Here are the key steps to understand and install common Python packages for machine learning:
1. NumPy: NumPy is the fundamental package for scientific computing in Python. It provides multidimensional array and matrix data structures along with tools to work with these data structures.
2. SciPy: SciPy builds on NumPy and provides routines for integration, optimization, linear algebra, Fourier transforms, and more. SciPy contains modules for signals and image processing, optimization, special functions, clustering, and more.
3. scikit-learn: Scikit-learn is a powerful machine learning library that supports supervised and unsupervised learning. It features various classification, regression and clustering algorithms.
4. Matplotlib: Matplotlib is a
Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed.Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or unfeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach,Optical character recognition (OCR),learning to rank and computer vision.
In this presentation on machine learning I have talked about different types of machine learning algorithms like supervised learning , unsupervised learning, reinforcement learning. also I have talked about the difference between AI, ML, Data science, Deep learning.
Unlocking the Potential of Artificial Intelligence_ Machine Learning in Pract...eswaralaldevadoss
Machine learning is a subset of artificial intelligence that involves training computers to learn from data and make predictions or decisions based on that data. It involves building algorithms and models that can learn patterns and relationships from data and use that knowledge to make predictions or take actions.
Here are some key concepts that can help beginners understand machine learning:
Data: Machine learning algorithms require data to learn from. This data can come from a variety of sources such as databases, spreadsheets, or sensors. The quality and quantity of data can greatly impact the accuracy and effectiveness of machine learning models.
Training: In machine learning, training involves feeding data into a model and adjusting its parameters until it can accurately predict outcomes. This process involves testing and tweaking the model to improve its accuracy.
Algorithms: There are many different algorithms used in machine learning, each with its own strengths and weaknesses. Common machine learning algorithms include decision trees, random forests, and neural networks.
Supervised vs. Unsupervised Learning: Supervised learning involves training a model on labeled data, where the desired outcome is already known. Unsupervised learning, on the other hand, involves training a model on unlabeled data and allowing it to identify patterns and relationships on its own.
Evaluation: After training a model, it's important to evaluate its accuracy and performance on new data. This involves testing the model on a separate set of data that it hasn't seen before.
Overfitting vs. Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, leading to poor performance on new data. Underfitting occurs when a model is too simple and fails to capture important patterns in the data.
Applications: Machine learning is used in a wide range of applications, from predicting stock prices to identifying fraudulent transactions. It's important to understand the specific needs and constraints of each application when building machine learning models.
Overall, machine learning is a powerful tool that can help businesses and organizations make more informed decisions based on data. By understanding the basic concepts and techniques of machine learning, beginners can begin to explore the potential applications and benefits of this exciting field.
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
Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed.Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or unfeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach,Optical character recognition (OCR),learning to rank and computer vision.
In this presentation on machine learning I have talked about different types of machine learning algorithms like supervised learning , unsupervised learning, reinforcement learning. also I have talked about the difference between AI, ML, Data science, Deep learning.
Unlocking the Potential of Artificial Intelligence_ Machine Learning in Pract...eswaralaldevadoss
Machine learning is a subset of artificial intelligence that involves training computers to learn from data and make predictions or decisions based on that data. It involves building algorithms and models that can learn patterns and relationships from data and use that knowledge to make predictions or take actions.
Here are some key concepts that can help beginners understand machine learning:
Data: Machine learning algorithms require data to learn from. This data can come from a variety of sources such as databases, spreadsheets, or sensors. The quality and quantity of data can greatly impact the accuracy and effectiveness of machine learning models.
Training: In machine learning, training involves feeding data into a model and adjusting its parameters until it can accurately predict outcomes. This process involves testing and tweaking the model to improve its accuracy.
Algorithms: There are many different algorithms used in machine learning, each with its own strengths and weaknesses. Common machine learning algorithms include decision trees, random forests, and neural networks.
Supervised vs. Unsupervised Learning: Supervised learning involves training a model on labeled data, where the desired outcome is already known. Unsupervised learning, on the other hand, involves training a model on unlabeled data and allowing it to identify patterns and relationships on its own.
Evaluation: After training a model, it's important to evaluate its accuracy and performance on new data. This involves testing the model on a separate set of data that it hasn't seen before.
Overfitting vs. Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, leading to poor performance on new data. Underfitting occurs when a model is too simple and fails to capture important patterns in the data.
Applications: Machine learning is used in a wide range of applications, from predicting stock prices to identifying fraudulent transactions. It's important to understand the specific needs and constraints of each application when building machine learning models.
