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What is machine learning?
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Why use machine learning for decision-making?
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What are the challenges of machine learning for decision-making?
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How to use machine learning for decision-making?
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How to improve your machine learning skills?
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Here’s what else to consider
Decision-making is a crucial skill for any professional, especially in data-driven fields. However, it can also be challenging, complex, and uncertain. How can you leverage the power of machine learning algorithms to improve your decision-making process and outcomes? In this article, we will explore some of the benefits and limitations of machine learning for decision-making, and how to apply some practical tips and tools to enhance your data analysis and interpretation.
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- Daniel (Dan) Lieb Data and Artificial Intelligence Leader | Executive Influence | Strategy | Technology | Innovation | Digital…
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- Isham Rashik 🤖 Machine Learning Engineer 🦾 Generative AI 🧠 Natural Language Processing 💻 Prompt Engineering 👨💻 Computer…
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1 What is machine learning?
Machine learning is a branch of artificial intelligence that enables computers to learn from data and perform tasks without explicit programming. Machine learning algorithms can discover patterns, make predictions, and optimize solutions based on data inputs and feedback. Machine learning can be classified into different types, such as supervised, unsupervised, and reinforcement learning, depending on the level of human guidance and the goal of the algorithm.
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I agree with the statement. However, when using the outcomes of ML for decision making one need to think about the quality of data, model choice and independent testing & validation of results of the applied ML. Ml increases the speed, quality and accuracy of the decisions and is more reliable than an ‘expert’ opinion.
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- Ahmed Raafat Unstructured Data Scientist, L2 @Beyond Limits
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Machine learning, a branch of artificial intelligence, empowers computers to learn from data without explicit programming. It encompasses supervised, unsupervised, and reinforcement learning, each tailored to different learning scenarios. Data quality and quantity are pivotal in training accurate models, enabling applications from healthcare diagnostics to autonomous vehicles. While it promises transformative advancements, challenges like fairness, transparency, and ethical use must be addressed to ensure its responsible integration into society.
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2 Why use machine learning for decision-making?
Machine learning can offer several advantages for decision-making, such as speed and scalability, accuracy and reliability, and adaptability and innovation. These algorithms can process large volumes of data faster and more efficiently than humans, and handle complex problems involving multiple variables and constraints. They can reduce human errors and biases, provide consistent and objective results based on data evidence and logic, learn from new data and feedback, adapt to changing situations and environments, as well as generate novel and creative solutions that humans may not think of.
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Machine learning in business decision-making can be compared to the introduction of assembly lines in manufacturing. Just as assembly lines revolutionized production by increasing efficiency and throughput, automated insights from ML supercharges the decision-making process.One can swiftly analyze data at a volume and speed that's humanly impossible. By learning from historical decisions and influencing factors, ML can share recommendations at a rapid pace for humans to act upon.
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- Ahmed Raafat Unstructured Data Scientist, L2 @Beyond Limits
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Machine learning is a pivotal tool for decision-making due to its capacity to rapidly process vast data, ensuring accuracy and objectivity while mitigating human errors and biases. Its adaptability and innovative potential, along with its ability to handle complex, multifaceted problems, make it indispensable in today's data-driven world. Moreover, machine learning continually evolves through learning from new data, offering a proactive and efficient approach to decision-making that humans alone cannot match.
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3 What are the challenges of machine learning for decision-making?
Machine learning is not a one-size-fits-all solution for decision-making, and it also comes with certain challenges and limitations. For instance, the quality and availability of data can influence the accuracy of the algorithm; it may be difficult to understand how the algorithm works, why it makes certain decisions, and what the underlying assumptions and trade-offs are; and humans need to be involved in the process to define the problem, select data, evaluate results, implement actions, monitor and audit the algorithm, and ensure that it aligns with the goals, values, and norms of the organization and society.
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The challenges of machine learning for decision-making include obtaining high-quality data, ensuring interpretability of complex models, achieving generalisation to new data, addressing biases and fairness issues, scaling up for large datasets, ensuring model robustness, integrating models into existing systems, and complying with regulations. Overcoming these challenges requires a multidisciplinary approach and ongoing monitoring and adaptation of machine learning systems.
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- Ahmed Raafat Unstructured Data Scientist, L2 @Beyond Limits
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Machine learning indeed presents challenges for decision-making. Data quality and availability can significantly impact its effectiveness, and the "black box" nature of some algorithms can hinder transparency and interpretability, making it challenging to trust and understand their decisions fully. Additionally, while machine learning can automate aspects of decision-making, human oversight remains essential to frame problems, guide data selection, evaluate outcomes, and ensure ethical alignment with organizational values and societal norms. Striking the right balance between automation and human involvement is critical to harnessing the potential of machine learning while addressing these challenges.
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- Fred Simkin Developing and delivering knowledge based automated decisioning solutions for the Industrial and Agricultural spaces.
