Difference between revisions of "How do I leverage Artificial Intelligence (AI)?"
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* [[How do I leverage Artificial Intelligence (AI)?]] ... [[Reading Material & Glossary|Reading/Glossary]] ... [[Courses & Certifications|Courses/Certs]] ... [[Education]] ... [[Help Wanted]] | * [[How do I leverage Artificial Intelligence (AI)?]] ... [[Reading Material & Glossary|Reading/Glossary]] ... [[Courses & Certifications|Courses/Certs]] ... [[Education]] ... [[Help Wanted]] | ||
* [https://www.arxiv-sanity.com/ Arxiv Sanity Preserver] to accelerate research | * [https://www.arxiv-sanity.com/ Arxiv Sanity Preserver] to accelerate research | ||
− | * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative | + | * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]] |
− | * [[Python]] | + | * [[Python]] ... [[Generative AI with Python|GenAI w/ Python]] ... [[JavaScript]] ... [[Generative AI with JavaScript|GenAI w/ JavaScript]] ... [[TensorFlow]] ... [[PyTorch]] |
− | * [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] | + | * [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] |
+ | * [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]] | ||
* [https://landing.ai/ai-transformation-playbook/?utm_source=CourseraMailingList&utm_medium=DLSMailingList&utm_campaign=Playbook AI Transformation Playbook | Andrew Ng - Landing AI] | * [https://landing.ai/ai-transformation-playbook/?utm_source=CourseraMailingList&utm_medium=DLSMailingList&utm_campaign=Playbook AI Transformation Playbook | Andrew Ng - Landing AI] | ||
* [https://www.aaai.org/ Association for The Advancement Of Artificial Intelligence] | * [https://www.aaai.org/ Association for The Advancement Of Artificial Intelligence] | ||
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* <b>AI in IoT (Internet of Things)</b>: Combine AI with IoT devices to create smart and autonomous systems for home automation, industrial applications, or healthcare monitoring. | * <b>AI in IoT (Internet of Things)</b>: Combine AI with IoT devices to create smart and autonomous systems for home automation, industrial applications, or healthcare monitoring. | ||
− | * <b>AI-driven Products or Services</b>: Develop AI-powered products or services that address specific needs or pain points in the market. These could be AI-based software applications, chatbots, virtual assistants, recommendation systems, or predictive analytics tools. You can create AI-powered products or tools that solve specific business problems. For example, you could create a chatbot that automates customer support or an [[analytics]] tool that uses machine learning to identify trends and patterns in data. | + | * <b>AI-driven Products or Services</b>: Develop AI-powered products or services that address specific needs or pain points in the market. These could be AI-based software applications, chatbots, virtual [[assistants]], recommendation systems, or predictive analytics tools. You can create AI-powered products or tools that solve specific business problems. For example, you could create a chatbot that automates customer support or an [[analytics]] tool that uses machine learning to identify trends and patterns in data. |
** <b>AI as a Service (AIaaS)</b>: Offer AI capabilities as a service to other businesses. This could involve providing access to pre-trained machine learning models, natural language processing (NLP) APIs, or computer vision algorithms that they can integrate into their own products or workflows. | ** <b>AI as a Service (AIaaS)</b>: Offer AI capabilities as a service to other businesses. This could involve providing access to pre-trained machine learning models, natural language processing (NLP) APIs, or computer vision algorithms that they can integrate into their own products or workflows. | ||
** <b>Subscription-based AI Services</b>: Offer subscription plans for access to premium AI features, datasets, or AI-powered tools on a recurring basis. | ** <b>Subscription-based AI Services</b>: Offer subscription plans for access to premium AI features, datasets, or AI-powered tools on a recurring basis. | ||
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<b>7 Ways to make money using Machine Learning and AI | Make MONEY from Machine Learning | <b>7 Ways to make money using Machine Learning and AI | Make MONEY from Machine Learning | ||
</b><br>This video titled "7 Ways to make money using Machine Learning and AI | Make MONEY from Machine Learning" explains 7 ways to make money using Machine Learning and AI (artificial intelligence). Opportunities are endless and there are a lot of ways using which people like students, professionals, teachers who has acquired knowledge in this field can earn a lot of money. | </b><br>This video titled "7 Ways to make money using Machine Learning and AI | Make MONEY from Machine Learning" explains 7 ways to make money using Machine Learning and AI (artificial intelligence). Opportunities are endless and there are a lot of ways using which people like students, professionals, teachers who has acquired knowledge in this field can earn a lot of money. | ||
