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Mastering Machine Learning Basics

Category Data Engineering

Model: A machine learning model can be a mathematical representation of a real-world process. To generate a machine learning model you will need to provide training data to a machine learning algorithm to learn from.

Algorithm: Machine Learning algorithm is the hypothesis set that is taken at the beginning before the training starts with real-world data. When we say Linear Regression algorithm, it means a set of functions that define similar characteristics as defined by Linear Regression and from those set of functions we will choose one function that fits the most by the training data.

Training: While training for machine learning, you pass an algorithm with training data. The learning algorithm finds patterns in the training data such that the input parameters correspond to the target. The output of the training process is a machine learning model which you can then use to make predictions. This process is also called “learning”.

Regression: Regression techniques are used when the output is real-valued based on continuous variables. For example, any time series data. This technique involves fitting a line.

Classification: In classification, you will need to categorize data into predefined classes. For example, an email can either be ‘spam’ or ‘not spam’.

 

Target: The target is whatever the output of the input variables. It could be the individual classes that the input variables maybe mapped to in case of a classification problem or the output value range in a regression problem. If the training set is considered then the target is the training output values that will be considered.

Feature: Features are individual independent variables that act as the input in your system. Prediction models use features to make predictions. New features can also be obtained from old features using a method known as ‘feature engineering’. More simply, you can consider one column of your data set to be one feature. Sometimes these are also called attributes. And the number of features are called dimensions.

Label: Labels are the final output. You can also consider the output classes to be the labels. When data scientists speak of labeled data, they mean groups of samples that have been tagged to one or more labels.

Overfitting: An important consideration in machine learning is how well the approximation of the target function that has been trained using training data, generalizes to new data. Generalization works best if the signal or the sample that is used as the training data has a high signal to noise ratio. If that is not the case, generalization would be poor and we will not get good predictions. A model is overfitting if it fits the training data too well and there is a poor generalization of new data.

Regularization: Regularization is the method to estimate a preferred complexity of the machine learning model so that the model generalizes and the over-fit/under-fit problem is avoided. This is done by adding a penalty on the different parameters of the model thereby reducing the freedom of the model.

Parameter and Hyper-Parameter: Parameters are configuration variables that can be thought to be internal to the model as they can be estimated from the training data. Algorithms have mechanisms to optimize parameters. On the other hand, hyperparameters cannot be estimated from the training data. Hyperparameters of a model are set and tuned depending on a combination of some heuristics and the experience and domain knowledge of the data scientist.

These are some of the machine learning basics that I thought are important and should be looked into for someone who is a machine learning beginner. In case you have encountered some common terms which are not included here, do write about them in the comments below.

Thanks for reading this. If you are interested in talking more on this, just drop me a message @alt227Joydeep. I would be glad to discuss this further. Also please hit the claps and help this article reach more audience.

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Navigating The Two Major Data Trends in 2024

As the data landscape continues to evolve rapidly, businesses are compelled to stay abreast of emerging trends to maintain competitiveness. In the year 2024, two prominent trends are poised to redefine data analytics: the proliferation of Generative AI and the adoption of modern data contracts. These trends not only reshape how organizations utilize data but also underscore the importance of ethical considerations and robust governance in data management. This article explores these trends in-depth, providing insights into effective strategies for implementation and the implications for businesses navigating the data landscape.Trend #1: The Ascendancy of Generative AIGenerative AI, characterized by its ability to create new content autonomously, has gained significant traction across industries. The advent of large language models (LLMs) has propelled Generative AI into the mainstream, with tech giants like Microsoft, Google, and Meta integrating Generative AI capabilities into their products. As businesses increasingly rely on AI-driven insights, Generative AI is poised to become an indispensable tool for enhancing productivity and driving innovation.Strategy for Effective Implementation:To leverage Generative AI effectively, businesses must develop a comprehensive strategy tailored to their specific needs and objectives. This strategy should encompass several key components:Identifying suitable use cases:Organizations should identify areas where Generative AI can augment existing processes and generate tangible value. 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Organizations must establish clear guidelines and protocols for data collection, storage, and usage to mitigate risks associated with data misuse or bias.Managing costs and licensing:While Generative AI offers immense potential, it also comes with significant costs, both in terms of technology investments and licensing fees. Organizations must develop a cost-effective strategy for scaling AI initiatives while ensuring compliance with budgetary constraints.Balancing automation and human judgment:While AI-driven insights can enhance decision-making processes, it’s essential to strike a balance between automation and human judgment. Human oversight is crucial for interpreting AI-generated insights, identifying biases, and ensuring ethical decision-making.Ethical considerations:As AI becomes increasingly integrated into business operations, organizations must prioritize ethical considerations and accountability. This includes addressing issues related to data privacy, algorithmic bias, and the potential societal impact of AI-driven decisions.Trend #2: Adoption of Modern Data ContractsModern data contracts have emerged as a solution to streamline data usage and sharing, effectively addressing the challenges associated with broken data integrations and communication gaps between application and analytics teams.Structured Data Interactions:Modern data contracts represent a paradigm shift in how organizations manage data interactions. Unlike traditional contracts, which are static and cumbersome to maintain, modern data contracts are dynamic agreements that evolve with changing data requirements and business needs.Integration into workflows:By integrating data contracts into existing workflows and development processes, organizations can ensure seamless data interactions across disparate systems and applications. This integration enables teams to collaborate more effectively, reducing friction and improving data quality and consistency.Implementation Strategies:Implementing modern data contracts requires a strategic approach focused on collaboration, standardization, and automation. Key strategies include:Developing clear standards:Organizations should establish clear standards and guidelines for data contracts, outlining key parameters such as data formats, schemas, and validation rules. These standards help ensure consistency and interoperability across data systems and applications.Instituting change controls:Change management processes are essential for managing versioning and ensuring smooth transitions between data contract iterations. By implementing robust change controls, organizations can minimize disruptions and maintain data integrity throughout the contract lifecycle.Training and tools:Equipping teams with the necessary training and tools is crucial for successful data contract implementation. Training programs should cover topics such as contract management, data governance, and compliance, while tools such as data modeling platforms and contract management software can streamline the contract development and deployment process.As businesses navigate the complexities of the data landscape in 2024, adapting to the rise of Generative AI and modern data contracts is essential for driving innovation and maintaining competitiveness. By developing comprehensive strategies for AI adoption and data governance, organizations can harness the transformative power of Generative AI while ensuring ethical and responsible data usage. Likewise, embracing modern data contracts enables organizations to streamline data interactions, improve collaboration, and enhance data quality and consistency. By embracing these trends and implementing best practices, businesses can unlock new opportunities for growth and success in the digital age.

