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best source to learn ai and programming?

best source to learn ai and programming?

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Doc’s Answer

Valarie proficiency in AI programming languages is just one facet of becoming an accomplished AI Programmer. In today’s cloud-centric world, you must also be well-versed in cloud services and infrastructure management.

PYTHON - Python stands at the forefront of AI programming thanks to its simplicity and flexibility. It’s a high-level, interpreted language, making it ideal for rapid development and testing, which is a key feature in the iterative process of AI projects. The real strength of Python in AI comes from its extensive libraries and frameworks like TensorFlow, PyTorch, and Keras, which offer pre-built functions and classes that significantly speed up the development of complex algorithms.

JAVA - Java’s object-oriented approach makes it a strong candidate for AI projects, especially in large-scale, enterprise-level applications. Its platform independence is a major plus since it allows AI systems developed in Java to be easily transferred across different environments without the need for recompilation. One of Java’s notable strengths in AI is in the growing field of natural language processing and neural networks. Programming libraries like Deeplearning4j offer robust tools for creating complex neural network structures. Java’s strong memory management and high-performance garbage collection mechanisms ensure efficient handling of the large data sets typical in AI.

PROLOG - Prolog, which stands for Programming in Logic, is a language designed for AI’s more specific needs, particularly in symbolic reasoning, problem-solving, and pattern matching. Unlike imperative languages that follow a sequence of commands, Prolog is declarative, focusing on the relationship between facts and rules. In the field of AI, Prolog is extensively used for developing expert systems, natural language processing, and proving theorems. Its strength lies in its pattern matching and automatic backtracking features, which are useful in developing complex AI algorithms that involve rule-based logical queries. Prolog’s use also extends to the broader Semantic Web, information retrieval from large databases, and the creation of expert system shells for specialized problem-solving.
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Luke’s Answer

There isn't a single "best" source to learn AI and programming, as the best approach depends on your learning style, goals, and prior experience. Here are some excellent options to consider:

Online Courses and Platforms:

Coursera: Offers a wide range of courses on AI and programming, from beginner to advanced levels. Many courses come from top universities and industry leaders. (https://www.coursera.org/)
edX: Similar to Coursera, edX provides a variety of AI and programming courses from prestigious institutions. Some courses offer certificates upon completion. (https://www.edx.org/)
Udacity: Known for its project-oriented approach, Udacity offers Nanodegree programs in various AI and programming fields. These programs are more intensive but can provide valuable skills for your career. (https://www.udacity.com/)
DataCamp: Offers interactive tutorials and data science career tracks that include introductions to AI and machine learning. A great option for those who enjoy a hands-on learning approach. (https://www.datacamp.com/)
Khan Academy: Provides a free introduction to computer programming, which can be a helpful foundation before diving into AI. (https://www.khanacademy.org/computing/computer-programming)
Books and Tutorials:

Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig: A widely used university textbook offering a comprehensive introduction to AI concepts. A good choice for those who prefer in-depth learning.
Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron: A practical guide using popular Python libraries for machine learning tasks. Assumes some programming experience.
Grokking Deep Learning by Andrew Trask: A visual approach to understanding deep learning concepts, making them more accessible to beginners.
Additional Resources:

Online communities: There are many online forums and communities dedicated to AI and programming. These can be a great way to ask questions, get help, and connect with other learners.
YouTube channels: Several YouTube channels offer tutorials and lectures on AI and programming. These can be a good option for visual learners who prefer video instruction.
MOOCs (Massive Open Online Courses): Many universities offer free or low-cost MOOCs on AI and programming topics. Check out platforms like Coursera and edX for these offerings.
Here are some tips for choosing the best resources for you:

Consider your learning style: Do you prefer a structured learning environment like online courses, or do you learn better through hands-on projects or reading books?
Start with the basics: If you're new to programming, it's important to build a foundation in core programming concepts before diving into AI.
Set realistic goals: Don't try to learn everything at once. Set achievable goals and gradually build your knowledge and skills.
Practice consistently: The key to mastering any skill is consistent practice. Dedicate time to working on projects and applying what you learn.
Remember, the best source is the one that keeps you engaged and motivated to learn. Explore different options, find what works for you, and enjoy the journey of learning AI and programming!
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Shayla’s Answer

Like many have pointed out, your learning style and technical background play a big role. From what I've seen, having a degree can make you a more appealing job candidate in any field. Attending a recognized school or program is also a smart move. It lets you connect with others who share your interests, learn from top professionals, and eventually land a job.
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Nathan’s Answer

Learning AI is all about utilizing the tools available to you through the process of trial and error. You should use anthropic, open AI, hugging face, llama, and anything else available to you in the latest versions in order to hone your skills.

