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Top Machine Learning Models and Algorithms in 2021
Machine Learning can analyze millions of data sets and recognize patterns within minutes. While we know what Machine Learning is and what it does, there’s little that is known about the different Machine Learning models types. Algorithms and models form the basis for Machine Learning programs and applications that enterprises use for their technical projects.
There are multiple types of Machine Learning models. Each model provides specific instructions to the program for executing a task. In general, there are three different techniques under which different models can be classified.
This article will highlight the different techniques used in Machine Learning development. After that, we will focus on the top Machine Learning models examples and algorithms that enable the execution of applications for deriving insights from data.
Read more : Machine Learning: Everything you Need to Know
Machine Learning Development Techniques
There are three different types of techniques used in Machine Learning. They are based on the kind of input or output you want to generate and how you want the Machine Learning programs to work.
- Supervised learning
The supervised learning model takes a set of data as input and predicts a reasonable output from that data. It makes use of historical data to make the predictions. When there’s uncertainty, a supervised learning method is used to generate a known input and output. The data is labelled, and the model is trained to deliver a specific outcome every time some input data is fed to it. - Unsupervised learning
When there is labelled data, unsupervised learning is used for Machine Learning models. It is used for finding out inferences from unstructured data sets. There is no specific outcome that is mentioned in the beginning. The Machine Learning algorithms work on their own to identify similarities in data and automatically find a structure. Basically, it is used to find the hidden patterns in the data that make sense together. - Reinforcement learning
In the reinforcement technique of Machine Learning models types, the algorithms achieve a specific outcome after which there is some reinforcement for the same. The Machine Learning model takes action that goes toward the goal, after which it receives a reward. The reward for algorithms is the improvement in execution. The more times it achieves a specific outcome, the better it gets at performing the action.
Let’s have a look at the different models and Machine Learning algorithms that come under these three separate techniques of Machine Learning.
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Machine Learning Algorithms and Models for 2021
The types of Machine Learning models are mostly based on supervised learning as of now. Because there is so much uncertainty associated with unsupervised and reinforcement learning, most applications use supervised Machine Learning programs. Here is the Machine Learning models list that is primarily used by companies:-
- Linear regression
One of the most popular Machine Learning models Python developers use is Linear Regression. The algorithm establishes a linear relationship between two variables to deliver a specific result. It is used for predictive analytics, forecasting supply & demand, and assessing the different trends in the market. There is a dependent variable and an independent variable, which is an explanatory one that determines the result. - Binary classification
Most Machine Learning services companies build binary classification models using two variables. It offers either of the two possible outcomes. The binary classification Machine Learning model uses the logistic regression algorithm. There can be either “Yes or No”, “Right or Wrong”, or any other binary outcome for each execution. For example — “Is this email Spam?”. The answer could be Yes or No. - K-Nearest neighbours
Mostly used for classification, K-nearest neighbour is one of the classics in the Machine Learning models list. It makes use of old available data to classify data into different categories. The Machine learning program will use the data to identify similarities in each case. The K-nearest neighbour algorithm involves a parameter that determines how many neighbours will be used in the classification system. - Decision trees
Also known as regression trees, decision trees are extensively used for Machine Learning development. There is a tree-like model that learns from simple rules from the prior data in the tree. It makes observations about the input data and reaches a decision based on the branches and roots involved in reaching the conclusion. The route you take are the branches while each decision is called a node. They are used to predict survival rates, price of financial instruments, insurance premiums, and more. Checkout blog on Decision Tree Algorithm in Machine Learning - Naive Bayes
Used primarily in text classification problems, Naive Bayes Machine Learning algorithms focus on working with high-dimensional data sets, such as filtering spam or classifying a huge amount of news articles. Each variable is regarded as independent, and the features of input data are completely unrelated. At first, it may seem complex, but it’s an extremely efficient probabilistic classifier for Machine Learning programs. - Multi-class classification
Used by Python developers, the multi-class classification model is used to generate predictions and analysis for two or more classes. There are multiple data sets that are used to identify which dataset the outcome belongs to. The multi-class model predicts more than two outcomes from the input data. For example — “Is this product made of plastic, steel, or iron?” The model can classify the data and deliver from two or more outcomes.
