Here's how you can establish a solid AI foundation to kickstart your career.

Machine Learning: Supervised, unsupervised, and reinforcement learning Common algorithms (e.g., linear regression, decision trees, neural networks) Model training, validation, and testing Deep Learning: Neural network architectures Convolutional Neural Networks (CNNs) for image processing Recurrent Neural Networks (RNNs) for sequential data Transformers for natural language processing Natural Language Processing (NLP): Text preprocessing and tokenization Word embeddings Sentiment analysis and text classification Language models and generation Computer Vision: Image processing techniques Object detection and recognition Image segmentation Data Science fundamentals: Data collection and cleaning Exploratory data analysis

The paper published by Alan Turing, The Imitation Game, poses a question: can machines think? To answer this question, it is necessary to define: what does it mean for a machine to think? Turing's proposal is based on a "Turing" test that simulates virtual interactions between a human being and 2 personalities, a human and a machine, and aims to check whether it is possible to distinguish who is writing on the other side. Currently with NLP models like ChatGPT, the level of communication that a machine can perform is impressive, and can easily be confused with a human being. The statistics that lie at the basis of AI are also a fundamental point to consider when starting studies.

Dive into a career transformed by AI excellence! Imagine a journey where mastering AI fundamentals and essential programming languages isn't just a path—it's your launchpad to revolutionize industries and lead innovation. Engage in cutting-edge projects that redefine possibilities and stay ahead with the latest AI trends. Let's shape the future of technology together, where every challenge becomes an opportunity, and your expertise in AI becomes the cornerstone of success!

Embark on a career reshaped by AI excellence! Picture a journey where mastering AI fundamentals and essential programming languages isn't just a roadmap—it's your gateway to revolutionizing industries and spearheading innovation. Immerse yourself in groundbreaking projects that redefine boundaries and stay at the forefront with the latest AI advancements. Together, let's shape the future of technology, where every obstacle becomes a chance to excel, and your AI expertise becomes the bedrock of triumph!

?? Empezar con el pie derecho: - Asegúrese de dominar los conceptos básicos antes de adentrarse en algoritmos complejos del aprendizaje automático. - Progrese paso a paso: Comience con cursos en línea o libros sobre la historia de la IA, estadística básica y Python. - Fundamentos sólidos: Entender los principios del ML es crucial para tomar decisiones informadas en inteligencia artificial. - Python a la vanguardia: Este lenguaje versátil y fácil de usar es fundamental en el mundo de la IA. - Construir desde cimientos sólidos: Sentar las bases adecuadas es clave para avanzar con confianza en estudios más avanzados. ?Anímese a explorar y consolidar sus conocimientos en inteligencia artificial para potenciar su desarrollo profesional!

Keep your focus in 3 pillars: - Statistics - Programming (Python/R) - Business With those 3 together you are able to develop great AI models. Those competencies are the basis of AI/ML.

Hello everyone! Here are my tips for those interested in building knowledge in AI: 1) Study the Microsoft Azure AI Fundamentals learning path. You don't need to take the certification, but study. 2) Study Python OOP. Mainly in data science and machine learning, Python is essential due to the NumPy, pandas, Matplotlib, scikit-learn, Seaborn and TensorFlow libraries. 3) Start with local open-source AI projects. As you progress, evolve to Microsoft Azure, OpenAI and other mature platforms. 4) Obtain the brand new AWS AIF-C01 certification. This provides early access to AI on the leading cloud platform, AWS. Earning this will greatly enhance your profile, as AWS previously lacked such an accessible entry point to AI. #AWS #AmazonWebServices

To establish a solid AI foundation for your career, focus on building key skills. These will differ depending on whether you're in product, development, marketing, etc. Continuous learning through online courses, workshops, and tutorials will keep your skills sharp and industry-relevant! Deepen your understanding of machine learning algorithms, LLMs, prompting, RAG and other essential concepts. Engage with real-world data sets to apply your knowledge and solve practical problems. Use no-code platforms like MindStudio to prototype, test, and build incredible apps with NO coding!

The best way to always use today's best tools is to always search for knowledge, contributing and sharing in communities. To do this, there needs to be motivation. Search for business problems, preferably real and everyday problems (if you are employed) and develop models, chatbots, for simpler problems. This practice will greatly expand your scope of action and learning.