Overall, machine learning is a powerful tool that can help businesses and organizations make more informed decisions based on data. By understanding the basic concepts and techniques of machine learning, beginners can begin to explore the potential applications and benefits of this exciting field.
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
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 age of artificial intelligence, deep dives on machine learning and deep learning. Machine perception and applications. How company use AI in their businesses. Case study: Netflix. Basic tools for data manipulation and data visualization.
Abstract: Detection of fake news based on deep learning techniques is a major issue used to mislead people. For
the experiments, several types of datasets, models, and methodologies have been used to detect fake news. Also,
most of the datasets contain text id, tweets id, and user-based id and user-based features. To get the proper results
and accuracy various models like CNN (Convolution neural network), DEEP CNN, and LSTM (Long short-term
memory) are used
Machine Learning is a fascinating field that has been making headlines for its incredible advancements in recent years. Whether you're a tech enthusiast or just curious about how machines can learn, this article will provide you with a simple and easy-to-understand overview of some key Machine Learning concepts. Think of it as your first step towards a Machine Learning Complete Course!
Deep learning vs. machine learning what business leaders need to knowSameerShaik43
Artificial intelligence isn’t the future — it is the present. Already, businesses are deploying AI tools in a variety of ways: improving marketing and sales, guiding research and development, streamlining IT, automating HR and more.
https://www.tycoonstory.com/technology/deep-learning-vs-machine-learning-what-business-leaders-need-to-know/
Machine Learning The Powerhouse of AI Explained.pdfCIO Look Magazine
Artificial Intelligence (AI) and Machine Learning (ML) are two terms that have revolutionized the technology landscape, becoming integral in various sectors.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
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 age of artificial intelligence, deep dives on machine learning and deep learning. Machine perception and applications. How company use AI in their businesses. Case study: Netflix. Basic tools for data manipulation and data visualization.
Abstract: Detection of fake news based on deep learning techniques is a major issue used to mislead people. For
the experiments, several types of datasets, models, and methodologies have been used to detect fake news. Also,
most of the datasets contain text id, tweets id, and user-based id and user-based features. To get the proper results
and accuracy various models like CNN (Convolution neural network), DEEP CNN, and LSTM (Long short-term
memory) are used
Machine Learning is a fascinating field that has been making headlines for its incredible advancements in recent years. Whether you're a tech enthusiast or just curious about how machines can learn, this article will provide you with a simple and easy-to-understand overview of some key Machine Learning concepts. Think of it as your first step towards a Machine Learning Complete Course!
Deep learning vs. machine learning what business leaders need to knowSameerShaik43
Artificial intelligence isn’t the future — it is the present. Already, businesses are deploying AI tools in a variety of ways: improving marketing and sales, guiding research and development, streamlining IT, automating HR and more.
https://www.tycoonstory.com/technology/deep-learning-vs-machine-learning-what-business-leaders-need-to-know/
Machine Learning The Powerhouse of AI Explained.pdfCIO Look Magazine
Artificial Intelligence (AI) and Machine Learning (ML) are two terms that have revolutionized the technology landscape, becoming integral in various sectors.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Basic phrases for greeting and assisting costumers
ML-Chapter_one.pptx
1. 1
ML-Chapter One
Introduction to Machine Learning
Belay E., Asst. Prof.
e-mail: belayenyew@gmail.com
Mobile: 0946235206
University of Gondar
College of Informatics
Department of Information Technology
2. 2
The Way Forward
Topics Areas covered
Introduction to
Machine
Learning
Definition of Machine Learning, a brief history of Machine
Learning, Fundamentals of Machine Learning, Application of
Machine Learning
Data Preprocessing
Data Cleaning, data integration, Data reduction, Data transformation,
and data discretization
Supervised
Learning
Algorithms
Concept of Supervised Learning, Decision Trees, Naïve
Bayesian Classification, the k-Nearest Neighbors Classifiers,
Ensemble, Linear Discriminant Analysis, Support Vector
Machine, Time-Series Forecasting, Sequential Pattern Analysis
Evaluation
techniques
Metrics, cross-validation, statistics, Addressing the multiple
comparisons problem.
Unsupervised
Learning
Algorithms
Concept of unsupervised learning, k-Means Clustering,
Hierarchical Clustering, Gaussian Mixture Model, Hidden Markov
Model, Principal Component Analysis
Reinforcement
Learning
Introduction to Reinforcement Learning, Markov Decision
Process, Monte Carlo Methods for Prediction & Control
Deep learning
Concept of deep learning, Regularization , convolutional neural
networks, recurrent neural networks etc.