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Using ML in automating decisions, perhaps in some strictly defined cubicle/office bound process and where there are no regulatory and compliance requirements (they are after all black boxes). However in dynamic environments like an offshore oil rig diagnostics and repair or organizations which is subject to intense scrutiny from regulatory bodies, based on 40 years in the field...nope
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4 How to use machine learning for decision-making?
Machine learning can be a powerful tool for decision-making, but it requires careful planning, execution, and evaluation. To use machine learning for decision-making, you need to define the problem and the objective, choose the data and the algorithm, analyze and interpret the results, and make and communicate the decision. Firstly, you must identify the problem you want to solve, the objective you want to achieve, and the criteria you want to measure. You also need to select data sources, data preparation methods, and an appropriate machine learning algorithm. Secondly, after running the algorithm, you should analyze and interpret the results with tools such as visualization or feature importance. Finally, you must make a decision based on the results and communicate it clearly to stakeholders. By following these tips and tools, you can effectively use machine learning for decision-making.
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I once worked with a startup eager to use machine learning for every business decision. While commendable, this is impractical. And unnecessary.I explained to the founder that machine learning must be another tool in your tool box. Yes, it is powerful but not omnipotent. It's vital to delineate which problems are better addressed by simple descriptive analysis or statistics. For example, while ML can aid in predicting customer preferences, discovering what features your customers liked the most can be done with simpler diagnostic techniques.
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- Ahmed Raafat Unstructured Data Scientist, L2 @Beyond Limits
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Effectively using machine learning for decision-making requires a well-defined process that encompasses problem definition, data selection, algorithm choice, result analysis, and clear communication. It starts with a clear understanding of the problem at hand and the objectives to be achieved, ensuring that machine learning aligns with the specific decision-making needs. Careful data selection and preparation are pivotal, as the quality and relevance of data directly impact the model's accuracy. Choosing the right algorithm suited to the problem is equally critical. Once the model is run, thorough analysis and interpretation of the results, aided by visualization and feature importance tools, provide insights for informed decision-making.
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5 How to improve your machine learning skills?
Machine learning is a rapidly expanding field that requires continual learning and improvement. To sharpen your machine learning skills, you should start by learning the fundamentals, such as mathematics, statistics, programming, and data science. You can use online courses, books, or blogs to gain this foundational knowledge. Then, you should apply your skills to real-world problems and projects using datasets, tools, and platforms. Platforms such as Kaggle, Coursera, and Udemy have machine learning challenges and competitions you can join. Additionally, keep up with the latest trends and developments in machine learning by using online resources such as podcasts, newsletters, or journals. Machine learning can improve your decision-making skills by providing data-driven insights, predictions, and solutions. Nevertheless, it's important to be aware of its challenges and limitations and use it wisely and responsibly. By following these tips and tools, you can enhance your machine learning skills for better decision-making outcomes.
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- Isham Rashik 🤖 Machine Learning Engineer 🦾 Generative AI 🧠 Natural Language Processing 💻 Prompt Engineering 👨💻 Computer Vision 👁️ Data Science 📊 Community Mentor 👨🏫 On a drive to change the world 🚀
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The best way to improve your machine-learning skills is to start by exploring research papers (only if you have good enough basic knowledge) and equip yourself with cutting-edge techniques, methodologies, and insights. This knowledge drives innovative solutions, refining algorithms for better performance. By understanding the latest advancements and best practices, you can develop models that produce more accurate predictions, ensuring optimized decision-making and improved outcomes in real-world applications.
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- Ahmed Raafat Unstructured Data Scientist, L2 @Beyond Limits
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Improving your machine learning skills is essential in this fast-evolving field. Begin by establishing a strong foundation in mathematics, statistics, programming, and data science through online courses and educational resources. Practical experience is key, so engage in hands-on projects and competitions on platforms like Kaggle and Coursera to apply your knowledge to real-world problems. Staying updated with the latest developments through podcasts, newsletters, and journals is crucial to remain at the forefront of the field. Machine learning can indeed enhance decision-making by providing data-driven insights, but it's equally vital to understand its limitations and ethical considerations.
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6 Here’s what else to consider
This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?
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- Daniel (Dan) Lieb Data and Artificial Intelligence Leader | Executive Influence | Strategy | Technology | Innovation | Digital Transformation | Econometric Marketing | Behavioral Science | Banking Regulation | Mentoring | Great Danes
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Still not working for free, LinkedIn.But an example of a hard decision is "Which Beatles album is better, Rubber Soul or Revolver?" Just kidding, everyone knows it's Revolver.
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- Bartek Włodarczyk Advancing AI with Synthetic Data Cloud as CEO, PhD at SKY ENGINE AI
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No free data LN! But hard decision can be "to be or not to be" for instance. No free data LN! But hard decision can be "to be or not to be" for instance.
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