Testing and Quality assurance is the most important and critical part of a machine learning project. Here in this ppt, you can get more idea about How to Do Testing of Machine Learning Projects?
How Machine Learning works, the relationship between machine learning and other fields (AI, Data Science, Statistics, Big Data, and Data Mining).
Examples of ML (Regression, Classification)
Mathematics of ML
Machine learning involves using algorithms to optimize performance using example data or past experience. It is useful when human expertise does not exist, cannot be explained, or needs to adapt over time. The document discusses different types of machine learning including supervised learning techniques like classification and regression as well as unsupervised learning techniques like clustering. It provides examples of applications in various domains and lists resources for datasets, journals, and conferences in the machine learning field.
How Python can be used for machine learning?NexSoftsys
I would suggest you can use the python code for machine learning algorithms, in this presentation to easily implement and explore code in your projects.
Read more https://www.slideshare.net/nexsoftsys/why-do-we-use-python-and-ml-ai
The document discusses expert systems, which are computer programs that simulate human expertise to solve complex problems. It defines expert systems and describes their key components, including the knowledge base, inference engine, working memory, and explanation facility. The document also outlines the structure of rule-based expert systems, explaining how production rules work and the two main methods of inference: forward chaining and backward chaining. Finally, it briefly discusses the roles of domain experts, knowledge engineers, and knowledge users in developing expert systems.
This document provides an introduction to machine learning, including the main types (supervised, unsupervised, semi-supervised, and reinforcement learning) and key terminology like datasets, features, and models. It also outlines the typical machine learning process of data collection, preparation, training, evaluation, and tuning a model. Specific supervised and unsupervised learning techniques are mentioned along with examples of their applications.
This document discusses the key steps and considerations for planning, designing, and implementing an information system project, including:
1) Creating a project plan that defines the tasks, team members, timeline, costs, and resources.
2) Addressing important social and ethical design issues like privacy, security, intellectual property, and equity.
3) Using prototypes to help understand requirements for complex problems through an iterative process.
4) Conducting feasibility studies and choosing appropriate solutions based on whether the problem can and should be solved.
Lessons Learned from Testing Machine Learning SoftwareChristian Ramirez
1) Machine learning software is difficult to test compared to traditional software because it is monolithic rather than modular, so changing one part affects the whole system.
2) When testing machine learning models, you need to understand what the models have been taught from the training data, including potential issues like spurious correlations, rather than just checking inputs and outputs.
3) Testing machine learning software effectively requires a good mathematical foundation as well as an understanding of different machine learning techniques and how to implement them.
Sirada Thongaiem from class 6/5 describes the key elements of an information system including the components and principles of how computers operate, computer networking and communication systems, the features of computers and peripherals, and how to effectively solve problems using information technology. She discusses programming, developing computer projects, properly using hardware and software, searching the internet, using computers for decision making, and responsibly using information technology for work. Sirada also suggests guidelines for responsible information technology use.
How Machine Learning works, the relationship between machine learning and other fields (AI, Data Science, Statistics, Big Data, and Data Mining).
Examples of ML (Regression, Classification)
Mathematics of ML
Machine learning involves using algorithms to optimize performance using example data or past experience. It is useful when human expertise does not exist, cannot be explained, or needs to adapt over time. The document discusses different types of machine learning including supervised learning techniques like classification and regression as well as unsupervised learning techniques like clustering. It provides examples of applications in various domains and lists resources for datasets, journals, and conferences in the machine learning field.
How Python can be used for machine learning?NexSoftsys
I would suggest you can use the python code for machine learning algorithms, in this presentation to easily implement and explore code in your projects.
Read more https://www.slideshare.net/nexsoftsys/why-do-we-use-python-and-ml-ai
The document discusses expert systems, which are computer programs that simulate human expertise to solve complex problems. It defines expert systems and describes their key components, including the knowledge base, inference engine, working memory, and explanation facility. The document also outlines the structure of rule-based expert systems, explaining how production rules work and the two main methods of inference: forward chaining and backward chaining. Finally, it briefly discusses the roles of domain experts, knowledge engineers, and knowledge users in developing expert systems.
This document provides an introduction to machine learning, including the main types (supervised, unsupervised, semi-supervised, and reinforcement learning) and key terminology like datasets, features, and models. It also outlines the typical machine learning process of data collection, preparation, training, evaluation, and tuning a model. Specific supervised and unsupervised learning techniques are mentioned along with examples of their applications.
This document discusses the key steps and considerations for planning, designing, and implementing an information system project, including:
1) Creating a project plan that defines the tasks, team members, timeline, costs, and resources.
2) Addressing important social and ethical design issues like privacy, security, intellectual property, and equity.
3) Using prototypes to help understand requirements for complex problems through an iterative process.
4) Conducting feasibility studies and choosing appropriate solutions based on whether the problem can and should be solved.