3. 3
Reference
1. Ethem ALPAYDIN. Introduction to Machine Learning, Third Edition, 2014
2. Mohssen Mohammed, Muhammad Badruddin Khan, Eihab Bashier,
Mohammed Bashier Machine Learning: Algorithms and Applications,2017
3. Ian Goodfellow Yoshua Bengio Aaron Courville, Deep Learning, 2016
Evaluation:
-Assignment-10%
-Article Review-10%
-project-15%
-Test -20%
-Final Exam-45%
5. 5
Introduction
Definition of Machine Learning(ML):
▶ It is the field of study that gives a computer the ability to learn
without being explicitly programmed (Arthur Samuel, 1959)
▶ A computer program is said to learn from experience E with
respect to some class of tasks T and performance measure P, if
its performance at tasks in T, as measured by P, improves with
experience E (Tom Mitchell, 1999).
▪ Task T: classifying handwritten digits from images
▪ Performance measure P : percentage of digits classified
correctly
▪ Training experience E: dataset of digits given classifications
▶ Learning is Generalization: the ability to perform a task in a
situation which has never been encountered before
6. 6
Introduction..
Why Machine Learning?
▶ Necessity: many things we want to do cannot be done by “programming”.
▶ Recent progress in algorithms and theory
▶ Growing flood of online data
▶ Computational power is available
▶ Growth of Industry etc..
When Do We Need Machine Learning?
▶ Tasks That Are Too Complex to Program:
▪ Tasks beyond Human Capabilities-analysis of very large and complex data
sets like medical archives, weather prediction and electronic commerce etc.
▪ Develop systems that are too difficult or impossible to construct manually
▶ Adaptivity:
▪ Programmed tools is rigidity
▪ Develop systems that can automatically adapt and customize themselves to
the needs of the individual user through experience
▪ Machine learning tools –adapts to their input data – adaptive to changes in
the environment they interact with.
7. 7
Introduction..
AI vs ML vs DL
Artificial
Intelligence(AI)
Machine
Learning(ML)
Deep Learning
(DL)
A technique which enables a
machine to mimic human
behaviors
A subset of AI technique which uses statistical
methods to enable machine to improve with
experiences
A subset of ML which makes the computation
of multilayers neural network feasible
8. 8
Introduction..
Basic Paradigm of ML
▶ Observe set of examples: Training data
▶ Infer something about process that generated data
▶ Use inference to make predictions about previously unseen data: test data.
9. 9
Introduction..
Brief History of Machine Learning
▶From the 1950s to now, machine learning has significantly developed.
THE TURING TEST 1950
▶ Alan Turing created the “Turing Test” to determine whether or not a computer
was capable of real intelligence.
▶ Turing explored the idea of how to determine whether machines can think
THE FIRST COMPUTER PROGRAM 1952
▶ Arthur Samuel created the first implementation of machine learning, the game
of checkers.
▶ In winning strategies, and incorporating those strategies into the game.
10. 10
Introduction..
Brief History of Machine Learning..
NEURAL NETWORKS FOR COMPUTERS 1957
▶ Frank Rosenblatt designed the first neural network which is called perceptron
for computers in 1957, which was meant to simulate the thought process of a
human brain.
“NEAREST NEIGHBOR” ALGORITHM 1967
▶ The “nearest neighbor” algorithm was written in 1967, allowing computers to
begin recognizing basic patterns. This could be used as a mapping route for
traveling salesmen.
EXPLANATION BASED LEARNING 1981
▶ EBL, or Explanation Based Learning, was created in 1981 by Gerald Dejong.
This concept allowed a computer to analyze training data and create a
general rule it can follow by discarding unimportant data.
11. 11
Introduction..
Brief History of Machine Learning..
NetTalk program 1985
Terry Sejnowski invented the NetTalk program that could learn to
like a baby does during the process of language acquisition.
The artificial neural network aimed to reconstruct a simplified
the complexity of learning human-level cognitive tasks
12. 12
Introduction..
Brief History of Machine Learning..
Machine Learning in 1990s
the work in machine learning shifted from the knowledge-driven
driven approach.
Scientists and researchers created programs for computers that
amounts of data and draw conclusions from the results.
This led to the development of the IBM Deep Blue computer,
world’s chess champion Garry Kasparov in 1997
13. 13
Introduction..
Brief History of Machine Learning
Deep Learning 2006
▶ This is the year when the term “deep learning” was coined by
▶ He used the term to explain a brand-new type of algorithms that
and distinguish objects or text in images or videos
14. 14
Introduction..
