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Data Science News
Medium
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How artificial intelligence is transforming the world
- Artificial intelligence (AI) is transforming every walk of life by enabling people to rethink how we integrate information, analyze data, and improve decision making.
- Despite its lack of familiarity, AI is already altering the world and raises important questions for society, economy, and governance.
- This paper discusses novel applications of AI in various sectors and addresses issues such as data access problems, algorithmic bias, and legal liability.
- To maximize AI benefits, recommendations include encouraging data access, investing in AI research, promoting digital education, creating advisory committees, regulating broad AI principles, addressing bias complaints, maintaining human oversight, and promoting cybersecurity.
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Medium
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Statistics in Python:Understanding Measures of Spread in Python
- Variance measures the average squared deviation of data points from the mean.
- Standard deviation is the square root of the variance. It measures the average deviation of data points from the mean.
- Mean Absolute Deviation calculates the average absolute deviation of data points from the mean.
- Quantiles divide the dataset into equal parts. IQR is the range between the first and third quartiles.
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Medium
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this Unlock the hidden patterns and goldmine of insights buried within your data!
- Exploratory Data Analysis (EDA) serves as the cornerstone of understanding and gaining insights from raw data.
- EDA is an initial phase of data analysis employing statistical graphics and data visualization methods to identify structure and patterns within the data, anomalies, and formulating hypotheses for further analysis.
- EDA plays a crucial role in identifying trends, outliers, and relationships between variables, guiding modeling and analysis, leading to informed decision-making in various domains.
- EDA employs a plethora of techniques, including univariate, bivariate, multivariate analysis, correlation, regression, and more.
- Data preprocessing involves cleaning, transforming, and organizing data to make it suitable for analysis. Its primary purposes include identifying and handling missing values, outliers, and noise, detecting and correcting inconsistencies and alleviating biases.
- Data visualization facilitates the exploration and interpretation of complex datasets by presenting information in a visual format, enhancing understanding, aiding in pattern recognition, and enabling effective communication of insights to stakeholders.
- Auto EDA utilizes automated tools to streamline the exploratory data analysis process through the generation of descriptive statistics, visualizations, and insights without manual intervention.
- In-depth Exploration of EDA section gives practical case studies showcasing EDA's application in diverse domains, with best practices and tips for conducting effective EDA.
- Popular Data Visualization tools include Matplotlib, Seaborn, Plotly, etc., whereas Auto EDA tools include Sweetviz, AutoViz, D-Tale, Pandas Profiling, and Dataprep.
- EDA is crucial for data-driven decision-making, as it empowers analysts to extract valuable insights to drive informed decision-making in various domains.
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Medium
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A Leap of Faith: Machine Learning without Mastering Statistics
- AI-FOMO, fear of missing out, is rampant as companies embark on AI projects without understanding statistics.
- The video discusses the importance of mastering statistical principles and understanding data for AI projects to add business value.
- Tips for discussions with consultants and vendors and conducting different types of analysis are shared.
- Creating a healthy data culture and good habits are highlighted for successful AI and analytics projects.
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Analyticsindiamag
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Kunal Shah Says ‘GPT Makes Him 10x More Efficient in Sharing Ideas with the Team’
- Kunal Shah, founder and CEO of Cred, highlights how AI, specifically ChatGPT, helps him in sharing ideas with his team.
- AI is revolutionizing various industries, including healthcare, where it improves speed and accuracy for radiologists.
- Shah mentions how companies use AI skills tests for prospective employees, where completing assignments would be impossible without AI assistance.
- Expressive AI enables Shah to better explain visual ideas to his team, making him 10x more efficient in the process.
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Analyticsindiamag
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Why Ollama is Good for Running LLMs on Computer
- Ollama is an open-source tool that enables users to run large language models (LLMs) on their local computers.
- It offers a user-friendly interface and compatibility with popular models like LLaMA 2 and Mistral.
- Ollama is efficient, cost-effective, and allows users to harness the power of AI models without relying on cloud services or expensive hardware.
- While Ollama is the fastest solution to run LLMs locally on a terminal, it also provides a GUI option.
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Medium
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318
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Airplane Collisions in the U.S. Between 2001–2014
- Airplane collisions between 2001-2014 had a profound impact on civilians, causing loss of life, trauma, and disruption.
- Financially, such collisions strained state and federal resources, requiring significant funding for immediate response efforts, investigations, and infrastructure repair.
- Mitigating risks includes enforcing safety protocols, investing in advanced technology, strengthening communication, and implementing stricter regulations.
- Descriptive statistics reveal insights into airplane collisions, highlighting the economic burden and guiding strategies for prevention.
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Medium
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90
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A UFO Encounter in Papua New Guinea
- A group of witnesses, including Reverend Gill, reported a UFO sighting in Boianai, Papua New Guinea.
- They observed a strange object with four humanoid figures on its upper deck.
- Interaction between the witnesses and the figures took place, including mimicking gestures.
- Despite investigations, the incident remains unexplained and extraordinary.
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Towards Data Science
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Feature Engineering for Machine Learning
- Feature engineering is the process of creating meaningful new features that will maximize the performance of our model.
- Feature engineering can be divided into creating new features and processing them.
- Creating new features involves applying techniques such as aggregation, difference and ratio, age calculation, indicator or Boolean, and one hot encoding.
- Processing features is important to ensure that machine learning models fit the data as intended.
- Common processing steps include outlier treatment, missing value treatment, feature scaling, dimensionality reduction, and target variable transformation.
