Due to the help of GenAI, I can now focus more on high-level problems that require strategic thinking rather than being stuck with repetitive tasks. In this article, I will break down the key use cases of GenAI for data scientists. I will also talk about some essential tools.

Whether you are an aspiring data scientist, experienced AI/ML practitioner, business analyst, or AI engineer, learn more about how GenAI transformed my work as a data scientist.

What Is GenAI?

GenAI or Generative AI refers to AI models that can generate new content based on its training data. For example, think of an AI model that is trained with tons of geopolitical data. When you ask the model to write you a paragraph or essay that isn’t present in the training data, the model can write entirely new things based on what it has learned from the training data. Such models are called generative AI or GenAI.

These AI models usually have transformer-based architectures. Some of the most effective GenAI models are GPT-4, BERT, T5, etc. Check the following chart to learn how GenAI is different from traditional AI.

Why Is GenAI Important for Data Scientists?

Data scientists need to perform an array of complex and time-consuming tasks. GenAI can assist in many of these tasks in the following ways.

GenAI Automates Repetitive Tasks

Preprocessing data takes up to 80% of a data scientist’s time. Previously, I had to process raw data manually to make the data suitable for model training. But, now I can use GenAI tools like OpenAI Codex, Pandas AI, etc., for automated preprocessing.

With these tools, I don’t need to do these repetitive tasks anymore and can save a lot of time that I use on other complex tasks.

It Enhances Data Quality and Augmentation

If I have to work with an imbalanced dataset, I can use GenAI to generate synthetic data. The data generated by AI simulates real-world distributions, so I can train the model with that synthetic data. It reduces the need for additional real-world data samples.

Code Generation and Debugging Gets Faster

Writing basic codes for AI models is another repetitive task that GenAI can now take over. I use GenAI tools like GitHub Copilot to generate code snippets. These tools can also be used for debugging and code improvements.

AI Does Better Model Tuning and Optimization

Fine-tuning hyperparameters is a complex job. Using GenAI tools helps me select the best possible configuration for ML models.

Easy to Get Insights and Reports

Generative AI can create detailed reports, brief summaries, etc., to provide the necessary insights in simple language. As a result, I can present the development of the process to all shareholders much easier than before.

What GenAI Can Do for a Data Scientist?

GenAI is now involved in the following areas of my workflow.

Data Processing and Augmentation

• It cleans up and normalizes raw data for me.
• I can fill in missing values of datasets using AI-powered imputation
• Data classes can be balanced by generating synthetic datasets

Feature Engineering and Selection

• Extracting important features from raw data has become more convenient
• It transformed unstructured data into structured formats automatically
• GenAI can recommend strategies for selecting model features

Code Generation and Debugging

• I can write Python, SQL, and other codes by just entering natural language prompts
• GenAI can debug my written code and suggest optimizations for a better structure
• Machine learning pipelines can be generated automatically

Model Optimization

• GenAI finds the best hyperparameter configurations for me
• Designing deep learning architectures is less time-consuming
• Training models become faster with GenAI

How GenAI Transformed My Data Science Workflow?

I have already mentioned areas where GenAI has been most helpful. Now, I want to give you a detailed breakdown of how GenAI transformed my work as a data scientist.

Task 1: Data Preprocessing and Cleaning

Traditional Workflow

Previously, I had to handle missing values, remove outliers, normalize data, and encode variables manually. For each task, I would need to write separate scripts or complete the tasks separately. It would take hours, or even days for large datasets. So, developing a model would be tougher.

GenAI Workflow

Now I can use natural language prompts, such as ‘fill missing values in my dataset’ to handle missing values. I can also generate preprocessing scripts quickly and fix data quality issues.

Task 2: Data Augmentation and Synthetic Data Generation

Traditional Workflow

Imagine I need to make a fraud detection model. Previously, I had to collect a huge amount of data on fraudulent transactions. But, collecting such data can be tedious and time-consuming. It also involves a lot of permissions and approvals from authorities as this data is highly sensitive.

GenAI Workflow

With GenAI tools, I can now generate realistic synthetic data for this situation. For example, generated data on fraudulent transactions will mimic actual distributions. I can also create variations of existing datasets and balance datasets without collecting real-world data through costly and tedious processes.

Task 3: Feature Engineering and Selection

Traditional Workflow

Extracting features from raw data is one of the most tedious tasks in the workflow. It requires a high level of domain expertise, as well as a lot of time and experimentation. So, I had to invest a notable amount of time and effort in feature engineering and selection earlier.

GenAI Workflow

Now I have automated tools to generate meaningful features from raw data. I can also use AI-powered selection techniques to identify the most impactful features. It helps me reduce dimensionality without losing important information. For example, I can extract time-series features for a predictive maintenance tool using Featuretools.

Task 4: Code Generation and Debugging

Traditional Workflow

Before generative AI, I had to write all my codes manually. The process involves writing codes for machine learning models, SQL queries, Python scripts, and more. These would take up a lot of my time. Moreover, writing code manually leads to a lot of unwanted errors. As a result, debugging would be much more difficult and time-consuming.

GenAI Workflow

Now I have multiple tools to use for code generation and debugging. Instead of writing the code manually, I simply input a prompt, such as ‘write a SQL query to find the top 5 customers by revenue’. The tool gives me the necessary code without any error.

If I need to modify any part of the code, these tools help me with auto-complete features. I can also find errors in codes much easier than before.