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* <b>[[Generative AI]]/[[Conversational AI]]</b>: When a machine establishes a conversation with human in natural way. Example, [[ChatGPT]], Siri, Alexa, Cortana. [[Natural Language Processing (NLP)]] is a branch of AI that deals with the interaction between computers and human language. By leveraging NLP, you can use AI-powered chatbots to interact with customers and provide support or use [[Sentiment Analysis]] to gauge customer feedback. | * <b>[[Generative AI]]/[[Conversational AI]]</b>: When a machine establishes a conversation with human in natural way. Example, [[ChatGPT]], Siri, Alexa, Cortana. [[Natural Language Processing (NLP)]] is a branch of AI that deals with the interaction between computers and human language. By leveraging NLP, you can use AI-powered chatbots to interact with customers and provide support or use [[Sentiment Analysis]] to gauge customer feedback. | ||
* <b>[[Assistants]], [[Personal Companions]] & [[Agents]]</b>: Enhance our lives through intelligent support, personalized assistance, and efficient task management. | * <b>[[Assistants]], [[Personal Companions]] & [[Agents]]</b>: Enhance our lives through intelligent support, personalized assistance, and efficient task management. | ||
− | * <b>Speech Recognition</b>: Speech recognition uses AI algorithms to convert spoken language into text. You can use speech recognition to automate transcriptions, create voice-activated assistants, or improve accessibility for users with disabilities. | + | * <b>Speech Recognition</b>: Speech recognition uses AI algorithms to convert spoken language into text. You can use speech recognition to automate transcriptions, create voice-activated [[assistants]], or improve accessibility for users with disabilities. |
* <b>[[Recommendation]] Engines</b>: Hyper Personalization. Example Product, News, Content. AI-powered recommendation engines analyze user behavior and data to provide personalized recommendations. You can use recommendation engines to suggest products, services, or content based on a user's interests and preferences. | * <b>[[Recommendation]] Engines</b>: Hyper Personalization. Example Product, News, Content. AI-powered recommendation engines analyze user behavior and data to provide personalized recommendations. You can use recommendation engines to suggest products, services, or content based on a user's interests and preferences. | ||
* <b>[[Predictive Analytics]]</b>: Uses machine learning algorithms to analyze historical data and make predictions about future events. Examples - Sales [[forecasting]], Inventory [[forecasting]], Demand planning, optimize supply chain management | * <b>[[Predictive Analytics]]</b>: Uses machine learning algorithms to analyze historical data and make predictions about future events. Examples - Sales [[forecasting]], Inventory [[forecasting]], Demand planning, optimize supply chain management | ||
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+ | = Roadmaps = | ||
+ | Overall, roadmaps can be a valuable tool by providing a clear and concise overview of the learning journey. | ||
+ | |||
+ | == Machine Learning (ML) Roadmap == | ||
+ | * [https://dbourke.link/mlmap Interactive Machine Learning Roadmap] | ||
+ | * [https://github.com/mrdbourke/machine-learning-roadmap Machine Learning Roadmap Resources] | ||
+ | * [https://www.mrdbourke.com/mlcourse/ The Zero to Mastery Machine Learning Course] | ||
+ | |||
+ | {|<!-- T --> | ||
+ | | valign="top" | | ||
+ | {| class="wikitable" style="width: 550px;" | ||
+ | || | ||
+ | <youtube>pHiMN_gy9mk</youtube> | ||
+ | <b>2020 Machine Learning Roadmap | ||
+ | </b><br>Getting into machine learning is quite the adventure. And as any adventurer knows, sometimes it can be helpful to have a compass to figure out if you're heading in the right direction. Although the title of this video says machine learning roadmap, you should treat it as a compass. Explore it, follow your curiosity, learn something and use what you learn to create your next steps. Links: Interactive Machine Learning Roadmap - https://dbourke.link/mlmap | ||
+ | |} | ||
+ | |}<!-- B --> | ||
+ | |||
+ | == Large Language Model (LLM) Roadmap == | ||