Checkout blog on Machine Learning Solutions for Digital Transformation
Machine Learning Models are evolving in 2021
With each year, Machine Learning models are evolving. There are newer advancements that can enable better execution. Apart from the ones mentioned above, there is Random Forest, Logistic Regression, Linear Discriminant model, AdaBoost, and Vector Quantization, among several others that are used for Machine Learning development.
Deciding which Machine Learning algorithms and models to choose depends on the type of data sets you have. Structured and unstructured data call for different model implementation. If you need proper consulting on Machine Learning development, our experts at BoTree Technologies can assist you. We provide complete Machine Learning services with different model implementations for predictive analytics, forecasting, pattern & image recognition, and more.
Contact us today for a FREE CONSULTATION.
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Top Machine Learning Models and Algorithms in 2021 was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
Data Science Curriculum for Professionals
If you are looking to expand or transition your current professional career that is buried in spreadsheet analysis into one powered by data science, then you are in for an exciting but complex journey with much to explore and master. To begin your adventure, following this complete road map to guide you from a gnome in the forest of spreadsheets to an AI wizard known far and wide throughout the kingdom.
Originally from KDnuggets https://ift.tt/3tYFz9b
Extraction of Objects In Images and Videos Using 5 Lines of Code
PixelLib is a library created for easy integration of image and video segmentation in real life applications. Learn to use PixelLib to extract objects In images and videos with minimal code.
Originally from KDnuggets https://ift.tt/3fe2d9a
Solve for Success: The Transformative Power of Data Visualization
Learn from experts and hear real-world use cases about how you and your organization can optimize data to enable innovation through visualization. Register now.
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15 Habits I Learned from Highly Effective Data Scientists
I’m using these habits in 2021 to become a more effective future data scientist.
Originally from KDnuggets https://ift.tt/3rhDGCJ
Invisible Workforce of the AI Era
Behind AI Industry— Data Labeling Services
Digital transformation has updated the global business through innovative technology. Among these, artificial intelligence (AI) has played an important role in accelerating this process and powering diverse industries such as manufacturing, medical imaging, autonomous driving, retail, insurance, and agriculture. A Deloitte survey found that in 2019, 53% of businesses adopting the AI model spent over $20 million on technology and talent acquisition.
The Surging Demand for Data Labelling Services
Thirty years ago computer vision systems could hardly recognize hand-written digits. But now AI-powered machines are able to facilitate self-driving vehicles, detect malignant tumors in medical imaging, and review legal contracts. Along with advanced algorithms and powerful compute resources, labeled datasets help to fuel AI’s development as well.
AI depends largely on data. The unstructured raw data need to be labeled correctly so that the machine learning algorithms can understand and get trained for better performance. Given the rapid expansion of digital transformation progress, there is a surging demand for high-quality data labeling services.
According to Fractovia, the data annotation market was valued at $ 650 million in 2019 and is projected to surpass $5billion by 2026. The expected market growth leads to the increasing transition of raw unlabeled data into no bias training data by the human workforce.
AI’s new workforce
Data labelers are described as “AI’s new workforce” or “invisible workers of the AI era”. They annotate a tremendous amount of raw datasets for AI model training. There are commonly 3 ways for AI companies to organize the data labeling service.
In-house
The AI enterprises hire part-time or full-time data labelers. As the labeling team is part of the company, the developers can have direct oversight of the whole annotation process. When the projects are quite specific, the team can adjust quickly. In general, it is more reasonable to have an in-house team for long-term AI projects as the data outputs should remain stable and consistent.