Task 5: Model Optimization and Tuning

Traditional Workflow

The success of a model greatly depends on fine-tuning its hyperparameters. Earlier, I had to tune the model manually to find the best hyperparameters. But, the process was slow and inefficient. Grid Search and Random Search would take a long time. So, the development lifecycle was much longer.

GenAI Workflow

I don’t have to manually tune the model now because GenAI tools can optimize it much faster. These tools find the best hyperparameters automatically and efficiently search for the best model configurations. They also visualize results instantly to identify patterns in model performance.

Task 6: Extracting Insights and Reports

Traditional Workflow

Be it model performance or any other technical data, I would face a lot of challenges in communicating data with non-technical stakeholders. For them, I had to make reports manually. It would consume a notable share of my workflow.

GenAI Workflow

Now I can generate data insights and reports in just a few clicks with almost no manual labor. I can generate automated summaries of data trends and patterns, easy-to-digest reports, etc., in just minutes. It saves a lot of my time that I can use in the complex tasks of my workflow.

Essential GenAI Tools for Data Scientists

Many specialized tools have now come to the market to streamline the workflow of a data scientist. I use the following tools frequently and want to give you a quick overview of their use cases. Check it out.

Data Preprocessing and Cleaning Tools

• Pandas AI: With AI-based automation, it is commonly used for data wrangling and transformation.
• Trifacta: This is a GenAI tool for data cleaning, preparation, and anomaly detection.
• Dataprep: I use this tool to understand data rapidly through exploratory data analysis.
• DataRobot AI: It is used for end-to-end machine learning automation.

Data Augmentation and Synthetic Data Generation Tools

• Gretel.ai: This AI-powered tool generates synthetic datasets for augmentation.
• Mostly AI: It is also used for synthetic data generation and balancing datasheets.
• YData Synthetic: This is the best tool for time-series generation.
• Microsoft Presidio: It is used for data anonymization and augmentation.

Feature Engineering and Selection Tools

• FeatureTools: It generates time-series and structured data.
• TSFresh: It extracts features from time-series data.
• AutoFeat: It selects the most impactful features from high-dimensional datasets.

Code Generation and Debugging Tools

• GitHub Copilot: It helps complete code for Python, SQL, and ML scripts.
• OpenAI Codex: It is used for general-purpose coding.
• Tabnine: The best predictive code generation tool I use.

Model Optimization Tools

• Optuna: I use it for tuning hyperparameters.
• Weights & Biases: It is used for experiment tracking and tuning.
• SigOpt: It is used for parameter tuning.

Data Visualization Tools

• Tableau AI: It can generate interactive dashboards.
• DataRobotAI: Automated predictive analysis is its most powerful feature.
• Narrative Science: It generates automated reports.

Challenges of Using GenAI as a Data Scientist

While GenAI transforms the workflow of a data scientist, it comes with its own challenges and limitations. Here are some of the most common challenges of using GenAI as a data scientist and how to overcome them.

1. GenAI models, especially large language models generate outputs based on probabilistic predictions. As a result, they can hallucinate, lack verifiability, and struggle with numerical precision. Cross-checking outputs with trusted sources and having human experts review the outputs can help overcome this challenge.
2. Due to a lack of explainability, GenAI models may generate biased outputs. This is why data scientists must perform bias audits continuously. Also, you should use ethically sourced datasets.
3. Blindly trusting GenAI tools can result in flawed outputs. Besides, data scientists can gradually lose human intuition, creativity, and domain expertise if they continue to rely on GenAI tools for even the smallest of tasks. To overcome this, data scientists must use GenAI tools as an assistant, not a decision-maker.
4. With a higher dependency on tools, data scientists may tend to perform tasks they don’t excel in. This can set a bad example for aspiring data scientists, especially for those who think someone can become a data scientist just by using tools.

Conclusion

Data scientists usually have a complex workflow that involves preprocessing data, extracting features, transforming raw data into structured data, and many more. They would do most of these tasks manually before GenAI emerged. But, now they commonly use an array of GenAI tools that have made the workflow much more efficient.

I talked about how GenAI transformed my work as a data scientist in this guide and explained what tools I use to boost my efficiency. However, you must remain careful so that GenAI tools don’t get dominant over yourself. Use tools to assist you but continue putting your creativity and human intuition into the process.

The post How GenAI Transformed My Work as a Data Scientist first appeared on Magnimind Academy.

Benford’s Law is an unusual law that exists in the principle in both data science and work in mathematics and forensic accounting. However, it turns out that this mathematical principle predicts pattern of such first digit distribution within many naturally occurring datasets and has turned out to be an extremely effective tool for detecting fraud and data integrity validation and anomaly detection. From tax returns to election results, Benford’s Law is held in use in many areas to detect irregularities in the data pattern. Based on these principles, this mathematical rule is about Benford’s Law that manifests peculiar first digit distribution patterns. The purpose of that essay is to examine several applications of the mathematical trick of the famous Benford’s Law and to show its consequences and limits.

Benford’s Law is a statistical rule that describes how the initial digits actually occur in data collections occurring in real world of data. smaller digits in particular 1 appear much more frequently rather than expected equal appearance patterns, which mean that data follows Benford’s Law. The first digit 1 occurs 30.1 % and the first digit 9 occurs only 4.6 %. Thousands of numerical datasets involving population data as well as river length information, stock figures, and various other scientific constants show a logarithmic first digit frequency pattern.

