Artificial Intelligence

How do you start AI and ML ? Where do you go to learn these skills? What courses are the best? There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos. What’s more important than how you start is why you start. Start with why. Why do you want to learn these skills? Do you want to make money? Do you want to build things? Do you want to make a difference? Again, no right reason. All are valid in their own way. Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started. Got a why? Good. Time for some hard skills. I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course I’ve completed courses from (in order): Treehouse / youtube( free) - Introduction to Python Udacity - Deep Learning & AI Nanodegree Coursera - Deep Learning by Andrew Ng fast.ai - Part 1and Part 2 They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that. If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI. Join for more: https://t.me/machinelearning_deeplearning 👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5 Like for more ❤️ All the best 👍👍

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MIT Introduction to Deep Learning - 2023 Starting soon! MIT Intro to DL is one of the most concise AI courses on the web that cover basic deep learning techniques, architectures, and applications. 2023 lectures are starting in just one day, Jan 9th! Link to register: http://introtodeeplearning.com MIT Introduction to Deep Learning The 2022 lectures can be found here: https://m.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI

Future Trends in Artificial Intelligence 👇👇 1. AI in healthcare: With the increasing demand for personalized medicine and precision healthcare, AI is expected to play a crucial role in analyzing large amounts of medical data to diagnose diseases, develop treatment plans, and predict patient outcomes. 2. AI in finance: AI-powered solutions are expected to revolutionize the financial industry by improving fraud detection, risk assessment, and customer service. Robo-advisors and algorithmic trading are also likely to become more prevalent. 3. AI in autonomous vehicles: The development of self-driving cars and other autonomous vehicles will rely heavily on AI technologies such as computer vision, natural language processing, and machine learning to navigate and make decisions in real-time. 4. AI in manufacturing: The use of AI and robotics in manufacturing processes is expected to increase efficiency, reduce errors, and enable the automation of complex tasks. 5. AI in customer service: Chatbots and virtual assistants powered by AI are anticipated to become more sophisticated, providing personalized and efficient customer support across various industries. 6. AI in agriculture: AI technologies can be used to optimize crop yields, monitor plant health, and automate farming processes, contributing to sustainable and efficient agricultural practices. 7. AI in cybersecurity: As cyber threats continue to evolve, AI-powered solutions will be crucial for detecting and responding to security breaches in real-time, as well as predicting and preventing future attacks. Like for more ❤️ Artificial Intelligence

You never got that kind of Physics in school    I'm hooked on this crazy professor's video experiments. He illustrates interesting and simple Physics   — Melted steel + liquid = ?    — What if an atomic bomb is detonated in the Mariana Trench?    — Attempts to drown an anvil in mercury    Subscribe 👉 Physics on fingers Subscribe 👉 Physics on fingers Subscribe 👉 Physics on fingers

AI is the next biggest skill to learn. AI experts are earing up to $200000+ per year. Here are 4 FREE courses from Google and Microsoft that most people don't know: https://microsoft.github.io/AI-For-Beginners/? https://www.cloudskillsboost.google/paths/118 https://www.deeplearning.ai/courses/ai-for-everyone/ https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/ More free resources: https://t.me/udacityfreecourse

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Several future trends in artificial intelligence (AI) are expected to significantly impact the current job market. Here are some key trends to consider: 1. AI Automation and Robotics: AI-driven automation and robotics are likely to replace certain repetitive and routine tasks across various industries. This can lead to a shift in the types of jobs available and the skills required for the workforce. 2. Augmented Intelligence: Rather than fully replacing human workers, AI is expected to augment human capabilities in many roles, leading to the creation of new types of jobs that require a combination of human and AI skills. 3. AI in Healthcare: The healthcare industry is likely to see significant changes due to AI, with the potential for improved diagnostics, personalized treatment plans, and more efficient healthcare delivery. This could create new opportunities for healthcare professionals with AI expertise. 4. AI in Customer Service: AI-powered chatbots and virtual assistants are already transforming customer service, and this trend is expected to continue. Jobs in customer service may evolve to focus more on complex problem-solving and emotional intelligence, as routine tasks are automated. 5. Data Science and AI: The demand for data scientists, machine learning engineers, and AI specialists is expected to grow as organizations seek to leverage AI for data analysis, predictive modeling, and decision-making. 6. AI Ethics and Governance: As AI becomes more pervasive, there will be an increased need for professionals specializing in AI ethics, governance, and regulation to ensure responsible and ethical use of AI technologies. 7. Reskilling and Upskilling: With the evolving nature of jobs due to AI, there will be a growing need for reskilling and upskilling programs to help workers adapt to new technologies and roles. 8. Cybersecurity and AI: As AI systems become more integrated into critical infrastructure and business operations, there will be a growing demand for cybersecurity professionals with expertise in AI-based threat detection and defense. Overall, the rise of AI is expected to bring both challenges and opportunities to the job market, requiring individuals and organizations to adapt to the changing landscape of work and skills.

To automate your daily tasks using ChatGPT, you can follow these steps: 1. Identify Repetitive Tasks: Make a list of tasks that you perform regularly and that can potentially be automated. 2. Create ChatGPT Scripts: Use ChatGPT to create scripts or workflows for automating these tasks. You can use the API to interact with ChatGPT programmatically. 3. Integrate with Other Tools: Integrate ChatGPT with other tools and services that you use to streamline your workflow. For example, you can connect ChatGPT with task management tools, calendar apps, or communication platforms. 4. Set up Triggers: Set up triggers that will initiate the automated tasks based on certain conditions or events. This could be a specific time of day, a keyword in a message, or any other criteria you define. 5. Test and Iterate: Test your automated workflows to ensure they work as expected. Make adjustments as needed to improve efficiency and accuracy. 6. Monitor Performance: Keep an eye on how well your automated tasks are performing and make adjustments as necessary to optimize their efficiency.

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Complete Roadmap to learn Generative AI in 2 months 👇👇 Weeks 1-2: Foundations 1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI. 2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning. Weeks 3-4: Machine Learning Basics 1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics. 2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics. Weeks 5-6: Deep Learning 1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes. 2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data. Weeks 7-8: Generative Models 1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). 2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models. Additional Tips: - Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings. - Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others. Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible. 2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day. Best Resources to learn Generative AI 👇👇 Learn Python for Free Prompt Engineering Course Prompt Engineering Guide Data Science Course Google Cloud Generative AI Path Unlock the power of Generative AI Models Machine Learning with Python Free Course Deep Learning Nanodegree Program with Real-world Projects Join @free4unow_backup for more free courses ENJOY LEARNING👍👍

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