- Feature engineering is a dimension of machine learning that allows us to control the model’s performance to an exceptional degree.
- Machine learning is not just about asking the algorithm to figure out the patterns; it is about enabling the algorithm to do its job effectively by providing the kind of data it needs.
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Towards Data Science
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How to Evaluate Your Predictions
- Testing and benchmarking machine learning models by comparing their predictions on a test set, even after deployment, is of fundamental importance.
- When choosing a scoring measure, one should adhere to the idea of proper scoring rules, which require a score that is minimized at the thing we want to measure.
- To score a prediction, we need a function that is minimized (in expectation) when the prediction is the best thing we can do.
- We can consider different scoring measures, such as the mean squared error (MSE) for predicting the conditional mean, or the mean absolute error (MAE) for the conditional median.
- The MSE and the MAE are appropriate metrics for mean and median prediction respectively, as they take into account the distribution of the data.
- A scoring measure for estimating quantiles is the quantile score, which penalizes big differences between predictions and actual values.
- With the quantile score, we can also score the prediction intervals by averaging the scores for the alpha/2 and the 1-alpha/2 quantiles.
- The energy score is a scoring measure for evaluating the prediction of a distribution.
- The power of a score to predict a distribution might be limited in practice, and even a method that leads to a large improvement might only have a slightly smaller score.
- It is important to select the right measure to evaluate the predictions, as the wrong measure might lead to choosing and keeping the wrong model for our prediction task.
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Medium
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Hypothesis Testing: Two-Sample T-Test & Chi-Squared Test for TikTok
- TikTok is developing a machine learning model to classify videos based on claims or opinions.
- They are conducting Two-Sample T-Tests and Chi-Squared Tests to understand the relationship between engagement metrics and account verification status.
- The results show that not verified authors have significantly higher mean values for views, likes, comments, shares, and downloads.
- They are also building a Logistic Regression Model to understand the impact of engagement metrics on the likelihood of an account being verified.
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Medium
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Best Data Science Institute in India: Enroll Now for Upgrade your Career in Data Science |…
- Enrolling in online data science courses in India can improve career prospects.
- Advantages of learning data science include career advancement and problem-solving skills.
- Digicrome offers various data science courses for different skill levels.
- To enroll, visit their website and register online.
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Analyticsindiamag
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10 Free Courses to Build AI Agents in 2024
- AI Agents are the latest development in artificial intelligence. There are courses available that allow you to learn how to build next-gen consumer and enterprise agent workflows that can enhance your productivity and help solve complex problems.
- The top 10 free courses to help you learn how to build AI agents include Multi AI Agent Systems with crewAI, Building RAG Agents with LLMs, and Build Agents with GPT-4o.
- Another course is called Build AI Agents Smarter Than ChatGPT and it delves into the concept of building AI agents that surpass current models like ChatGPT. It introduces Agency Swarm, a new framework.
- The Right Way to Build AI Agents With crewAI is another course that offers the best practices for building AI agents with crewAI, focusing on a fully local setup. The tutorial includes practical coding demonstrations.
- One of the courses, Building Agentic RAG with LlamaIndex, will teach you to build a RAG agent with tool access for autonomous information retrieval. It will cover aspects such as tool use and multi-step reasoning with tool use.
- CampusX: Building AI Agents, teaches you to build intelligent agents capable of performing complex tasks and enhancing automation processes. By the end, you’ll explore how to implement AI agents to improve automation across various industries.
- Another course is The Complete Guide to Building AI Agents for Beginners, which helps you develop custom AI agent systems for companies of all sizes, from small firms to large corporations.
- These courses are offered by popular institutions like NVIDIA and DeepLearning.ai and are taught by experts in the field of artificial intelligence.
- By taking these courses, you will gain important skills and knowledge that will help you build cutting-edge AI agents that can enhance your productivity and help you solve complex problems.
- Following these courses' end, you will be able to design and deploy multi-agent architectures, drive significant progress in AI systems, and build your own fully functional social media marketing agency that will generate ad copy, create ad images with DALL-E 3, and reliably post them on Facebook.
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Towards Data Science
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I built a reusable dashboard for Read the Docs traffic analytics using Vizro
- This article explains how the author built a dashboard to visualize traffic data for documentation they maintain using Vizro.
- The author uses generative AI package Vizro-AI to build charts for the dashboard.
- The author supplies data and natural language instructions to Vizro-AI, which generates Python code to create the requested charts.
- Vizro-AI can be slower and more expensive because it calls OpenAI, but it simplifies the visualization process.
- The generated code and charts can be used in Vizro, which uses a configuration approach to specify custom dashboard layouts.
- In this example, charts were generated to track documentation traffic, and then built into a Vizro dashboard.
- With more effort, this dashboard can be further customized with filters, parameters, or separate navigable pages.
- The author had no front-end design skills and limited Python experience yet was able to build a professional-looking dashboard in about 50 lines of code.
- The dashboard is easily extensible and can be shared among colleagues, making it a useful tool for tracking documentation impact.
- This approach works equally well in a Python script and can be deployed for wider access.
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Medium
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Research on Hawkes Process for Machine Learning research part9
- Hawkes Process has been used to model Limit Order Book (LOB) dynamics in several ways.
- A novel methodology of using Compound Hawkes Process for the LOB is proposed, with each event having an order size sampled from a calibrated distribution.
- The process ensures that the spread always remains positive and the model parameters are conditioned on time of day to support empirical observations.
- An enhanced non-parametric method is used to calibrate the Hawkes kernels and allow for inhibitory cross-excitation kernels.
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