What makes Benford’s Law so important is that it can be universally applied with little effort. The logarithmical law is a law that applies to data with huge data ranges and is derived from processes of exponential development as well as multiplication. Its application in broad fields in which such patterns are found gives this law broad usefulness; namely in economics as in biology and physics. The analysis tool has the best capability for discovering both the fraudulent activities as well as manipulated data records. When human made numbers are introduced, there are also unanticipated biases that randomize the required Benford statistics.

Despite this, Benford’s Law is a useful tool for many situations and no place for it. There are certain restrictions under which Benford’s Law works perfectly well in use. The regime with which the law optimally functions is one where a dataset extends over many orders of magnitude. Because of this, Benford’s Law does not hold for human heights or shoe sizes, where working with small data sets or data ranges of interest fails. Even if deviations from the expected frequency patterns, by themselves, cannot be proven to be a fraud since they can be due simply to natural dataset uniqueness or external data influences.

Benford’s Law is also one which shares equal importance between human tendencies and mathematical explanations. The mathematical law states that there exists a tendency in nature to keep to the ordered patterns, that despite the fact that humans frequently disturb these patterns. First, Benfords Law generates two essential characteristics that allow for the Benfords Law to be utilized in scientific analysis and investigative auditing as it helps reveal unobservable relationships ofdata. To detect financial crime, to verify authenticity of research and where elections outcomes are in question, Benford’s law provides an advantageous tool for the specialists to use numerical analysis in its unique way, which helps to uncover hidden truths.

The need to discover effective number analysis methods to analyze increasing relevance of big data makes Benford’s Law a very important tool. In this data driven era, we first have fundamental requirement of data accuracy to which is in turn determined the worldwide decision. Benford’s Law, which states that the patterns within seemingly unordered numbers exist, is used to lead the truth seekers to find the real information and expose fraudulent activities in the world. We start our pathway of understanding Benford’s law mathematical structure but seeing its practical use in unveiling concealed information.

What is Benford’s Law?

Benford’s Law, also known as the First-Digit Law, states that in many naturally occurring collections of numbers, the leading digit is more likely to be small. Specifically, the probability that the first digit dd (where dd ranges from 1 to 9) appears as the leading digit is given by:

Data shows the appearance rate of 1 at the beginning position exceeds 9 by about 26 times during the set period. The logarithmic distribution pattern appears in datasets covering ranges from one to several orders of magnitude for populations and financial records and river measures. The widespread application of Benford’s Law serves to detect anomalies and uncover fraud and validate data integrity because human-made numbers deviate from its natural distribution format. The analysis tool finds applications in forensic accounting and election analysis because it helps experts find hidden secrets within data collections.

This means that the digit 1 appears as the first digit about 30.1% of the time, while the digit 9 appears as the first digit only about 4.6% of the time. The distribution of first digits according to Benford’s Law is as follows:

However, first glance at this distribution appears counterintuitive. So that in theory, it should be that each digit from 1 to 9 would have an equal probability to be out first. However, as Benford’s law indicates a natural bias towards smaller digits, and that pattern is found in so many of the real-world datasets, I do not find it appropriate to conclude that something must be going on.

The History of Benford’s Law

Despite being named after physicist Frank Benford, who popularized it in 1938, the phenomenon was first observed by astronomer Simon Newcomb in 1881. At the time that such use was done, logarithm tables were used to make calculations and Newcomb noticed that the pages were more worn for numbers beginning with 1 than for numbers beginning with 9. He stated that there seemed to be more numbers with lower first digits used in calculations.

Newcomb later took this observation further, expanding it on more than 20,000 numbers from many sources including river lengths, population counts, and physical constants. He then found that the first digits of these numbers always followed the distribution of Benford’s Law (logarithmic distribution).

Why Does Benford’s Law Work?

The underlying reason for Benford’s Law lies in the concept of scale invariance and the logarithmic nature of many natural phenomena. Here’s a simplified explanation:

• A dataset containing orders of magnitude is required. For instance, think of the populations of cities to which the numbers of a few thousand to a few million apply. As numbers are spread over such a wide range, it goes without saying that smaller digits will show up more often as leading digits.
• The log nature of Benford’s Law is a consequence of what the numbers grow exponentially. Smaller digits dominate towards the end of the scale in an exponential sequence, while larger digits only become more common the larger the numbers are.
• A lot of natural processes do involve multiplication or percentage growth (e.g. stock prices or bacterial growth). Because these processes tend to follow Benford’s Law by creating a logarithmic distribution of first digits, these processes will tend to produce numbers.

Applications of Benford’s Law

Benford’s Law serves multiple practical applications which extend between financial domains and forensic disciplines. These are the main applications of Benford’s Law:

1. Fraud Detection

Benford’s Law is a foremost method in identifying financial fraud cases. Generally, it is rare for artificial data made out of artificial data made in contravention to natural processes to follow the distribution pattern of first digits because the artificial data was created by means of human intervention in deliberate acts. For example:

Benford’s Law is used to verify the tax declaration by authorities. Auditors compare actual data with the basis because the expected distribution of first digits of reported income or expenses is the basis for the expected distribution of the first digits of manipulations or fraudulent activities.

Accounting fraud examination techniques help financial statement auditors to detect irregularities in a company. Invariably businesses involved in financial data manipulation create figures that are counter to Benford’s Law.