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The Journey from System Admin to DevOps Superstar

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Performance Testing Trends: Future of Software Optimization

Performance testing is an integral part of the software development lifecycle as it helps determine the scalability, stability, speed, and responsiveness of an application as compared to the workload given. It is not a standalone process and should be run throughout the software development process.It serves the purpose of assessing various aspects of an application’s performance, such as application output, processing speed, data transfer velocity, network bandwidth usage, maximum concurrent users, memory utilization, workload efficiency, and command response times. By evaluating these metrics, performance testers can gain valuable insights into the application’s capabilities and identify any areas that require improvementUsing AI to automate testing:Performance testing encompasses various stages, each posing unique challenges throughout the testing lifecycle. 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This approach emphasizes the need for system components to work together harmoniously while assessing their performance individually. The integration of cloud-based environments has become crucial, as cloud computing is an integral part of modern technology infrastructure. Cloud-based environments provide a flexible and reliable platform that enables seamless integration and coordination of various components, ultimately leading to enhanced system performance. It is crucial to prioritize comprehensive performance testing, which involves evaluating individual component performance, managing loads, monitoring in real-time, and debugging, to ensure optimal system performance.Self Service:When adopting the aforementioned trends, it’s essential to consider practical implementation tips for successful outcomes. For instance, performance engineers can use AI-powered tools to analyze performance data more effectively, leading to more accurate and actionable insights. Integrating cloud-based solutions can provide the flexibility and scalability required for modern performance testing demands. As stakeholders implement these trends, the collaboration between development, testing, and IT operations teams becomes crucial for successful integration and improved application performance.SaaS-based Tools:Testers can now easily set up and execute tests at cloud scale within minutes, thanks to the convergence of self-service, cloud-based testing, SaaS, and open architecture. Unlike older desktop-based tools that demand extensive setup, the emerging tools simplify the process with just a few clicks. Furthermore, these modern technologies offer seamless interoperability, significantly enhancing performance capabilities.Changing Requirements:In classic app testing, testers had to make educated guesses about the software’s use and create requirements and service-level agreements accordingly. However, in DevOps-oriented environments, performance requirements are seen as dynamic and evolving. Traditional requirements are now driven by complex use cases, accommodating different user experiences across various devices and locations. Performance engineering plays a critical role in continuously monitoring systems and proactively identifying and resolving issues before they can negatively impact customer retention or sales.Sentiment analysis:Monitoring production provides insight into server response times but does not capture the true customer experience. Synthetic transactions, on the other hand, simulate real user actions in production continuously. They can range from basic interactions like logging into an e-commerce site and adding products to a cart, to more complex transactions that track performance end to end without actually completing real orders or charging credit cards. 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This approach helps identify system weaknesses and addresses them before reaching the production stage.Conclusion:As software development continues to evolve, performance testing must keep pace with emerging trends and technologies. By leveraging AI-driven automation, open architecture with cloud integration, and practical implementation tips, stakeholders can optimize their performance testing processes to deliver high-performing and responsive software applications. Real-world examples and a focus on key performance metrics ensure that these trends are not only understood but effectively implemented to achieve the desired outcomes. Embracing these trends empowers software development teams to elevate the user experience, enhance customer satisfaction, and drive business success.

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