The cons of an in-house data labeling team are quite obvious. The labor cost is a huge fixed expense. Moreover, as the labeling loop contains many processes, such as building custom annotation tools, QC and QA, feedback mechanism, training a professional labeling team, etc., it takes time and effort to build infrastructures.
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Outsourcing
Hiring a third-party annotation service can be another option. Professional outsourced companies have experienced annotators who finish tasks with high efficiency as well. Specialized labelers can proceed with a large volume of datasets within a shorter period.
However, outsourcing may lead to less control over the labeling loop and the communication cost is comparably high. A clear set of instructions is necessary for the labeling team to understand what the task is about and do the annotations correctly. Task requirements may also change as developers optimize their models in each stage of testing.
Crowdsourcing
Crowdsourcing means sending data labeling tasks to individual labelers all at once. It breaks down large and complex projects into smaller and simpler parts for a large distributed workforce. A crowdsourcing labeling platform also implies the lowest cost. It is always the top choice when facing a tight budget constraint.
While crowdsourcing is considerably cheaper than other approaches, the biggest challenge, as we can imagine, is the accuracy level of the tasks. According to a report studying the quality of crowdsourced workers, the error rate of the task is significantly related to annotation complexity. For the basic description tasks, crowdsource workers’ error rate is around 6%, while the rate is up to 40% when it comes to sentiment analysis.
A turning point during COVID-19
Crowdsourcing has been proven beneficial during the COVID-19 crisis as in-house and outsourced data labelers are affected extremely due to lockdown. Meanwhile, people stuck indoors are now turning to more flexible jobs. Millions of unemployed or part-time workers are starting the crowdsourcing labeling work.
ByteBridge: a SAAS Labeling Platform
ByteBridge, a tech startup for data annotation service, providing high-quality and cost-effective data labeling services for AI companies.
High Quality
ByteBridge employs a consensus mechanism to guarantee accuracy and efficiency as the QA process is embedded into the labeling process.
Consensus mechanism — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.
Moreover, all the data is 100% manually reviewed.
The automated platform allows developers to set labeling rules directly on the dashboard. Moreover, developers can iterate data features, attributes, and task flow, scale up or down, make changes based on what they are learning about the model’s performance in each step of test and validation.
Developers can also check the processed data, speed, estimated price, and time.
Cost-effective
By cutting down the intermediary costs, ByteBridge offers the best value service. Transparent pricing lets you save resources for the more important parts.
End
“High-quality data is the fuel that keeps the AI engine running smoothly and the machine learning community can’t get enough of it. The more accurate annotation is, the better algorithm performance will be.” said Brian Cheong, founder, and CEO of ByteBridge.
Designed to empower AI and ML industry, ByteBridge promises to usher in a new era for data labeling and accelerates the advent of the smart AI future.
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Invisible Workforce of the AI Era was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
Via https://becominghuman.ai/invisible-workforce-of-the-ai-era-b488d5ecec3d?source=rss—-5e5bef33608a—4
source https://365datascience.weebly.com/the-best-data-science-blog-2020/invisible-workforce-of-the-ai-era
Do You Need Synthetic Data for Your AI Projects?
It can be difficult for businesses to build a supportive environment for data scientists to train the machine learning algorithms without a large amount of data collected from various data streams through products/services. Although data-gathering behemoths like Google and Amazon have no such problems, other businesses often lack access to the datasets they need.
Many businesses cannot afford to collect data because it is a costly undertaking. Companies are unable to pursue AI projects because of the high costs of collecting third-party data. As a result, businesses and academics are increasingly using synthetic dataset to build their algorithms. Synthetic data is information that is created in a lab and isn’t collected by precise measurement or any other means. Artificial data achieves the same results as actual data without jeopardizing privacy, according to a study conducted by MIT. The significance of AI synthetic data for AI project advances is discussed in this paper.