2. Election Forensics

Benford’s Law gives scientists a statistical framework that helps spot voting irregularities in voting tallies. By looking into the vote count in particular regions of the 2009 Iranian presidential election, however, they noticed pronounced deviations from distribution according to Benford’s Law and concluded that voting results had been manipulated.

3. Scientific Data Validation

Benford’s Law allows scientists to have an authentic method to check the accuracy of their research datasets. If a given distribution pattern of data is not matched, there is a failure probably due to problems during data acquisition or processing.

4. Economic and Financial Analysis

Benford’s Law is applied by economists and financial analysts to evaluate macroeconomic statistics such as GDP measurements and stock cost data, and inflation numbers. If the data does not pass exactly by the expected distribution, signals of manipulation, or any potential anomalies, can arise.

5. Forensic Science

Also used by law enforcement agencies to examine a crime report, forensic investigators also use it to interpret bits of DNA and for river length assessment. The law mentions some sequences that are believed to suggest evidence alteration as well as data mistakes.

Limitations of Benford’s Law

Although using Benford’s Law has power, it doesn’t always work in all cases. Benford’s Law is not valid proper for proper application of under some conditions.

1. It is said that Benford’s Law applies when the dataset contains multiple orders of magnitude and has full freedom on natural distribution. For data of narrow range like human heights and shoe sizes, the distribution patterns remain consistent, and as per the law, these do not fall under the purview of the law.
2. Having substantial datasets is the key to the effectiveness of using Benford’s Law. In random errors within small datasets, which are inherently small, wrong outcomes cannot be expected, giving small datasets poor distribution patterns.
3. According to Benford’s Law, the distribution patterns of human numbers which come from human activities should be regular anomalies. Also, rounding techniques are human tendency and the human shows preference for some specific digits.
4. Benford’s law deviations certainly do not necessarily indicate fraudulent or erroneous activities. In addition, valid explanations such as original data properties as well as external circumstances may also produce deviations from the data.

How to Apply Benford’s Law

Some steps for proper application of Benford’s Law are:

1. Then we use the data collection method to get our analytical dataset. Free spaces should be provided for various orders of magnitude of analyzed data, while being free from artificially restricted ranges.
2. We have to apply the initial non zero digit extraction to all the numbers of which we have the dataset.
3. Suppose observed frequency count for digits from 1 to 9 when they come out in first positions.
4. Run the tests to check if observed first digit frequencies match Benford’s Law predicted values.
5. It monitors Measure Deviations to find any large difference between the forecasted statistical pattern and actual data results. As a statistical tool, you should carry out the chi-squared test to find out statistically significant deviations between the actual and predicted data patterns.
6. After the discovery of significant deviations, the investigation team should examine irregularities to see what their root causes are. In case significant deviations appear additional analysis through auditing or forensic examination needs to be performed.

Real-World Examples of Benford’s Law in Action

1. Enron Scandal

Benford’s Law was used in the analysis of Enron financial statements during the scandal investigation in order to identify possible fraudulent activities. The fact that financial statements were exhibiting accounting fraud was confirmed by the Benford’s Law deviations in first digit distributions.

2. Greek Economic Crisis

On the other hand, Benford’s Law was applied to investigate Greek macroeconomic data during the Greek economic crisis. They found large deviations from what they expected in the distribution which proved EU deficit targets resulted in data manipulation.

3. COVID-19 Data

Benford’s Law was applied to the reported case numbers from various countries in the COVID-19 pandemic. Some analysts who applied the law data found signs of underreporting or intentional tampering.

Conclusion

Benford’s Law is a mathematical discovery used to make people view surprising structural patterns within naturally developing datasets. The Benford’s Law serves as a very useful forensic tool to discover unsuspected fraudulent activities and to discover irregular data patterns in financial and a medical investigations. When applying Benford’s Law, one needs to exercise caution because Benford’s Law has its limitations with respect to each dataset that is going to be analyzed.

It will ensure the fundamental relevance of Benford’s Law tools to the integrity of data as widespread as possible in the modern life and divination of the underlying numerical realty. This special way of analysis gives the reading to Benford’s Law through which each data scientist, auditor and others will get an insight into numerical stories through the numbers.

References

1. Barabesi, L., Cerioli, A., & Perrotta, D. (2021). Forum on Benford’s law and statistical methods for the detection of frauds. Statistical Methods & Applications, 30, 767-778.
2. Etim, E. O., Daferighe, E. E., Inyang, A. B., & Ekikor, M. E. (2021). application of benford’s law and the detection of accounting data fraud in nigeria.
3. Goodman, W. M. (2023). Applying and Testing Benford’s Law Are Not the Same. Spanish journal of statistics, (5), 43-53.

The post Benford’s Law: The Math Trick That Detects Fraud first appeared on Magnimind Academy.

Recognize the Fundamentals of Data Science

The programming languages Python and R, which are frequently used in data science, should also be familiar to you.

Discover Statistics

You need to comprehend the fundamentals in order to learn data science. Data science requires statistics to function. It offers the methods and tools needed to analyze data and make predictions. The fundamentals of statistics, such as probability theory, statistical inference, and hypothesis testing, should be studied. When studying statistics, make sure that you use statistical software like R or Python.