The topic introduces an advanced concept in artificial intelligence, NLP. I don't disagree with the topic at all, but I would like to present another advanced topic that moves away from the technical aspect and into the strategic one. I'm talking about responsible AI. The six pillars of responsible AI are: Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability. By articulating a solid and clear understanding of each of these six pillars, you not only demonstrate a profound knowledge of AI but also the care and responsibility that is increasingly required of an AI engineer today. #Fairness #ReliabilityandSafety #PrivacyandSecurity #Inclusiveness #Transparency #Accountability

Conquer the AI basics? Now, scale the stairway to advanced mastery! Neural networks, the cerebral cortex of AI, beckon. Deep learning, where algorithms mimic the human brain, awaits. Natural language processing (NLP), the bridge between machines and human language, demands exploration. Specialized courses become your guide – structured learning paths to illuminate the path. Delve into academic papers and engage in workshops and conferences. These are the crucibles where cutting-edge AI knowledge is forged. Ascend with purpose and become an AI vanguard shaping the future. #AI #AdvancedLearning #LifelongLearner

As I mentioned in the previous contribution, it is extremely important to be part of a community. By sharing content you reinforce your learning and even discover questions you didn't know you had. In addition to the obvious advantage of gaining knowledge from other people in the community, who have already solved similar problems. Be part of a community and try to be active in exchanging knowledge. This will certainly bring great evolution as a professional.

To kickstart your AI career, networking widely is essential. Connect with industry professionals through LinkedIn, attend AI conferences, workshops, and webinars. Participate in AI-related forums and online communities. Networking allows you to gain insights from experienced professionals, learn about the latest trends, and discover job opportunities. It also opens doors to mentorship and collaboration, which can accelerate your learning and career progression. Actively engaging with the AI community builds a strong professional network that supports your career development.

Curiosity, interest, the desire to learn more is one of the most striking factors when we observe results from people not only in AI but also in other professions. For those who work without the desire, it will be much more difficult to seek knowledge and "spend time" to always be up to date. My tip is for you to try to enjoy what you are doing, and make this continuous and long-term learning process last more smoothly in your routine.

Staying updated is crucial to kickstarting a career in AI. The field evolves rapidly, with new technologies and methodologies constantly emerging. Subscribe to AI-focused newsletters, follow leading AI research journals, and participate in relevant online communities. Attend industry conferences and webinars to hear directly from experts and innovators. This commitment to staying informed not only enhances your knowledge but also keeps your skills relevant and sharp, preparing you to leverage the latest AI advancements effectively in your career.

The most obvious place to look for hands-on experience in AI and ML is Kaggle. But try not to limit yourself to that. Seek to build ML models to solve problems in your reality. Do you have a finance manager app? No? Why not create an ML model to read credit card expenses and classify them? This can give strong indications of where your money is going. Do you like computer vision? And that camera in your backyard? Why not create an algorithm to detect people, and perhaps in the future classify them as potential intruders based on visual expressions? Solving a real problem is good because you will come across real situations. Such as missing, disorganized data, etc.

Only by applying your skills with what you have learned can you really leverage your knowledge. It makes your skills credible, makes your portfolio concrete and helps you assess which direction you'd wanna go while growing in AI It could be applying your technical skills and building models from scratch, or pioneering and consulting businesses in AI

To establish a solid AI foundation and kickstart your career, focus on acquiring a strong understanding of core concepts in mathematics, statistics, and programming, particularly in languages like Python and libraries such as TensorFlow and PyTorch; engage in hands-on projects and internships to gain practical experience; participate in online courses and certifications from reputable platforms to stay updated with the latest advancements; actively contribute to open-source projects and join AI communities to network with professionals; and continuously read research papers and attend conferences to deepen your knowledge and stay informed about emerging trends and technologies in the field.

Establishing a solid AI foundation involves more than mastering technical skills. Consider the broader implications of AI in society, such as ethical concerns and potential biases in algorithms. Stay informed about industry regulations and data privacy issues. Embrace interdisciplinary learning to understand how AI intersects with fields like psychology, philosophy, and law. Such a comprehensive approach not only enriches your technical proficiency but also prepares you to tackle complex challenges, making you a well-rounded professional in the AI community.

Domain knowledge and field-specific skills are crucial, but here are 10 impactful skills that require no inherent talent. Stay Current: Follow AI news and participate in forums to stay updated. Continuous Learning: Take courses, attend webinars, and get certifications in AI. Build Skills: Gain hands-on experience through projects and internships. Network: Join AI groups, attend conferences, and connect with professionals. Customize Applications: Tailor your resume and cover letter for each AI job. Prepare for Interviews: Practice coding and discuss AI concepts confidently. Showcase Projects: Create a portfolio highlighting your AI work and outcomes. Stay Persistent: Keep applying and stay motivated throughout your job search.