Lessons Learned from Testing Machine Learning SoftwareChristian Ramirez
1) Machine learning software is difficult to test compared to traditional software because it is monolithic rather than modular, so changing one part affects the whole system.
2) When testing machine learning models, you need to understand what the models have been taught from the training data, including potential issues like spurious correlations, rather than just checking inputs and outputs.
3) Testing machine learning software effectively requires a good mathematical foundation as well as an understanding of different machine learning techniques and how to implement them.
Sirada Thongaiem from class 6/5 describes the key elements of an information system including the components and principles of how computers operate, computer networking and communication systems, the features of computers and peripherals, and how to effectively solve problems using information technology. She discusses programming, developing computer projects, properly using hardware and software, searching the internet, using computers for decision making, and responsibly using information technology for work. Sirada also suggests guidelines for responsible information technology use.
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 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.
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ A Computer Program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
Machine learning is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
Testing AI involves validating that AI systems perform as intended and are free of unintended behaviors. This includes testing the training data, model architecture, and system outputs. Challenges include the inability to test all possible inputs and scenarios, as well as accurately interpreting ambiguous or uncertain outputs. Emerging techniques use machine learning to automatically generate test cases, fuzz testing to introduce adversarial inputs, and model analysis to evaluate behaviors. Proper testing is crucial to ensure AI systems do not negatively impact users or society.
- 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 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.
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.
Machine learning and robotics are closely related fields. Machine learning allows robots to learn from experience and data without being explicitly programmed, which enables robots to perform complex tasks. Some examples of how machine learning is applied in robotics include computer vision to allow robots to see, imitation learning to mimic human behaviors, and self-supervised learning where robots generate their own training examples. Machine learning is used in applications such as medical technologies to help patients.
Hacking Predictive Modeling - RoadSec 2018HJ van Veen
This document provides an overview of machine learning and predictive modeling techniques for hackers and data scientists. It discusses foundational concepts in machine learning like functionalism, connectionism, and black box modeling. It also covers practical techniques like feature engineering, model selection, evaluation, optimization, and popular Python libraries. The document encourages an experimental approach to hacking predictive models through techniques like brute forcing hyperparameters, fuzzing with data permutations, and social engineering within data science communities.
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.
Machine learning is a branch of artificial intelligence that uses algorithms to allow computers to learn from data without being explicitly programmed. It works by building models from sample data known as training data, rather than following strictly static program instructions. The document then discusses examples of machine learning applications including self-driving cars, face and speech recognition. It also covers machine learning algorithms, training methods, and how machine learning is beginning to allow machines to outperform humans in certain tasks.
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.
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 different types of machine learning including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of each type, such as using labeled data to classify emails as spam or not spam for supervised learning, grouping fruits by color without labels for unsupervised learning, and using rewards to guide an agent through a maze for reinforcement learning. The document also covers applications of machine learning across different domains like banking, biomedical, computer, and environment.
At the EuroSTAR conference 2016 in Stockholm I presented about the testing of artificial intelligence and machine learning. But also about testing using intelligent machines.
This document provides an overview of machine learning, including definitions of key terminology, the typical machine learning process, different machine learning approaches (supervised, unsupervised, semi-supervised, and reinforcement learning), applications of machine learning, and advantages and disadvantages of machine learning. It discusses how machine learning allows systems to learn from data and improve automatically without being explicitly programmed.
Machine learning is a type of artificial intelligence that allows systems to learn from data without being explicitly programmed. The document provides an introduction to machine learning, explaining what it is, why it is used, common algorithms, advantages, and challenges. Some key challenges discussed include poor quality data, overfitting or underfitting training data, the complexity of machine learning processes, lack of training data, slow implementation speeds, and imperfections in algorithms as data grows.
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.
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Explore the top 8 Leading Frameworks of PythonNexSoftsys
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Similar to Easily apply Quality Assurance and Testing in the ML Project
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 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.
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ A Computer Program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
Machine learning is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
Testing AI involves validating that AI systems perform as intended and are free of unintended behaviors. This includes testing the training data, model architecture, and system outputs. Challenges include the inability to test all possible inputs and scenarios, as well as accurately interpreting ambiguous or uncertain outputs. Emerging techniques use machine learning to automatically generate test cases, fuzz testing to introduce adversarial inputs, and model analysis to evaluate behaviors. Proper testing is crucial to ensure AI systems do not negatively impact users or society.
- 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 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.
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.
Machine learning and robotics are closely related fields. Machine learning allows robots to learn from experience and data without being explicitly programmed, which enables robots to perform complex tasks. Some examples of how machine learning is applied in robotics include computer vision to allow robots to see, imitation learning to mimic human behaviors, and self-supervised learning where robots generate their own training examples. Machine learning is used in applications such as medical technologies to help patients.