Become an Expert in Python

Python is one of the programming languages that are most frequently used in data science. It has a huge ecosystem of libraries and tools, is adaptable, and is simple to learn. The foundational concepts of Python, such as data types, control flow, and functions, should be studied. Also, you want to become familiar with using Python libraries used frequently in data science, like NumPy, Pandas, Matplotlib, and Scikit-Learn. Python will be your best friend in achieving a variety of essential steps in data analysis including data collection, data cleaning, data analysis, and data visualization.

Discover the Power of SQL

In addition to Python for data cleaning, you should also become familiar with working with databases and information storage platforms including SQL and NoSQL databases. Relational databases are everywhere. SQL will be an important asset for you when you are in the job market.

Take a course in data science

A great option to learn data science from scratch is to enroll in a data science course. There are many learning platforms that offer data science courses. There are many online courses for various topics, including programming, statistics, and machine learning. Most people begin learning from these online platforms but give up along the way. Make sure you join a forum, a group of data science enthusiasts supporting each other, or join a synchronous course that provides some form of coaching.

To sum up, studying data science from the start involves commitment, perseverance, and hard work. You must learn the fundamentals of data science with statistics, Python, SQL, and enroll in a data science course. You will be able to examine data, draw conclusions, and make well-informed judgments that can change businesses and sectors if you have these skills.

.  .  .
To learn more about variance and bias, click here and read our another article.

The post How To Learn Data Science From Scratch? first appeared on Magnimind Academy.

Supervised Learning

Supervised Learning

Learning from Labeled Data is an aspect of supervised learning. The machine learning model learns to predict the output based on the input after the correct output is labeled on the input data. A spam email filter, for instance, is first trained on a group of emails where both text and the label of the emails are provided. After the training, the filter takes the text of an email as its input and determines whether or not it is spam.

The steps of supervised learning are as follows:

Collection of data: Gather data with labels that include both the input and the output.

Preprocessing of data: Preprocess the data and clean it up.

Choosing a model: Select a suitable machine learning model for the issue.

Model training: Use the labeled data to teach the machine learning model.

Evaluation of a model: Analyze the machine learning model’s performance on a test set.

Model deployment: Apply the model to new data to make predictions.

Linear regression, logistic regression, decision trees, random forests, and neural networks are all common supervised learning algorithms.

Unsupervised Learning

Unsupervised Learning

With unsupervised learning, the data come without any labels. The machine learning model learns to recognize patterns and structure in the data without the input data being labeled with the correct output. In customer segmentation, for instance, the model learns to group customers according to their behavior using the input data. When training this model, the dataset does not include the segments of each customer.

The steps that make up unsupervised learning are as follows:

Collection of data: Gather unlabeled data consisting solely of the input.

Preprocessing of data: Preprocess the data and clean it up.

Choosing a model: Select a problem-appropriate unsupervised learning model.

Model training: Use the unlabeled data to teach the unsupervised learning model.

Evaluation of a model: Make use of your domain expertise to evaluate the effectiveness of the unsupervised learning model.

Model deployment: Utilize the model to discover structure and patterns in brand-new data.

Clustering, principal component analysis (PCA), and association rule mining are a few common unsupervised learning algorithms.

Supervised vs. Unsupervised Learning

When to Use Supervised vs. Unsupervised Learning

When the problem has labeled data and clear input and output, supervised learning is used.

Image recognition, natural language processing, and stock price prediction all make use of classification and regression.

When unlabeled data are available and the problem lacks clear input and output, unsupervised learning is utilized.

Customer segmentation, anomaly detection, and exploratory data analysis all make use of them frequently. Practitioners of machine learning can select the appropriate approach for their particular problem and maximize the performance of their models by comprehending the distinctions between these two approaches.

The post Supervised Vs. Unsupervised Learning: Understanding The Differences first appeared on Magnimind Academy.

Linear Regression

Linear Regression

One of the simplest and most widely used machine learning algorithms is linear regression. It can be used to model the relationship between a dependent variable and one or more independent variables and is used for predictive modeling. Finding the best line of fit that minimizes the sum of squared differences between the predicted and actual values is the objective of linear regression.

Logistic Regression

Logistic regression is a variant of linear regression that is used for binary classification problems. Based on one or more predictor variables, it is used to model the probability of a binary response variable. Marketing, finance, and medical diagnosis all make extensive use of logistic regression.

Decision Trees

Decision Trees

Machine learning algorithms known as decision trees are utilized for both classification and regression problems. Based on the values of the features, they divide the data in a recursive manner into smaller subsets. The objective is to develop a tree-like model that can be used to predict features’ values.

Random Forest

An extension of decision trees, a random forest makes use of an ensemble of trees to make predictions. A subset of the features for each tree is chosen at random, and the predictions from all of the trees are combined to make a final prediction. Random forests are utilized extensively in fields like natural language processing and computer vision due to their high accuracy and stability.

Support Vector Machines (SVM)

Support Vector Machines (SVM)

Support Vector Machines (SVM) are a type of machine learning algorithm used to solve classification and regression issues. They function by locating the ideal hyperplane or boundary that divides the data into distinct classes. SVM is widely used in bioinformatics and text classification, and it is particularly useful for solving complex non-linear problems.

K-Nearest Neighbors (KNN)

K-Nearest Neighbors (KNN) is a straightforward and efficient machine learning algorithm for regression and classification problems. It works by making a prediction based on the labels or values of the k closest neighbors to a given test example. In fields like image classification and recommendation systems, KNN is frequently used.

