🔅 Advanced NLP with Python for Machine Learning
🌐 Author: Gwendolyn Stripling
🔰 Level: Advanced
⏰ Duration: 1h 26m
📗 Topics: Natural Language Processing, Machine Learning, Python
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🌀 Build upon your foundational knowledge of natural language processing by exploring more complex topics.
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This course is for anyone who wants to learn more advanced NLP methods. Instructor Gwendolyn Stripling, PhD, begins with a look at the fundamental concepts and principles of NLP, including the evolution and significance of natural language processing. She then reviews some NLP and Python basics—and introduces the NLP library spaCy—before jumping into more modern techniques and advancements in natural language processing using Transformer Models like GPT and BERT. Methods such as supervised fine-tuning, parameter efficient fine-tuning (PEFT), and retrieval-augmented generation (RAG) give you the foundational knowledge you need to improve large language model (LLM) performance. Learn the ways you can apply NLP in your applications and day-to-day, including how to analyze customer sentiments Each chapter ends with a challenge and solution, so you can test your knowledge as you go.
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🔅 Amplify Your Personal Brand with Generative AI
🌐 Author: Morgan Young
🔰 Level: General
⏰ Duration: 48m
📗 Topics: Personal Branding, Generative AI Tools
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🌀 Learn how to leverage cutting-edge tools powered by generative AI to build, boost, and grow your personal brand.
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Below is a list of guided projects to master AI & ML with Python that you should try
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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.
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.
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🔅 Computer Vision on the Raspberry Pi 4
🌐 Author: Matt Scarpino
🔰 Level: Intermediate
⏰ Duration: 1h 43m
📗 Topics: Raspberry Pi, Computer Vision
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🌀 Find out how to write and execute computer vision applications on the Raspberry Pi 4.
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More and more applications are using computer vision to detect and recognize objects. These applications usually execute on large computers, but developers can save money and power by running them on single-board computers (SBCs). The Raspberry Pi 4 is one of the most popular SBCs available. It's also the first computer in the Raspberry Pi family powerful enough to execute computer vision applications. Also, the software needed to build these applications can be downloaded freely from the Internet. In this course, instructor Matt Scarpino shows programmers how to write and execute computer vision applications on the Raspberry Pi 4. Matt introduces you to using the Thonny IDE, the OpenCV library, and NumPy array operations. He steps through object detection and neural networks, then explores convolutional neural networks (CNNs), including the Keras package and the TensorFlow package. Matt also walks you through what you can do with a Raspberry Pi HQ camera.
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To truly master these foundational algorithms
It's crucial to dive deeper into their real-world applications and understand how AI is shaping the future.
That's where "The Most Effective Guide to Master AI" comes in! This comprehensive guide covers everything you need to know:
- Real-world AI applications
- Computer Vision
- Generative Models
- Essential AI tools
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