+ | * [[Large Language Model (LLM)]] ... [[Large Language Model (LLM)#Multimodal|Multimodal]] ... [[Foundation Models (FM)]] ... [[Generative Pre-trained Transformer (GPT)|Generative Pre-trained]] ... [[Transformer]] ... ([[GPT-4]]) ... [[GPT-5]] ... [[Attention]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]] | ||
+ | |||
+ | The following subjects are derived from Ryan Nguyen's excellent 'How AI Built This' article, "[https://howaibuildthis.substack.com/p/this-is-what-id-do-if-i-could-learn?utm_source=post-email-title&publication_id=1643556&post_id=137614981 This is what I'd do if I could learn how to build LLM from scratch] where he asks the question, "What if start from absolute zero, knowing what I know now? Where would I begin? How would I tackle each challenge?" ... | ||
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+ | * [[Large Language Model (LLM)|Introduction to Large Language Model (LLM)s]] | ||
+ | * [[Attention|Attention Mechanism]] ... The emergence of LLM | ||
+ | * [[Transformer|Transformer Models]] | ||
+ | * [[Embedding|Embeddings]] | ||
+ | * [[Database|Vector Database]] | ||
+ | * [[Foundation Models (FM)]] | ||
+ | * [[Context]] | ||
+ | * [[Generative Pre-trained Transformer (GPT)#Let's build GPT: from scratch, in code, spelled out - Andrej Karpathy|Let's build a Generatively Pretrained Transformer (GPT)]] | ||
+ | * [[Fine-tuning]] | ||
+ | * [[Train Large Language Model (LLM) From Scratch]] | ||
+ | * [[In-Context Learning (ICL)]] | ||
+ | * [[Semantic Search]] | ||
+ | * [[Prompt Engineering (PE)]] | ||
+ | * [[LlamaIndex]] | ||
+ | * [[LangChain]] | ||
+ | * [[Retrieval-Augmented Generation (RAG)]] | ||
+ | * [[Assistants]] ... [[Personal Companions]] ... [[Agents]] | ||
+ | * [[Algorithm Administration#Large Language Model Operations (LLMOps)|Large Language Model Operations (LLMOps)]] | ||
+ | |||
+ | |||
+ | |||
+ | <youtube>thqP4I9NZls</youtube> | ||
+ | <youtube>twHxmU9OxDU</youtube> | ||
= How to Get into AI = | = How to Get into AI = | ||
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<b>How to Make Money as a Programmer in 2018 | <b>How to Make Money as a Programmer in 2018 | ||
</b><br>I'll go through 5 methods that you can use to make money as a programmer! We are lucky in that our skill will only get more valuable to society over time. Links to everything I've discussed are below. | </b><br>I'll go through 5 methods that you can use to make money as a programmer! We are lucky in that our skill will only get more valuable to society over time. Links to everything I've discussed are below. | ||
+ | |} | ||
+ | |}<!-- B --> | ||
+ | = Learning Approaches = | ||
+ | |||
+ | {|<!-- T --> | ||
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+ | {| class="wikitable" style="width: 550px;" | ||
+ | || | ||
+ | <youtube>T0r-uCXvDzQ</youtube> | ||
+ | <b>The Fastest Path To [[Deep Learning]] | ||
+ | </b><br>Learning [[Deep Learning]] can be confusing and often very frustrating. In this talk, Sam will set out a roadmap to go from knowing nothing to being fluent in [[Deep Learning]] in the fastest way possible. He will highlight courses, frameworks, math, methods, and strategies to get you started and set you on the path to being able to use [[Deep Learning]] for real worlds problems and apps. EVENT: FOSSASIA 2018 SPEAKER: Sam Witteveen, Machine Learning Developer Expert [[Google]] | ||
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+ | | valign="top" | | ||
+ | {| class="wikitable" style="width: 550px;" | ||
+ | || | ||
+ | <youtube>OypPjvm4kiA</youtube> | ||
+ | <b>How I'm Learning AI and Machine Learning | ||
+ | </b><br>For the past 6 months or so, I have been teaching myself about artificial intelligence. In this video, I describe some of the places I learned from and a few of the things I've done with my new found knowledge. Lots of my AI code: https://github.com/unixpickle/weakai | ||
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+ | |}<!-- B --> | ||
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+ | {| class="wikitable" style="width: 550px;" | ||
+ | || | ||
+ | <youtube>JJCq21Dc-Us</youtube> | ||
+ | <b>Learning AI and [[ChatGPT]] isn’t that hard | ||
+ | </b><br> | ||
+ | |} | ||
+ | |<!-- M --> | ||
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+ | {| class="wikitable" style="width: 550px;" | ||
+ | || | ||
+ | <youtube>FfQ90Q8czzQ</youtube> | ||
+ | <b>How to Learn [[Deep Learning]] (when you’re not a computer science PhD) | ||