Naive Bayes

Classification problems are handled by the probabilistic machine learning algorithm known as Naive Bayes. It works by modeling the probability of a class based on the values of its features using Bayes’ theorem. In fields like spam filtering and text classification, Naive Bayes is widely used.

Neural Networks

Neural Networks

Machine Learning Algorithms Inspired by the Human Brain Neural networks are a type of machine learning algorithm. They are widely used for image classification, natural language processing, and speech recognition, among other things. Each layer of interconnected nodes in a neural network carries out a straightforward computation.

Convolutional Neural Networks (CNN)

Convolutional neural networks are a kind of neural network that are made to solve problems with image classification. Predictions are made using a fully connected layer after the image is convolved using multiple filters to extract features. CNNs have achieved state-of-the-art results on many images.

The post All Machine Learning Algorithms You Should Know In 2023 first appeared on Magnimind Academy.

A subfield of artificial intelligence (AI) that focuses on the creation of algorithms and statistical models that enable computers to carry out activities that typically call for human intelligence is known as machine learning. Prediction, pattern recognition, and decision-making are some of these tasks. Algorithms for machine learning make predictions based on historical data and identify patterns in data using mathematical and statistical models.

Machine Learning

In contrast, deep learning is a subfield of machine learning that draws inspiration from the human brain’s structure and operation. Using artificial neural networks to process and analyze large amounts of data, deep learning algorithms attempt to imitate the human brain’s functions. These networks are made up of multiple layers of nodes that are connected to one another. Each layer takes information and sends it to the next layer.

The way they solve problems is one of the main differences between machine learning and deep learning.

Deep learning algorithms are designed to analyze and learn from data in a manner that mimics the way the human brain processes information, whereas machine learning algorithms are designed to analyze data and make predictions based on statistical models.

Deep learning may extract its own features from the data whereas machine learning requires features to be given in terms of data.

Deep Learning

The kind of data they are best suited to process is another important difference between the two.

Deep learning algorithms are better suited for unstructured data like images, videos, and audio, whereas machine learning algorithms are typically used for structured data like numerical or categorical data.

This is due to the fact that deep learning algorithms are able to identify patterns in intricate data that traditional machine learning algorithms have trouble capturing.

The model’s utilized level of complexity is another significant distinction. Deep learning algorithms employ much more complex models, such as artificial neural networks, whereas machine learning algorithms typically employ relatively straightforward models, such as decision trees or linear regression. Deep learning algorithms can now handle a lot of data and make better predictions thanks to this.

Conclusion

In conclusion, although machine learning and deep learning are both potent subfields of artificial intelligence, their methods, data types, and model complexity all differ. For businesses and organizations to select the AI-based solution that is most suitable for their particular requirements, it is essential to comprehend these distinctions. Deep learning and machine learning both have the potential to significantly alter our lives and revolutionize a variety of industries.

The post Machine Learning Vs. Deep Learning: What Is The Difference? first appeared on Magnimind Academy.

One of the key benefits of cloud security is scalability. Cloud service providers allow users to easily scale up or down their security resources as per the requirement, thus making it easy to manage changing security needs.

Another advantage is flexibility. Cloud security solutions can be customized to meet the specific security needs of an organization, making it possible to adjust security measures according to changing business requirements.

Cost-effectiveness is also a key advantage of cloud security. It eliminates the need to invest in expensive hardware, software, and infrastructure, thus reducing costs and improving efficiency.

Accessibility is another benefit of cloud security. With cloud computing, employees can access company data and applications from anywhere, at any time, providing greater convenience and enabling remote work.

However, cloud security also has some limitations that need to be considered. One of the biggest challenges is ensuring the privacy and security of sensitive data. Data breaches and cyberattacks are becoming increasingly common, and organizations need to take the necessary steps to protect their data.

Another limitation is the risk of vendor lock-in. Organizations may become dependent on a single cloud service provider, which can result in a lack of flexibility and higher costs if they need to switch to a different provider.

In conclusion, cloud security offers several benefits such as scalability, flexibility, cost-effectiveness, and accessibility. However, organizations need to be aware of the limitations, such as privacy and security concerns and vendor lock-in and take the necessary measures to mitigate these risks.

.  .  .
To learn more about variance and bias, click here and read our another article.

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Usually, knowing what values you should use for the hyperparameters of a specific algorithm on a given dataset is challenging. That’s why you need to explore various strategies to tune hyperparameter values.

With hyperparameter tuning, you can determine the right mix of hyperparameters that would maximize the performance of your model.

Hyperparameter tuning

The two best strategies in use for hyperparameter tuning are:

1. GridSearch

It involves creating a grid of probable values for hyperparameters. Every iteration tries a set of hyperparameters in a particular order from the grid of probable hyperparameter values. The GridSearch strategy will build several versions of the model with all probable combinations of hyperparameters, and return the one with the best performance.

Since GridSearch goes through all the intermediate sets of hyperparameters, it’s an extremely expensive strategy computationally.

2. RandomizedSearch

It also involves building a grid of probable values for hyperparameters but here, every iteration tries a random set of hyperparameters from the grid, documents the performance, and finally, returns the set of hyperparameters that provided the best performance.

As RandomizedSearch moves through a fixed number of hyperparameter settings, it decreases unnecessary computations and the associated costs, thus offering a solution to overcome the drawbacks of GridSearch.