Hacking Predictive Modeling - RoadSec 2018HJ van Veen
This document provides an overview of machine learning and predictive modeling techniques for hackers and data scientists. It discusses foundational concepts in machine learning like functionalism, connectionism, and black box modeling. It also covers practical techniques like feature engineering, model selection, evaluation, optimization, and popular Python libraries. The document encourages an experimental approach to hacking predictive models through techniques like brute forcing hyperparameters, fuzzing with data permutations, and social engineering within data science communities.
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.
Machine learning is a branch of artificial intelligence that uses algorithms to allow computers to learn from data without being explicitly programmed. It works by building models from sample data known as training data, rather than following strictly static program instructions. The document then discusses examples of machine learning applications including self-driving cars, face and speech recognition. It also covers machine learning algorithms, training methods, and how machine learning is beginning to allow machines to outperform humans in certain tasks.
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.
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 different types of machine learning including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of each type, such as using labeled data to classify emails as spam or not spam for supervised learning, grouping fruits by color without labels for unsupervised learning, and using rewards to guide an agent through a maze for reinforcement learning. The document also covers applications of machine learning across different domains like banking, biomedical, computer, and environment.
At the EuroSTAR conference 2016 in Stockholm I presented about the testing of artificial intelligence and machine learning. But also about testing using intelligent machines.
This document provides an overview of machine learning, including definitions of key terminology, the typical machine learning process, different machine learning approaches (supervised, unsupervised, semi-supervised, and reinforcement learning), applications of machine learning, and advantages and disadvantages of machine learning. It discusses how machine learning allows systems to learn from data and improve automatically without being explicitly programmed.
Machine learning is a type of artificial intelligence that allows systems to learn from data without being explicitly programmed. The document provides an introduction to machine learning, explaining what it is, why it is used, common algorithms, advantages, and challenges. Some key challenges discussed include poor quality data, overfitting or underfitting training data, the complexity of machine learning processes, lack of training data, slow implementation speeds, and imperfections in algorithms as data grows.
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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Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
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HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
HCL Notes and Domino License Cost Reduction in the World of DLAU
Easily apply Quality Assurance and Testing in the ML Project
1. How to Do Testing of
Machine Learning Projects?
2. • ML Stands for Machine Learning.
• Machine learning (ML) is the scientific study of algorithms and statistical models that
computer systems use to effectively perform a specific task without using explicit
instructions, relying on patterns and inference instead.
• Machine learning is affected by computer programs that automatically improve their
performance through experience.
• Machine learning is a subset of artificial intelligence. In the machine, learning
computers don’t have to be explicitly programmed but can change and improve their
algorithms by themselves.
• Machine Learning is changing the way software products and applications think and
respond to queries.
3. 1950
Alan Turning Created a
test to check if a
machine could fool a
human being into
believing it was taking
to a machine
1952
The first computer
learning program, a
game of checkers, was
written by Arthur
Samuel.
1957
First neural network
for computers was
invented by Frank
Rosenblatt, which
simulated the thought
processes of the
human brain.
1967
The Nearest Neighbor
Algorithm was written.
1979
Students of Stanford
University, California,
invented the Stanford
Cart which could
navigate and avoid
obstacles on its own.
1997
IBM's Deep Blue beats
the world champion at
Chess.
2002
A Software library for
Machine Learning,
named torch is first
released.
2016
Alpha Go algorithm
developed by Google
Deep Mind managed to
win five games out of
five in the Chinese
Board Game Go
competition.
5. There are many opportunities are available for Machine Learning.
Voice
Reorganization
Image
Recognition
Optical
Character
Recognition
Sensory Data
Analysis
Intelligent Data
Analysis
Advanced
Customization
6. • The following are some of the features of a Machine Learning model that needs to be
tested/quality assurance:
1. Quality Of data
2. Quality of Features
3. Quality Of ML algorithms
7. • Quality assurance is a set of practices that allow
you to assess the state of the System and improve
it.
• Quality assurance is the process of checking
mistakes and errors manufactured products and
avoiding the problem when delivering products or
services to customers.
• There is Quality assurance have the following
approaches.
• 1) Failure testing
• 2) Statistical control
• 3) Total quality management and many others.
8. • There is quality assurance not the particular official role for the machine learning.
• Here some cases when preparing data for machine learning
• There might be categorical (Textual, Boolean) values in the data set and not all
algorithms work great with textual values.
• Some features strength have higher values than others and are expected to be changed
for equal importance.
• Some time data will take the large dimensions and it will reduce after some time.
9. • Software testing will be one of the most critical factors that determine the success of a
machine learning system.
• Testing of the machine learning is not same as the testing process because in Machine
Learning Testing, looking for exactly the right output is exactly the wrong approach. and
generally in a testing situation, you seek to make sure that the actual output matches
the expected one.
10. • Testing will be used for the performed for securing the high performance of machine
learning models.
• the main problems you will encounter while dealing with machine learning are:
Understanding the questions being asked
Understanding the data supplied
Understanding the measure of success