Brief History of Machine Learning
GOOGLE AND FACEBOOK UTILIZE MACHINE LEARNING 2014
▶ In 2014, Google and Facebook made machine learning the pivotal
technology of their businesses.
MACHINE LEARNING AND CUSTOMER CARE 2015
▶ In 2015, Interactions acquired AT&T’s Watson and the AT&T speech and
language research team. Combined with their award winning Adaptive
Understanding™ technology, Interactions delivers unprecedented accuracy
in understanding that helps enterprises revolutionize their customer care
experience.
15. 15
Introduction..
Brief History of Machine Learning
MACHINE LEARNING AND SOCIAL MEDIA 2017
▶ Acquired by Interactions in 2017, Digital Roots provides companies with AI-
based social media.
▶ Its technology allows brands to quickly filter, respond, and interact with
followers on social media.
17. 17
Introduction..
Supervised Learning(SL)
▶ The main goal in SL is to learn a model from labeled training data
that allows us to make predictions about unseen or future data.
▪ Here, the term supervised refers to a set of samples where the desired
output signals (labels) are already known.
▶ SL is where you have input variables(x) and an output variable(y)
and you use an algorithm to learn the mapping function from the
input to the output.
▶ It inference a function from labeled training data consisting of a set
of training examples. Y=f(X)
Where y is a target and x is input values
18. 18
Introduction..
Supervised Learning(SL)
▶ Example, we can predict the market value of a used car by analyzing other cars
and the relationship between car attributes (X) such as year of make, car brand,
mileage, etc., and the selling price of the car (y).
▶ SL can be:
▪ Classification: e-mail as Spam or Non-Spam, Bank customers as Fraud or default,
Patient as Diabetic or Non- diabetic etc.
▪ Regression/continuous value: predict electric power consumption of a city in
Kilo-watt, Predict house price based on square meter, Predict sale amount
based on advertisement etc.
19. 19
Introduction..
Unsupervised Learning
▶ In SL, we know the right answer beforehand when we train our model, and in
unsupervised learning, however, we are dealing with unlabeled data or data
of unknown structure
▶ Using unsupervised learning techniques, we are able to explore the structure
of our data to extract meaningful information without the guidance of a
known outcome variable.
Finding subgroups with clustering:
▶ Clustering is an exploratory data analysis technique that allows us to organize a
pile of information into meaningful subgroups (clusters) without having any
prior knowledge of their group memberships
▶ Each cluster defines a group of objects that share a certain degree of similarity
but are more dissimilar to objects in other clusters, which is why clustering is
also sometimes called "unsupervised classification”
20. 20
Introduction..
Unsupervised Learning..
▶ Clustering is a great technique for structuring information and deriving
meaningful relationships among data.
▪ For example, it allows marketers to discover customer groups based on their
interests in order to develop distinct marketing programs.
▶ The figure below illustrates how clustering can be applied to organizing
unlabeled data into three distinct groups based on the similarity of their
features x1 and x2 :
21. 21
Introduction..
Reinforcement Learning(RL):
▶ Reinforcement is the process of learning from rewards while performing a
series of actions.
▪ the goal is to develop a system (agent) that improves its performance based on
interactions with the environment
▶ In reinforcement learning, we do not tell the learner or agent, for example,
a (robot), which action to take but merely assign a reward to each action
and/or the overall outcome.
• Agent: takes actions
• Environment: the world in which agent exist
and operate
• Action: a move the agent can make in the
environment
• Action Space: the set of possible actions an
agent can make in the environment.
• Reward: feedback that measures the success
or failure of agent’s actions
22. 22
Introduction..
Reinforcement learning(RL)..
▶ RL is learning by interacting with a space or an environment
▶ An RL learns from the consequences of its actions, rather than from being
taught explicit.
▪ It selects its actions on basis of its past experience and also by new choices
▶ The machine learning program should be able to assess the goodness of
policies and learn from past good action sequences to be able to generate a
policy i.e. RL
▶ Example: Game playing
▪ A popular example of RL is a chess engine.
▪ Here, the agent decides upon a series of moves depending on the state of the board
(the environment), and the reward can be defined as win or lose at the end of the
game
▪ a single move by itself is not that important
▪ It is the sequence of right moves that is good.
▪ A move is good if it is part of a good game playing policy
23. 23
Introduction..
Example of Machine Learning Application
▶ Learning Associations: Basket analysis/association discovery
▪ In the case of retail-finding associations between products bought by customers:
If people who buy X typically also buy Y
▪ Going over our data and calculate that P(chips/beer) = 0.7.