Selecting the hyperparameters to tune

The more hyperparameters of an algorithm you want to tune, the slower would be the tuning process. This makes it important to choose a minimum subset of hyperparameters to search or tune. But not all hyperparameters are equally important. Also, you’ll find little universal advice on how to select the hyperparameters that you should tune.

Having experience with the machine learning technique you’re using could give you useful insights into the behavior of its hyperparameters, which could make your choice a bit easier. You may even turn to machine learning communities to seek advice. But whatever your choice is, you should realize the implications.

Each hyperparameter that you select to tune will have the possibility of increasing the number of trials necessary for completing the tuning task successfully. And when you use an AI Platform Training to train your model, you’ll be charged for the task’s duration, which means choosing the hyperparameters to tune carefully would decrease both the time and training cost of your model.

Final words

For a good start with hyperparameter tuning models, you can go with scikit-learn though there are better options too for hyperparameter tuning and optimization, such as Hyperopt, Optuna, Scikit-Optimize, and Ray-Tune, to name a few.

.  .  .
To learn more about variance and bias, click here and read our another article.

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Finance

1. Financial Risk Management and Risk Analysis

For a company, there’re different financial risk factors like credits, market volatility, competitors, etc. For financial risk management, the first step is to identify the threat, followed by monitoring and prioritizing the risk. Several companies depend on data scientists to analyze their customers’ creditworthiness. This is done with the use of machine learning algorithms to evaluate the customers’ transactions. Again, if the risk of a finance company is associated with stock prices and sales volume, time series analysis where variables are usually plotted against time could be helpful.

2. Financial Fraud Detection

By analyzing big data with the use of analytical tools, financial institutions can detect anomalies or unusual trading patterns and receive real-time detection alerts to investigate such instances further. This would help in keeping track of frauds and scams.

3. Predictive Analytics

For a finance company, predictive analytics are crucial as they disclose data patterns to foresee future events that can be acted upon right now. Data science can use sophisticated analytics and help in making predictions based on data from news trends, social media, and other data sources. Thus, with predictive analytics, a finance company can predict prices, future life events, customers’ LTV (lifetime value), stock market moves, and much more, all of which will let it decide and strategize the best way to intervene.

4. Personalized Services

NLP (natural language processing), machine learning, and speech recognition-based software can analyze customer information and produce insights about their interactions. For instance, an AI-powered solution can process an individual’s basic information that he has specified in a questionnaire in addition to gathering data about his online behavior on a financial company’s website, his historical transactions, and his feedback, likes, comments, etc. on the company’s social media pages. All these would help the company optimize and customize its offerings to serve the individual (i.e. the customer) better.

Healthcare

1. Medical Image Analysis

With the use of machine learning and deep learning algorithms, image recognition with SVMs (Support Vector Machines), and MapReduce in Hadoop, to name a few, it has become possible to find microscopic deformities in medical images and even enhance or reconstruct such images.

2. Genomics

By using advanced data science tools like SQL, Bioconductor, MapReduce, Galaxy, etc., it has now become possible to examine and derive insights from the human gene much more quickly and in a more cost-effective way.

3. Predictive Analytics

A predictive model in health care uses historical data to learn from it and discover patterns to produce accurate predictions. Thus, with data science, you can find correlations between diseases, habits, and symptoms to improve patient care and disease management. Predictions of a patient’s health deterioration can also help in taking timely preventive measures, while predictions about a demand surge can facilitate adequate medical supply to healthcare facilities.

.  .  .
To learn more about variance and bias, click here and read our another article.

The post How To Makes Use Of Domain Knowledge In Data Science: Examples From Finance And Health Care first appeared on Magnimind Academy.

The 1950s — Early Days of AI

The 1950s — Early Days of AI

It all started in 1950 with Alan Turing. He was a young British polymath, who examined the mathematical prospect of AI. It was he who suggested that just like humans, machines too could use available information and reasoning to make decisions and solve problems. In his paper on creating thinking machines, titled ‘Computing Machinery and Intelligence’ in 1950, this was the logical framework based on which Turing discussed how intelligent machines can be built and their intelligence tested.

But why couldn’t Turing start work on his concepts right away? The problem was with the computers available in those days. They needed to change to facilitate such work. Prior to 1949, a precondition for intelligence was lacking in computers — they were unable to store commands; they could just execute the commands given to them. To put it differently, computers in those days could be told what to perform but they couldn’t remember what they executed. Additionally, computing was exceptionally pricey. Leasing a computer in the early 1950s would set you back by a whopping monthly amount of $200,000. Thus, testing this unfamiliar and uncertain field was affordable only for big technology companies and prestigious universities. Under such circumstances, anyone wishing to pursue AI would have needed proof of concept together with the backing of high-profile people to persuade the funding sources into investing in this endeavor.

The Conference Where It All Began

The Conference Where It All Began

It took five more years for the proof of concept to be initialized by Herbert Simon, Cliff Shaw, and Allen Newell’s program — the Logic Theorist. Funded by the RAND (Research and Development) Corporation, Logic Theorist was created to imitate a human’s problem-solving skills. Many consider it to be the first AI program, which was presented at the DSRPAI (Dartmouth Summer Research Project on Artificial Intelligence) in 1956, which was hosted by Marvin Minsky (an MIT cognitive scientist) and John McCarthy (a prominent cognitive scientist and computer scientist). It was at this conference that McCarthy coined the term ‘artificial intelligence’ and presented his thoughts in an open-ended discussion on AI by bringing together some of the top researchers from different fields.

