running
a machine learning algorithm on training data and
represents the rules, numbers, and any other
algorithm-specific data structures required to make
predictions.
23. 1. Data
collection
Data collection is the process
of gathering and measuring information
from countless different sources.
This is a critical first step that involves
gathering data from various sources
such as databases, files, and external
repositories.
Before starting the data collection
process, it’s important to articulate the
problem you want to solve with an ML
model.
24. 2. Data
Preparation
Data preparation/pre-processing techniques
generally refer to the addition, deletion, or
transformation of training set data.
Since the collected data may be in an undesired
format, unorganized, or extremely large, further
steps are needed to enhance its quality. The
three common steps for preprocessing data are
formatting, cleaning, and sampling.
Data preparation (also referred to as
“data preprocessing”) is the process of
transforming raw data so that data scientists and
analysts can run it through machine
learning algorithms to uncover insights or make
predictions.
25. 3. Choose a
ML model
For different purpose, different ML
models are available. So it depends on
the need that which ML model must be
selected.
The choice of ML model to be selected
depends on many factors like the
problem statement and the kind of
output you want, type and size of the
data, the available computational time,
number of features, and observations in
the data, etc.
26. 4. Train the
model The process of training an ML model involves providing an
ML algorithm (that is, the learning algorithm) with
training data to learn from.
Let's say that you want to train an ML model to predict if
an email is spam or not spam.
You would provide ML model with training data that
contains emails for which you know the target (that is, a
label that tells whether an email is spam or not spam).
Then the model should be trained by using this data,
resulting in a model that attempts to predict whether
new email will be spam or not spam.
27. 5. Evaluate
the model
Model evaluation is a method of assessing the
correctness of models on test data. The test data
consists of data points that have not been seen by
the model before.
There are two methods of evaluating models in data
science, Hold-Out and Cross-Validation.
To avoid overfitting, both methods use a test set
(not seen by the model) to evaluate model
performance.
28. 6. Parameter
Tuning
Each model has its own sets of parameters that
need to be tuned to get optimal output.
For every model, our goal is to minimize the error
or say to have predictions as close as possible to
actual values. This is one of the cores or say the
major objective of hyperparameter tuning.
There are following three approaches to
Hyperparameter tuning:
•Manual Search
•Random Search
•Grid Search
29. 7. Make
predictions
“Prediction” refers to the output of
an algorithm after it has been trained on a
historical dataset.
Machine learning has two main goals:
prediction and inference.
After you have a model, you can use that model to
generate predictions which means to give your
model the inputs it has never seen before and
obtain the answer the model has predicted.
In addition to making predictions on new data, you
can use machine-learning models to better
understand the relationships between the input
features and the output target which is known as
inference.
30. •Traffic Alerts
•Social Media
•Transportation and Commuting
•Products Recommendations
•Virtual Personal Assistants
•Self Driving Cars
•Dynamic Pricing
•Google Translate
•Online Video Streaming
•Fraud Detection
Applications
Of
ML
31. Thank you for reading till end!
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