Benefits of synthetic data
Real data and synthesized data are not distinguished by machine learning (ML) algorithms. Using synthetic datasets that closely resemble the properties of real data, machine learning algorithms may generate unaltered results. With the progression in technology, the gap between synthetic and actual data is narrowing. Synthetic data would not only be cheaper and easier to produce than real-world data, but it also adds protection by minimizing the use of personal and confidential data.
It’s critical when access to actual data is restricted for AI research, training, or quality assurance due to data sensitivity and company regulations. It allows businesses of all sizes and resources to benefit from deep learning, where algorithms can learn unregulated from unstructured data, liberalizing AI, and machine learning.
Testing algorithms
Synthetic data is especially important for evaluating algorithms and generating evidence in AI initiatives that allow efficient use of resources. It’s used to verify the possible efficacy of algorithms and give investors desire to move further into full-scale implementation of such algorithms.
Making dependable predictions
High-risk, low-occurrence incidents (also called black blue events) such as machinery malfunctions, car crashes, and rare weather calamities can all be accurately predicted using synthetic data. Training AI systems to function well in all cases necessitates a large amount of data, which can be helped using this data. It is used in the healthcare industry to construct models of rare disease symptoms. AI algorithms are equipped to distinguish illness conditions by mixing simulated and real X-rays.
Unethical activity detection
Without revealing confidential financial information, synthetic data structures can be used to test and train fraud detection systems. Waymo, Alphabet’s self-driving project, put its self-driving cars through their paces by driving 8 million miles on actual roads and over 5 billion miles on virtual roads built with synthetic data.
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Preventing data breach
Third-party data companies can monetize data exchange directly or through data platforms using synthetic data without placing their customer’s data at risk. Compared to primary data privacy preserving methods, it can be used to provide more value while still providing more information. Synthetic data will help companies build data-driven products and services rapidly.
Chemical reactions analysis
Nuclear science is an intriguing use of synthetic data. Simulators are used to study chemical reactions, evaluate effects, and formulate safety measures prior to the construction of real nuclear facilities. In these simulations, scientists use agents, which are generated using synthetic data that perfectly reflects the chemical and physical properties of element particles, to better understand correlations between the particles and their surroundings. Trillions of calculations are involved in nuclear reaction simulations, which are performed on some of the largest supercomputers.
Text to speech efficacy
Synthetic data was used to train a customized speech model and boost information acquisition at a Fortune 100 business like IT or oil and gas company, decides to simplify and streamline the process of capturing information from geoscientists/data scientists whose resignation or moving to a new place is expensive.
This gap in knowledge adherence can be filled in for the client in deploying a voice-based virtual assistant that can ask a predefined collection of questions and record the answers of scientists. The virtual assistant’s custom speech model is trained using Microsoft Azure Speech Service. A mixture of language (text), acoustic (audio), and phonetic data is used to train the model (Word Sounds).
The speech model must be trained with simulated data in addition to data from real sources to enhance the virtual assistants transcribe and comprehension accuracy (interview recordings, publicly available geology language documents etc.)
To train and refine the speech model, researchers used synthesized speech from Google Wavenet, IBM Watson Speech, and Microsoft TTS. Their synthetic data trained solution will have higher accuracy in transliterate and comprehension than otherwise.
Challenges with synthetic data
It has its own plethora of issues to resolve. Regardless of the potential benefit, generating high-quality data, particularly if the process is complex, can be difficult. Furthermore, to synthesize trustworthy data, the generative models (which produce synthetic data) must be extremely accurate. The errors in synthetic data are compounded by generative model inaccuracy, resulting in poor data quality. It can contain implicit prejudices, which can be difficult to validate against credible proof.
When replicating complexities with the fields of data, inconsistencies may occur. Due to the sophistication of classification techniques, monitoring all necessary features to efficiently reproduce real-world data can be difficult. While fusion data, it’s occasionally possible to alter interpretations inside datasets. When used in an actual world, this can hamper the effectiveness of an algorithm.