+ | </b><br>Talk video from meetup April 11, 2017 at [[Amazon|AWS]] office in SF. Huge thanks to Amazon for providing venue, food/drink, and video recording! Abstract: Many people claim that [[Deep Learning]] needs to be a highly exclusive field, saying that you must spend years studying advanced math before you even begin to attempt it. Jeremy Howard and I believed that this was just not true, so we set out to see if we could teach [[Deep Learning]] to coders (with no math prerequisites) in 7 part-time weeks. Our students are now using [[Deep Learning]] to identify chainsaw noise in endangered rain forests, create translation resources for Pakistani languages, reduce farmer suicides in India, diagnose breast cancer, and more. We wanted to help them get results fast, so we taught them in a code-centric, application-focused way. I’ll share what we learnt about how to learn [[Deep Learning]] effectively, so that you can set out on your own learning journey. [[Creatives#Rachel Thomas|Rachel Thomas] | ||
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+ | {| class="wikitable" style="width: 550px;" | ||
+ | || | ||
+ | <youtube>Cr6VqTRO1v0</youtube> | ||
+ | <b>Learn Machine Learning in 3 Months (with curriculum) | ||
+ | </b><br>How is a total beginner supposed to get started learning machine learning? I'm going to describe a 3 month curriculum to help you go from beginner to well-versed in machine learning. Its an accelerated learning plan, something i'd create for myself if I were to get started today, but I'm going to open source it for you guys. This curriculum will cover all the math concepts, the machine learning theory, and the [[Deep Learning]] theory to get you up to speed with the field as fast as possible. If anyone asks how to best get started with machine learning, direct them to this video! | ||
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+ | || | ||
+ | <youtube>waXHrc2m9K8</youtube>> | ||
+ | <b>How to Study Machine Learning | ||
+ | </b><br>Let me show you the techniques I use to study machine learning in this video. That includes living a healthy lifestyles, optimizing your learning environment, creating a personalized learning path, prioritizing effectively, and being an active learner. I'll demo the FAST technique, which you can use to help learn faster and more efficiently. I made this with machine learning technology in mind, but these techniques can be used for any field. Enjoy! | ||
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Latest revision as of 21:35, 26 April 2024
YouTube ... Quora ...Google search ...Google News ...Bing News
- How do I leverage Artificial Intelligence (AI)? ... Reading/Glossary ... Courses/Certs ... Education ... Help Wanted
- Arxiv Sanity Preserver to accelerate research
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Train, Validate, and Test
- Python ... GenAI w/ Python ... JavaScript ... GenAI w/ JavaScript ... TensorFlow ... PyTorch
- Gaming ... Game-Based Learning (GBL) ... Security ... Generative AI ... Games - Metaverse ... Quantum ... Game Theory
- Development ... Notebooks ... AI Pair Programming ... Codeless ... Hugging Face ... AIOps/MLOps ... AIaaS/MLaaS
- AI Transformation Playbook | Andrew Ng - Landing AI
- Association for The Advancement Of Artificial Intelligence
- Need Help Getting Started with Applied Machine Learning? | Jason Brownlee - Machine Learning Mastery ...These are the Step-by-Step Guides that You’ve Been Looking For!
- Tackle all your AI development needs | Upwork
The future is already here — it’s just not very evenly distributed. - William Gibson
Contents
How to Monetize AI and Machine Learning Solutions
There are several ways you can make money in AI:
- AI Consulting and Integration Services: Offer consulting services to help businesses understand how AI can benefit their operations. You can offer consulting services to businesses that want to leverage AI but don't have the expertise or resources to do so. As an AI consultant, you can help businesses identify opportunities for AI, develop AI strategies, and implement AI solutions. Provide guidance on AI strategy, implementation, and integration into existing systems.
- Training: You can offer training services to individuals or businesses that want to learn more about AI. This could include online courses, workshops, or one-on-one training sessions.
- Freelancing: You can offer your AI skills as a freelancer on platforms like Upwork, Freelancer, or Fiverr. There are many businesses and individuals looking for AI expertise on these platforms, and you can offer your services for a fee.
- Data Monetization: If you have access to valuable and well-curated data, you can offer data-driven insights, trends, or predictions to businesses willing to pay for this information. You can offer data labeling services to businesses that need high-quality labeled data for their AI models. This involves manually labeling data to improve the accuracy of machine learning models.
- Personalization and Recommendations: Use AI algorithms to deliver personalized content, recommendations, or targeted advertising, which can attract businesses looking to enhance user engagement and increase conversions.
