What do you do if your analytical skills need a boost with artificial intelligence and machine learning?

Use your background to maximum effect. I have no formal training in computer science. My mathematics formal training is focused on biophysics. As a lawyer and patent agent, my value is heavily connected to written and spoken communication skills. Communicating with applications powered by LLM or image generation transformers was facilitated by an expectation-free interest in maximizing my ability to communicate with an AI and get outputs that are useful to me - whether text or images. Learning around this naturally filled in gaps to help me establish realistic expectations from AI, as well as from my ability to use AI.

Always test AI output with your logical conclusion and understand the gaps. Data is always data, what goes in logic will come out. Test the logic

My fundamentals since my academic days always comprise engineering, maths, science, and technology. Then human intelligence, communication, emotions, analytics, and interpersonal skills were part and parcel of my day-to-day routine. It is mandatory to have these basics strong and clear in your mind. If you have these then Artificial Intelligence or Mechanised Learnings are not difficult to inculcate.

Understand Basics. Be the first to add your personal experience. Identify Tools. ... Practical Learning. ... Collaborate Online. ... Continuous Education. ... Apply Ethically. ...Feed more data. Adding new and fresh data is one of the most common and effective methods of improving the accuracy of your machine-learning model. ... Improve the data. Improving the existing data can also result in an improved AI/ML model. ... Improve the algorithm.

If your analytical skills need a boost specifically in the areas of artificial intelligence (AI) and machine learning (ML), there are several steps you can take to enhance your proficiency. Enroll online courses focused on AI and ML, platforms like Coursera, edX, and Udacity offer a wide range of courses taught by industry experts and academics. Courses covering topics like machine learning algorithms, deep learning, natural language processing, and data science develop hands-on experience. Practice coding and implementing AI and ML algorithms, where platforms like Kaggle provide datasets and competitions where you can apply your skills to real-world problems. Contributing to open-source projects to gain practical experience as well.

Creo que las herramientas te van a elegir a ti, más que tu las elijas a ellas....especialmente si son gratuitas...o te las elegirán e tu centro de trabajo...

The application is a great tool itself. I have ChatGPT, including the appropriate custom bots open all the time while learning about any topic - including AI. Using appropriately-selected bots, whether I customized them myself or otherwise, is key. I fast-tracked my own ChatGPT customization with Igor Pogany's excellent YT channel AI Advantage, as well as his downloadable templates. Not a paid endorsement, but an endorsement nonetheless.

Easy ways to get started on using AI as your assistant is 1) Use chatGPT 3.5 to prompt and get useful code for your data analysis like Pyspark, Python code a)to process and b) tosummarize c) to visualize data 2) Databricks has Command+ I keyboard shortcut to enter prompt in any notebook cell . It helps you a) to debug code b) to performance tune your code c) fresh code if you are not sure where to start

AI and ML are powerful, but they're not magic. Think of them as supercharged assistants, not replacements for your critical thinking. Start by understanding the problem you're trying to solve. What data do you have? What kind of insights are you looking for? Then, explore the AI/ML toolbox. There are tons of user-friendly options – forget complex coding for now. For example, say you're analyzing sales trends. Visualization tools can turn mountains of data into clear charts, revealing patterns you might miss. Machine learning can help you forecast future sales based on historical trends and market data. But it's your job to interpret these results. Is the ML model reliable? Do the sales trends make sense considering other factors?

Crux of the matter is to be identified before getting explored to the tools. Ex. You cannot ask ChatGPT to self design itself rather a proper instruction has to be given on the same to get the desired outcome.

The best way to learn further about AI and machine learning is to use its applications. Often, the best way to learn something is by experiencing it. Using chat bots, AI powered softwares, etc. can help you to learn how these tools can help and how powerful they can be in growing productivity.

In my quest to boost my analytical prowess in AI and ML, I prioritize online courses, hands-on projects, community engagement, continuous learning, industry events, online resources, practical application, networking, growth commitment, and mentorship for guidance and collaboration.

Focus on small, achievable projects. Think of them as stepping stones. Start by cleaning messy datasets – it's not glamorous, but it's crucial for good analysis. Use online tutorials to learn the basics of Python libraries like Pandas for data manipulation. Then, progress to building simple models. There are tons of online resources with pre-built datasets on everything from predicting house prices to classifying handwritten digits. The cool thing about AI and ML is there's a massive online community. There are forums full of people willing to help.

Real life projects be it official or personal can help big time while using AI and Machine learning from scratch to boost Analytical skills. Having basic theoretical knowledge is definitely an added advantage that I agree with, but to get the most out of AI, practical demo projects create more impact on mastering the art. Few more things to consider is - let yourself make mistakes during practical projects, put as much self thoughts as possible, try all ifs and buts, create conditionalities if not present, challenge the all possible outcomes, create several ways to reach the goal and all these are associated directly into practical learning!