Although there is a strong demand for high-quality data to drive AI and train machine learning models, there is indeed a scarcity of it. To help the AI initiatives, several businesses now use lab-generated synthetic data. When data is scarce and costly to procure, this data is especially useful. It would be the best choice for overcoming the difficulties of collecting real data for AI projects. The processes for generating simulated data increase will improve, but the quality of the data itself, to the point that are reasonably precise interpretations of the real world will be demonstrated.
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Do You Need Synthetic Data for Your AI Projects? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
Its Time To Reset The AI Application?
Do you think AI is changing your thinking ability? From applications recommending what movies to watch, what songs to listen, what to buy, what to eat, what ads you see, and the list goes on… all are driven by applications learning from you or delivering information through collective intelligence (i.e., people like you, location based etc.).
But are you sure the right recommendation is being provided to you or are you consuming the information as-is and adapting to it? Have you ever thought, would you have reached the same conclusion by applying your research and mental knowledge?
To add on, with information being readily available, less time and mental ability is spent on problem solving and more effort is spent on searching the solutions online.
As we build more smarter applications in future, which keeps learning everything about you, do you think this would change our thinking patterns even further?
Apart from AI systems trying to learn, there can be other ethical issues around trust and bias and how do you design and validate systems that provide recommendations which can be consumed by humans to provide unbiased decisions. I have covered this, as part of my earlier article — https://navveenbalani.dev/index.php/articles/responsible-and-ethical-ai-building-explainable-models/
As we are creators and validators of the AI system, the onus lies on us (humans) to ensure any technology is used for good.
As standards and compliance are still evolving in AI world, we should start designing systems that should let user decide how to use the application and when to reset it.
I am suggesting few approaches below to drive discussions in this area, which needs contribution from everyone to help deliver smart and transparent AI applications in future.
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The Uber Persona Model –
All applications build some kind of semantic user profiles incrementally to understand more about the user and provide recommendations. Making this transparent to the user should be first step.
Your application can have various semantic user profiles — one about you, one about your community (similar to you, location based etc..) and how this has been derived over a period of time. Finally your application should have a Reset Profile, that lets you reset your profile or a “Private AI” profile that enables you to use the application without knowing anything about you and let you discover the required information. Leaving the choice to the end-users on which profile to use, should lead to better control and transparency and making users build trust in the system.
Explainability and Auditability –
Designing applications with explainability in mind should be a key design principle. If the user receives an output from an AI algorithm, providing information as to why this output was presented and how relevant it is, should be built into the algorithm. This would empower users to understand why a particular information is being presented and turn on/off any preferences associated with an AI algorithm for future recommendations/suggestions.
For instance, take the example of server auditing, where you have tools that log every request and response, track changes in the environment, assess access controls and risk and provide end-to-end transparency.
Same level of auditing is required when AI delivers an output — what was the input, what version of model was used, what features were evaluated, what data was used for evaluation, what was the confidence score, what was the threshold, what output was delivered and what was the feedback.
Gamifying the Knowledge Discovery –
As information is readily available, how do you make it consumable in a way where you can nudge users to use their mental ability to find solutions, rather than giving all the information in one go. This would be particularly useful on how education in general (especially for schools/universities) , would be delivered to everyone in future.
How about a google like smart search engine, which delivers information that lets you test your skills. As mentioned earlier, in the Uber Persona Model section, the choice is up to the user to switch on/off this recommendation.
I hope this article, gave you enough insights on this important area.
To conclude , I would say the only difference between AI and we all in future, would be our ability to think and build the future we want.
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It’s Time To Reset The AI Application? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
Top 10 Python Libraries Data Scientists should know in 2021
So many Python libraries exist that offer powerful and efficient foundations for supporting your data science work and machine learning model development. While the list may seem overwhelming, there are certain libraries you should focus your time on, as they are some of the most commonly used today.
Originally from KDnuggets https://ift.tt/3lXHB6N
365 Data Science 