- Process Automation: Implement AI-driven automation solutions to streamline and optimize business processes for other companies. This could include automating customer support, data entry, or supply chain management, leading to increased efficiency and cost savings.
- Partnerships and Collaborations: Collaborate with existing companies to integrate AI capabilities into their products or co-create AI-driven solutions for mutual benefit.
- AI in IoT (Internet of Things): Combine AI with IoT devices to create smart and autonomous systems for home automation, industrial applications, or healthcare monitoring.
- AI-driven Products or Services: Develop AI-powered products or services that address specific needs or pain points in the market. These could be AI-based software applications, chatbots, virtual assistants, recommendation systems, or predictive analytics tools. You can create AI-powered products or tools that solve specific business problems. For example, you could create a chatbot that automates customer support or an analytics tool that uses machine learning to identify trends and patterns in data.
- AI as a Service (AIaaS): Offer AI capabilities as a service to other businesses. This could involve providing access to pre-trained machine learning models, natural language processing (NLP) APIs, or computer vision algorithms that they can integrate into their own products or workflows.
- Subscription-based AI Services: Offer subscription plans for access to premium AI features, datasets, or AI-powered tools on a recurring basis.
- Market Research: Provide AI-powered market research and consumer behavior analysis for businesses seeking valuable insights.
- Social Media: Use AI to analyze social media data for sentiment analysis, trend identification, or targeted advertising.
- Content Creation: Utilize AI to generate content, such as automated article writing, video editing, or music composition.
- Virtual Reality (VR) and Augmented Reality (AR): Combine AI with VR and AR technologies to create immersive and intelligent virtual experiences.
- Music and Entertainment: Use AI to compose music, create personalized playlists, or enhance audio and visual effects in entertainment content.
- Content Creation: Utilize AI to generate content, such as automated article writing, video editing, or music composition.
- - Specific industries, AI in... -
- Sports Analytics: Provide AI-driven sports analytics services to sports teams and organizations for performance optimization, player scouting, and fan engagement.
- Healthcare: Develop AI-powered healthcare solutions, such as medical image analysis, disease diagnosis, drug discovery, or personalized treatment plans.
- Healthcare Management: Create AI applications for hospital resource management, patient flow optimization, or medical appointment scheduling.
- Personalized Medicine: Offer AI-driven solutions for precision medicine, including genetic analysis and personalized treatment recommendations.
- E-commerce: Implement AI-driven product recommendations, personalized shopping experiences, or AI-powered customer service for e-commerce platforms.
- Finance: Create AI-driven financial tools, like robo-advisors for investment management, fraud detection systems, or credit risk assessment models.
- Personal Finance: Offer AI-powered budgeting tools, financial planning, or expense tracking applications for individuals.
- Gaming and Entertainment: Build AI-powered features for gaming, such as intelligent NPCs (non-playable characters), dynamic storylines, or procedural content generation.
- Education: Develop AI-based educational platforms that offer personalized learning paths, automated grading, or intelligent tutoring systems.
- Marketing and Advertising: Utilize AI to enhance marketing campaigns through better audience targeting, content optimization, or social media analytics.
- Cybersecurity: Develop AI-powered cybersecurity solutions to detect and prevent cyber threats, malware, and data breaches.
- Agriculture: Create AI applications for precision agriculture, including crop monitoring, yield prediction, and automated farming equipment.
- Transportation: Build AI systems for autonomous vehicles, traffic optimization, route planning, or predictive maintenance for transportation companies.
- Supply Chain and Logistics: Develop AI-based solutions for inventory management, demand forecasting, route optimization, or supply chain risk analysis.
- Real Estate: Use AI to analyze property data and provide insights for real estate investments, property valuation, or personalized property recommendations.
- Human Resources: Offer AI-based recruitment and talent management tools, employee sentiment analysis, or automated HR processes.
- Energy Management: Develop AI solutions to optimize energy consumption, predictive maintenance for energy infrastructure, or demand forecasting for energy providers.
- Environmental Monitoring: Develop AI solutions for environmental data analysis, climate modeling, or wildlife tracking and conservation.
- Government and Public Services: Develop AI applications to improve public services, such as traffic management, waste management, or emergency response systems.