Practical knowledge is most important along with theoretical learning. We can understand the work/task/code better if we do the practical with real life examples. It is important and effective if we keep practicing and keep solving the problems. Just going through the course or books doesn't provides us vast area of knowledge.

IMO, Collaboration has been pivotal in my research journey to sharpen analytical skills in AI/ML. Actively engaging with global online communities, notably GitHub and Stack Overflow, has unlocked access to real-world projects, offering deep insights that transcend textbook theories. Sharing challenges & diverse solutions with peers not only accelerated my problem-solving skills but also enriched my understanding through a multitude of perspectives. A notable collaboration involved refining a model for healthcare data analysis in my book chapter. Our collective effort was crucial in identifying and mitigating biases, significantly enhancing the model's predictive accuracy, and determining the use cases for blockchain in healthcare.

In my experience, collaborating online is a highly effective way to accelerate your learning curve in AI and ML. Online forums and communities serve as invaluable resources where you can seek guidance, share insights, and benefit from the experiences of others. Platforms like GitHub offer opportunities to collaborate on code and explore projects shared by the community. Engaging with these online communities exposes you to diverse perspectives, novel ideas, and innovative approaches, all of which can greatly enhance your analytical skills in AI and ML.

There is no end of learning. Continuous learning is a process of self developing and make yourself ready for the new tech. AI and ML are evolving and now the world is kind of an open learning era. You can learn from any sites if you are willing to learn. To make your analytical skills up to date and sharp, there is no 2nd choice without continuous learning

To put it short, stay curious. Here is the hack tho - choose a social, let's take tik tok for example, search and find the most interacted with names in the space, (say Rajiv Shah), follow them and be a part of their community - the more you interact and engage the more you will see your feed change to orient you around the desired content.

Highlighting the importance of staying updated with advancements in artificial intelligence and machine learning to enhance analytical skills. By engaging in continuous learning, individuals can explore new techniques, tools, and methodologies that can augment their analytical capabilities. This approach involves seeking out relevant courses, workshops, and resources to deepen understanding and refine skills in AI and ML, ultimately contributing to improved analytical proficiency in these domains.

I completely agree. Continuous learning is needed to understand any perspective. The tech industry is continuously evolving and learning new skills, tools and technology will definitely help in aligning knowledge with practice.

AI and ML are medium to derive the final output or desired results. In Short, Stay hungry, Stay Updated in order to stay in the market.

Es imprescindible que se establezca un código ético para el uso de estas tecnologías en la mayoría de los sectores implicados. Sin embargo, me temo que irá por detrás del desarrollo de las aplicaciones. Sin duda, en el que yo me muevo, el de la salud tiene implicaciones demasiado importantes como para que no esté regulado.

It is important to have an ethical understanding of AL and ML. Therefore, making a positive contribution rooted in ethical principles is essential. AL and ML can have a negative impact on society if it is not executed with caution. However, AL and ML can be an effective tool which can benefit society in a greater direction towards progress.

In addition to enhancing your analytical skills with AI and ML, it's imperative to apply empathy throughout your journey. Recognize the ethical implications inherent in these technologies, including issues surrounding data privacy, algorithmic bias, and the ramifications of automated decision-making. As you develop proficiency, prioritize ethical considerations and strive to ensure that your work aligns with ethical standards and contributes positively to society. Embracing empathy in your use of AI and ML is not just a professional responsibility but also a moral imperative, fostering a more equitable and responsible application of these powerful tools.

If my analytical skills need a boost, I am probably not getting enough sleep. I would then try to sleep more, and then re-organize my time schedule so I read content that would stimulate my mind and offer me a fresh perspective of things. I would absolutely not use AI. IMHO, it is not ready enough to deliver the analysis humans do and even if I would be wrong about that, it's not safe enough (and that I am not wrong about).

Test all the dynamics and make AI our tool in our life. The world is becoming AI driven era and we need to stay in the trend of AI. We must further develop more AI tools to make workforce effective.

When analyzing data, making use of AI can be key to improving our skills. If you find yourself in this situation you have to know that AI never stops evolving and changing, What do I mean by this? I mean you can't stay stagnant, you have to learn with it to stay up to date. For the most novices, I would recommend starting from the most basic because it can become very complicated without solid foundations, through courses, applications, among others. Those who are already familiar with this world of AI, machine learning, etc. and want to get more out of it, can choose to attend different master classes in which you will learn how to best interpret it and how to get the best performance out of it.

To enhance analytical skills with AI and ML, immerse yourself in continuous learning, practical projects, and experimentation. Collaborate with peers, seek feedback, and stay curious about new domains. Additionally, prioritize understanding ethical implications to make informed decisions.