Though McCarthy envisioned a great collaborative effort, the conference failed to meet his expectations. People attended and left the conference as they pleased, and a consensus couldn’t be reached on the standard methods that the field should use. But despite this setback, everyone enthusiastically agreed that AI was attainable. This conference was a significant milestone in the history of AI because it prompted the subsequent twenty years of AI research.

The Golden Years of AI

The Golden Years of AI

As computers became more accessible and cheaper and were able to work faster and store more information, machine learning algorithms too improved. This helped people become better at knowing which algorithm would be apt to apply in order to solve their problems. Early demonstrations like the General Problem Solver (GPS) by Newell and Simon, whose first version ran in 1957 (though work on the project continued for almost a decade), could use a trial and error method to solve a remarkable range of puzzles. But the GPS lacked any learning ability, as its intelligence was totally second-hand, and came from whatever information was explicitly included by the programmer.

In the mid-1960s, Joseph Weizenbaum created ELIZA at the MIT Artificial Intelligence Laboratory. ELIZA was a computer program designed for natural language processing between man and machine (or computers, to be specific). These successes, together with the backing of leading researchers (specifically, the DSRPAI attendees), persuaded government agencies like the DARPA (Defense Advanced Research Projects Agency) to fund AI research at numerous institutions.

It’s important to note the government’s interest was predominantly in machines that were capable of high throughput data processing as well as translating and transcribing the spoken language. There was a high degree of optimism about the future of AI but the expectations were even higher.

The First AI Winter and Subsequent Revival

The First AI Winter and Subsequent Revival

It started in the early 1970s when public interest in AI declined and research funding for AI was cut after the promises made by the field’s leading scientists didn’t materialize. More than a few reports criticized a lack of progress in this field. The first AI winter continued from 1974–80.

In the 1980s, AI research resumed when the British and U.S. governments started funding it again to compete with Japan’s efforts of becoming the global leader in computer technology with its Fifth Generation Computer Project (FGCP). By then, Japan had already built WABOT-1 (in 1972) — an intelligent humanoid robot.

AI also got a boost in the 1980s from two sources. One was attributed to David Rumelhart and John Hopfield, who popularized “deep learning” techniques that let computers learn from experience. The other was Edward Feigenbaum, who pioneered expert systems that imitated a human expert’s decision-making process.

It was in the 1980s when XCON — an Expert System of DEC, was put to use. XCON used AI techniques to solve real-world problems. By 1985, global corporations had started using Expert Systems.

The Second AI Winter

The Second AI Winter

From 1987 to 1993, the field experienced another major setback in the form of a second AI winter, which was triggered by reduced government funding and the market collapse for a few of the early general-purpose computers.

The 1990s and 2000s

The 1990s and 2000s

Several landmark goals of AI were achieved during this period. In 1997, IBM’s Deep Blue (a chess-playing computer system) defeated grandmaster Gary Kasparov, who was then the reigning world chess champion. This was a huge step forward for an AI-driven decision-making program. The same year saw the implementation of Dragon Systems’ speech recognition software on Windows. In the late 1990s, the development of Kismet by Dr. Cynthia Breazeal in the AI department of MIT was another major achievement as this artificial humanoid could recognize and exhibit emotions.

In 2002, AI entered the homes in the form of Roomba (launched by iRobot), the first robot vacuum cleaner that was commercially successful. In 2004, NASA’s two robotic geologists named Opportunity and Spirit navigated the Martian surface without human intervention. In 2009, Google began work (secretly) on developing its self-driving technology and testing its self-driven cars (which later passed Nevada’s self-driving test in 2014).

2010 to Present Day

2010 to Present Day

AI has developed by leaps and bounds to become embedded in our daily existence. In 2011, Watson — IBM’s natural language question-answering system, won the quiz show Jeopardy! by defeating two former champions, Brad Rutter and Ken Jennings. The same year, Eugene Goostman — the talking computer ‘chatbot’ captured headlines as it tricked judges during a Turing test into thinking he was human.

In 2011, Apple released Siri, a virtual assistant that NLP (natural language processing) enabled, to infer, study, answer, and suggest things to its human user while customizing the experience for every user. This was followed by similar versions of other companies in 2014 — Microsoft’s Cortana and Amazon’s Alexa.

Some other pioneering developments in the field of AI during this period were:

• Sophia — the first robot citizen (created by Hanson Robotics in 2016), which can make facial expressions, see (via image recognition), and talk via AI.
• In 2017, Facebook designed two chatbots to engage in start-to-finish negotiations with each other by using machine learning to continuously improve their negotiating tactics. But as they conversed, these chatbots diverged from human language and invented their own language to communicate, thus displaying AI to a great extent.
• In 2018, Google developed BERT, which uses transfer learning to handle a wide range of natural language tasks.

Wrapping up

Wrapping up

Today, we live in the age of big data where the rapid speed of data generation and unlimited sources facilitating data availability coupled with the massive computing power of machines, AI, and deep learning technologies have found successful applications in various domains. From banking, technology, and healthcare to marketing and entertainment, AI has achieved what once seemed to be inconceivable. The future of AI is bright as it’s poised to steadily improve further and significantly change how we live and work.

The post A Brief History Of AI first appeared on Magnimind Academy.