▪ The rule can be defined: 70 percent of customers who buy beer also buy chips.
▶ Classification: Credit scoring
▪ The bank calculates the risk of giving the amount of credit for a customer based
on customer information.
▪ Customer Information:-income, savings, collaterals, profession, age, past financial
history, and so forth
▪ record of past loan- the loan was paid back or not
▪ The aim is to infer a general rule coding the association between a customer’s
attributes and his risk. A classification rule learned may be of the form:
Discriminant rule: IF income > θ1 AND savings > θ2
THEN low-risk
ELSE high-risk
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Introduction..
Application of Machine Learning..
▶ Pattern recognition: recognizing character codes from their images
▪ Handwritten—for example, to read zip codes on envelopes or amounts
on checks.
▪ People have different handwriting styles; characters may be written
small or large, slanted, with a pen or pencil, and there are many possible
images corresponding to the same character
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Introduction..
Example of Machine Learning Application..
▶ Face recognition
▪ The input is an image, the classes are people to be recognized, and the
learning program should learn to associate the face images to identities.
▪ Difficulty in lighting, glasses may hide the eyes and eyebrows, and a
beard may hide the chin.
▶ In medical diagnosis
▪ The inputs are the relevant information we have about the patient and
the classes are the illnesses.
▪ The inputs contain the patient’s age, gender, past medical history, and
current symptoms
▶ Biometrics is recognition
▪ Recognition or authentication of people using their physiological and/or
behavioral characteristics
▪ Examples of physiological characteristics are images of the face,
fingerprint, iris, and palm; examples of behavioral characteristics are
dynamics of signature, voice, gait, and key stroke
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Introduction..
Example of Machine Learning Application..
▶ Regression: Predict the price of a used car
▪ Inputs are the car attributes—brand, year, engine capacity, mileage, and
other information—that we believe affect a car’s worth.
▪ The output is the price of the car. Such problems where the output is a
number are regression problems
▶ Document clustering
▪ the aim is to group similar documents
▪ For example, news reports can be subdivided as those related to politics,
sports, fashion, arts, and so on
▪ Documents are then grouped depending on the number of shared
words.
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Introduction
Machine Learning Workflow..
Data Preparation
▶ The preprocessing of the data is one of the most crucial steps in any
machine learning application
▶ Data collected from the real world is transformed to a clean dataset.
▶ Raw data may contain missing values, inconsistent values, duplicate
instances etc.
▶ So, raw data cannot be directly used for building a model.
▶ Different methods of cleaning the dataset are-
▪ Handling missing values
▪ Removing duplicate instances from the dataset.
▪ Normalizing the data in the dataset.
▪ Extract meaningful features
▪ Dimensionality reduction
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Introduction
Machine Learning Workflow..
Choosing Learning Algorithm-
▶ The best performing learning algorithm is researched.
▶ It depends upon the type of problem that needs to solved
and the type of data we have.
▶ If the problem is to classify and the data is labeled,
classification algorithms are used.
▶ If the problem is to perform a regression task and the data
is labeled, regression algorithms are used.
▶ If the problem is to create clusters and the data is
unlabeled, clustering algorithms are used.
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Introduction
Machine Learning Workflow..
Training Model-
▶ The model is trained to improve its ability.
▶ The dataset is divided into training dataset and testing dataset.
▶ The training and testing split is order of 80/20 or 70/30.
▶ It also depends upon the size of the dataset.
▶ Training dataset is used for training purpose.
▶ Testing dataset is used for the testing purpose.
▶ Training dataset is fed to the learning algorithm.
▶ The learning algorithm finds a mapping between the input and the
output and generates the model.
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Introduction
Machine Learning Workflow..
Evaluating Model-
▶ The model is evaluated to test if the model is any good.
▶ The model is evaluated using the kept-aside testing dataset.
▶ It allows to test the model against data that has never been used before for
training.
▶ Metrics such as accuracy, precision, recall etc. are used to test the
performance.
▶ If the model does not perform well, the model is re-built using different hyper
parameters.
▶ The accuracy may be further improved by tuning the hyper parameters.
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Introduction
Limitation of Machine Learning
▶ Are not useful while working with high dimensional data, that is where we
have large number of inputs and outputs.
▶ Cannot solve crucial AI problem like NLP, image recognition etc.
▶ One of the big challenges with traditional machine learning model is a
process called feature extraction
▪ Such as object recognition, handwriting recognition huge challenges .
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Understand and install the following python packages
▶ NumPy
▶ SciPy
▶ scikit-learn
▶ Matplotlib
▶ pandas