Here's how you can master artificial intelligence with effective strategies.

When I started as an AI/ML engineer at Google six years ago, mastering AI fundamentals was key. Working on an intelligent virtual assistant, I quickly realized the value of understanding core concepts like neural networks, supervised/unsupervised learning, and natural language processing (NLP). Despite my eagerness for advanced models, my mentor emphasized the basics. This foundation was crucial when we faced performance issues. I proposed sequence-to-sequence models with attention mechanisms, learned during my foundational studies, which significantly improved accuracy. This experience proved that a solid grasp of AI basics is essential for tackling advanced problems and real-world challenges.

As one of my colleague rightly pointed, most ai today ( neural networks) are actually doing matrix multiplications within themselves. Irrespective of how complex they are, the core concepts are basic mathematics and data structures. Building a strong understanding of these can greatly help in building a career in AI.

First thing first, in my mind one should learn probability theory, some basic linear algebra, partial derivatives and also implement the basics in python. Then go for the learning AI algorithms and its in real problems

Foundation: 1. Learn Basics: - Mathematics: Linear algebra, calculus, statistics. - Programming: Python, R. 2. Core Concepts: - Machine Learning: Algorithms, data preprocessing, model evaluation. - Deep Learning: Neural networks, TensorFlow/PyTorch. 3. Projects: - Start with simple datasets (e.g., Iris, MNIST). - Progress to complex projects (e.g., NLP). 4. Competitions: - Platforms like Kaggle to solve real-world problems. 5. Advanced Topics: - Explore reinforcement learning, generative models etc. 6. Stay Updated: - Follow AI research papers, blogs, attend webinars/conferences. 7. Community: - Join AI communities, attend meetups. 8. Collaborate: - Work on group projects, contribute to open-source.

When I first delved into AI, I underestimated the importance of a solid foundation. I was eager to tackle advanced algorithms, but quickly realised I was in over my head. I had to rewind and start with the basics—neural networks, supervised vs. unsupervised learning, and the essentials of natural language processing. It was only after taking some Linkedin and Coursera courses and working through beginner tutorials that I started to truly understand AI. Building this foundational knowledge was crucial, and I’m glad I took the time to do it right.

A large number of online platforms, cloud service providers already provide a well defined pathway and goals for excelling in machine learning career. Consistently following them or building similar roadmaps can help achieve goals faster.

Define clear objectives for your AI journey, whether it's mastering a specific technique or applying AI in a particular domain, to stay focused and motivated.

Define clear learning goals and objectives for your AI journey. Determine what specific skills or areas of AI you want to master and create a roadmap to achieve those goals.

As far as objectives are concerned, I'd advise you to start with a small project, ideally with data that you recover yourself (just to see how difficult it can sometimes be) or by recovering data that already exists. and test different solutions to produce results on this data, such as machine learning algorithms with scikitlearn or small models created from scratch with pytorch. my first project was to detect handwritten numbers by training a small model on mnist for example, for a small personal project.

Set well-defined learning targets and objectives for your journey into artificial intelligence. Identify the specific skills or AI domains you wish to become proficient in and outline a detailed plan to reach these goals.

Python is quite straightforward to learn, but has numerous applications, be it machine learning or astrophysics, there are large number of libraries built on python in almost all domains.

every time I get open-source code from someone doing data science, I run into a problem: the code is often pretty bad, it runs, it works well but it's all in the same file, there's a lot of optimization that could be done. the code will be your working tool and it's necessary (at least in the long term) to have a solid base so that you're more at ease. You'll even be able to create some very cool things, both for training and for deployment!

I think to learn coding is good for everyone. No matter if you are working later with code or not. The reason is that it teaches you how to structure thoughts logically which you can also benefit from in other areas. You think more about what effects other things and the chain of logic interactions between elements. For data Science ofc coding is key and if you can not code you can not really do data science. Citizen DS tools do not count. That being said statistics are ofc even more important than coding (and more difficult to learn) When has your last stat class been?

In the AI landscape, data plays a critical role. Mastering data handling and manipulation is essential for training AI models effectively. Begin by comprehending the type of data you have, then dive into exploratory data analysis and visualization methods to improve your AI tasks.

Understanding data is very crucial, as it helps in identifying the core problem statement. Visualisation of data can greatly help in finding the right kind of solutions, preprocessing steps needed.

The Importance of Data cannot be overemphasized in the AI ecosystem. Effective data handling and manipulation techniques are necessary for training AI models. It is crucial to first understand the type of data you’re working with then get your hands dirty with Exploratory Data analysis and visualization techniques to enhance your Ai workloads.

Mastering data skills for AI involves honing your abilities in data collection, processing, analysis, and visualization. These skills are crucial for unlocking insights and optimizing AI projects effectively. Maybe you would require a good Data Engineer for that (like me 😏😅😁) ...

Data is key! You can find a lot of informations by practicing how to decipher a dataset : - build plots - compute correlations - run regressions - what about PCA - deep auto-encoders maybe? You will gain a lot by digging into a dataset !

It's essential to emphasize the deployment and scalability of applications. Select a pre-built model and develop a complete ML application using a robust architecture(you can find various architectures online). This approach provides practical experience and deepens your understanding of the complexities involved in real-world ML development. As off-the-shelf models continue to improve, the focus shifts towards DevOps practices. Efficiently integrating and managing these models—plugging them in and out as needed—becomes crucial, allowing you to adapt quickly and maintain scalability in your solutions.

The more real world datasets you pick up the more intuitions you'd get around why certain approaches and best practices are needed. This would help you understand and comprehend research papers and upcoming methodologies better. In general this would also reduce the time it takes to formalise and explore model architectures that make more sense to a given use case.

Antecipar e gerenciar riscos é essencial em projetos de TI. Recentemente, em um projeto de migração de dados de um cliente, identifiquei possíveis problemas técnicos e a indisponibilidade de recursos como riscos significativos. Desenvolvi um plano de gerenciamento de riscos detalhado, que incluía estratégias de mitigação e alternativas de recursos. Quando a indisponibilidade de recursos ocorreu, estávamos preparados e conseguimos redirecionar rapidamente os recursos necessários, mantendo o projeto no prazo e sem afetar a operação do cliente.

Network and Collaborate: Engage with the AI community through forums, conferences, and social media to exchange knowledge and collaborate on projects. Building a strong network can provide support, inspiration, and opportunities for growth in the AI field.

it's not a sprint but a marathon, so it's best to learn the basics before getting down to business. Being in too much of a hurry can be detrimental to your learning.