I highly recommend huggingface as a place to learn about how to apply AI to coding - there are great code snippets and a community for AI enthusiasts.
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Giacomo’s Answer

Prestigeous universities
And company appreciate
Thank you comment icon Thanks for answering, Giacomo! Do you have any universities you can recommend for Valarie to explore? Sharyn Grose, Admin
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Swaroop’s Answer

Stanford's Machine Learning Specialization, led by Andrew NG, is the finest foundational course I've encountered. It does require some basic programming skills. If you lack these, Coursera offers courses to assist you in developing them.

The course is available in both paid and free versions. With the paid version, you have the privilege of submitting answers, interacting, and posing questions to mentors, among other things.

Here is the link to the course: https://www.coursera.org/specializations/machine-learning-introduction?utm_medium=sem&utm_source=gg&utm_campaign=B2C_NAMER_machine-learning-introduction_stanford_FTCOF_specializations_country-US-country-CA&campaignid=685340575&adgroupid=46849728719&device=c&keyword=andrew%20ng%20machine%20learning&matchtype=b&network=g&devicemodel=&adposition=&creativeid=606098666387&hide_mobile_promo&gad_source=1&gclid=CjwKCAjw88yxBhBWEiwA7cm6pVW3GEUiZbX_gEhRSDfe4HywtalV09sYIZxCVUmpXRXyRiqWf7Vv1RoCQk8QAvD_BwE#outcomes.
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Rory’s Answer

If you're ready to dive headfirst into the thrilling realm of Artificial Intelligence (AI) and coding, you're about to embark on an exhilarating adventure! Let's check out some top-notch resources to jumpstart your exploration:

DataCamp’s Master Guide on AI Learning:
DataCamp presents an all-inclusive guide to learning AI from the ground up. This master guide encompasses everything you'll need, from beginner's tips to valuable resources, and wisdom from industry gurus. Whether you dream of becoming a data scientist, machine learning engineer, AI researcher, or just an AI aficionado, this guide is your one-stop solution.
Uncover topics like understanding natural language, recognizing patterns, decision-making, and learning from experience. Plunge into the captivating realm of AI and uncover its vast potential.
Complimentary AI Training Courses:
Giants like Microsoft and Google offer complimentary AI training courses. These courses are perfect for establishing basic knowledge and hands-on skills. In the current job landscape, AI knowledge is a prized possession, and these courses can give you a competitive edge.
Python and TensorFlow:
Python is a flexible language extensively employed for AI and machine learning tasks. You can find numerous practical AI-centric projects on platforms like GitHub.
TensorFlow, a free-to-use machine learning library created by Google, is another potent resource. Delve into its vast codebase to learn about AI principles and enhance your skills.
Online AI Courses:
There's a wealth of online AI courses at your disposal. Some top contenders include:
Stanford Artificial Intelligence Professional Program: Perfect for learning how to train AI.
Udacity’s Artificial Intelligence Nanodegree program: Concentrates on mathematics and theory.
Other online platforms like Coursera, edX, and Udemy also provide AI courses. Browse through their offerings to find the one that suits your learning preferences.
Always remember, the voyage into AI is both thrilling and fruitful. Whether you're deciphering neural networks, delving into deep learning, or building smart chatbots, embrace the challenge and relish the journey!
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Chase’s Answer

There isn't a single source of truth for learning AI & Programming, but a few resources could leverage are:

Online Courses:
Coursera (courses from top universities like Stanford, DeepLearning.AI by Andrew Ng)
Udacity (Nanodegree programs in AI, Machine Learning, Deep Learning, etc.)
edX (Courses from MIT, Harvard, and other renowned institutions)
Udemy (A wide range of courses, including AI and programming tutorials)


Online Resources:
YouTube (Tutorials, lectures, and conferences on AI and programming)
Stack Overflow (Q&A platform for programming-related queries)
GitHub (Open-source projects and code repositories)
AI/ML blogs (e.g., KDnuggets, Towards Data Science, Machine Learning Mastery)
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