AI will be the greatest wealth creator in history. ... It’s going to destroy barriers
- Matt Higgins, a self-made millionaire, CEO of investment firm RSE Ventures and guest star on ABC’s Shark Tank
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Example Patterns to Leverage
There are many ways to leverage AI to improve your work, business, and life. Here are a few common patterns/categories where any AI Use Cases falls irrespective of industries.:
- Generative AI/Conversational AI: When a machine establishes a conversation with human in natural way. Example, ChatGPT, Siri, Alexa, Cortana. Natural Language Processing (NLP) is a branch of AI that deals with the interaction between computers and human language. By leveraging NLP, you can use AI-powered chatbots to interact with customers and provide support or use Sentiment Analysis to gauge customer feedback.
- Assistants, Personal Companions & Agents: Enhance our lives through intelligent support, personalized assistance, and efficient task management.
- Speech Recognition: Speech recognition uses AI algorithms to convert spoken language into text. You can use speech recognition to automate transcriptions, create voice-activated assistants, or improve accessibility for users with disabilities.
- Recommendation Engines: Hyper Personalization. Example Product, News, Content. AI-powered recommendation engines analyze user behavior and data to provide personalized recommendations. You can use recommendation engines to suggest products, services, or content based on a user's interests and preferences.
- Predictive Analytics: Uses machine learning algorithms to analyze historical data and make predictions about future events. Examples - Sales forecasting, Inventory forecasting, Demand planning, optimize supply chain management
- Recognition: Find an object or detect any pattern from any kind of data. Example Object detection, computer vision, Video/Image processing, voice recognition, and Facial Recognition. Image and video analysis use AI algorithms to recognize and interpret visual data. You can use this technology to automatically tag and organize images or videos, detect and track objects, or even identify faces for security or surveillance purposes.
- Anomaly Detection: To find patterns from data and establish relation between those to find outliers. Example risk analysis
- Fraud Detection: AI can help you detect and prevent fraud by analyzing patterns and anomalies in financial transactions. By leveraging AI-powered fraud detection tools, you can identify fraudulent activity in real-time and take appropriate action.
- Automation: Autonomous cars/Drones, Autonomous operation. AI-powered automation can streamline and optimize business processes by automating repetitive or time-consuming tasks. For example, you can use AI-powered tools to automate customer support, financial analysis, or inventory management. Autonomous systems use AI to make decisions and take actions without human intervention. You can leverage autonomous systems to automate tasks such as driving, manufacturing, or monitoring and control systems.
- Augmented Reality and Virtual Reality: AR and VR use AI to create immersive and interactive experiences. You can use AR and VR to create virtual product demonstrations, training simulations, or marketing campaigns.
Getting Started
- Kaggle
- Reading/Glossary ... Courses/Certs ... Podcasts ... Books, Radio & Movies - Exploring Possibilities ... Help Wanted
- Starting a Career in Artificial Intelligence | Reece Johnson - Best Colleges
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Roadmaps
Overall, roadmaps can be a valuable tool by providing a clear and concise overview of the learning journey.
Machine Learning (ML) Roadmap
- Interactive Machine Learning Roadmap
- Machine Learning Roadmap Resources
- The Zero to Mastery Machine Learning Course
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Large Language Model (LLM) Roadmap
- Large Language Model (LLM) ... Multimodal ... Foundation Models (FM) ... Generative Pre-trained ... Transformer ... (GPT-4) ... GPT-5 ... Attention ... GAN ... BERT
The following subjects are derived from Ryan Nguyen's excellent 'How AI Built This' article, "This is what I'd do if I could learn how to build LLM from scratch where he asks the question, "What if start from absolute zero, knowing what I know now? Where would I begin? How would I tackle each challenge?" ...
- Introduction to Large Language Model (LLM)s
- Attention Mechanism ... The emergence of LLM
- Transformer Models
- Embeddings
- Vector Database
- Foundation Models (FM)
- Context
- Let's build a Generatively Pretrained Transformer (GPT)
- Fine-tuning
- Train Large Language Model (LLM) From Scratch
- In-Context Learning (ICL)
- Semantic Search
- Prompt Engineering (PE)
- LlamaIndex
- LangChain
- Retrieval-Augmented Generation (RAG)
- Assistants ... Personal Companions ... Agents
- Large Language Model Operations (LLMOps)
How to Get into AI
Mental ResilienceBuilding your "mental resilience" as you head into a world where you will need to continue to reinvent yourself to leverage/compete with artificial intelligence - continually making yourself relevant. To do so, you need to gain data science expertise; again an understanding how best leverage artificial intelligence/machine learning capabilities and applications.
Watch me Build a ...
Learning Approaches
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