You're curious about AI. How can you get started with learning algorithms?

To embark on your AI journey, begin by grasping foundational concepts such as linear algebra, probability, and statistics. Familiarize yourself with programming languages like Python. Dive into algorithmic paradigms, starting with fundamental ones like greedy algorithms and dynamic programming. Implement algorithms from scratch and practice on platforms like LeetCode. Engage with literature on machine learning and delve into specific areas like supervised learning, reinforcement learning, and neural networks. Utilize online courses and resources, participate in AI communities, and tackle real-world problems to enhance your algorithmic expertise.

Some ways to get started into the evolving AI field are. 1. Get your fundamentals right. To do that choose books and videos that teach the basics in laymen terms. Videos from LinkedIn Learning, MIT, IBM, Courser, Udemy to name a few. 2. Keep reading tech articles everyday from Linkedin, medium, kaggle, as much as possible. 3. Practice as you learn, Github and Colab are the best places to start with. Try some concepts, make mistakes, search them on google, post the questions in stack overflow, github issues and many more forums to get help. Understand what you are learning is right. 4. Surround yourself with people in the field of AI, datascience, computer vision, NLP, and multiple other sub fields of AI.

Starting with AI and learning about its algorithms involves building a foundational understanding, diving deep into specific algorithms, and then contributing back to the community. By following these steps, not only can you gain a solid grasp of AI concepts and algorithms, but you can also share your knowledge, helping to foster a learning community. Remember, the journey of learning AI is ongoing, and staying curious, persistent, and engaged with the community is key to your growth and success in the field.

The best way to get started is choosing a very simple and basic project on kaggle. Something like titanic problem or housing price prediction. I have found these easy to understand and simple to code.

To get started you must have some basic knowledge about data structures and data manipulation algorithms - know how to manipulate, visualise and find statistics based on your input data - know about different models and their different layers and their inputs and outputs - know how to identify and manipulate hyper parameters - calculate loss and optimise loss - learn to visualise matrices learn all the building blocks from simpler to complex

Start with the foundational concepts of programming such as variables, loops, arrays, and conditional statements. Understand how these elements work together to solve problems. After that you can try to learn basic libraries like pandas, Numpy, sklearn etc.

Start by familiarizing yourself with the foundational concepts of algorithms and data structures. Understanding topics like sorting algorithms, search algorithms, and basic data structures such as arrays, linked lists, and trees lays a solid groundwork for more advanced learning.

Once equipped with the basics, choosing a programming language tailored to your needs is paramount. Python, R, Java, and C++ are popular choices. Python, favored for AI, boasts simplicity, vast libraries, and a strong community. R excels in data analysis and visualization. Java and C++ offer advanced capabilities for enhanced performance and scalability in AI applications. Assess your project requirements to make the optimal choice.

Here are some basic learning goals for those starting in ML: 1. Master the fundamentals of algorithms and data structures. 2. Dive deep into statistics, graphical models, and advanced machine learning theory, including topics like model evaluation, hyperparameter tuning, and bias-variance trade-offs. 3. Explore a range of classical models e.g. GBDT, SVM, linear regression, as well as delve into neural networks like LSTM, RNNs, CNN, LM, and LLM. 4. Develop proficiency in programming languages and tools for machine learning and AI, such as Python, PyTorch, and Scikit-learn. 5. Acquire the skills to deploy machine learning models into production environments, including understanding code testing practices, model monitoring techniques.

A programmer should well aware about each and every basic data structures and their related algorithms. If basics is not clear then it's impossible to solve complex problems on DSA

To make hands-on practice on Algorithms I would recommend Python for beginners. Python is quite easy to learn and has lots of libraries which reduce your codes. You can also try R language to the next level.

Select a programming language to begin implementing algorithms. Popular choices include Python, Java, and C++. Python is often recommended for beginners due to its readability and extensive libraries for AI and machine learning.

I'd recommend starting with Python, due to its simplicity and compatibility/availability with top notch libraries and frameworks like Tensorflow or PyTorch. But if you prefer no-code options, you may want to try Amazon SageMaker or Google AutoML for example

Choose a programming language to experiment with algorithms and enhance your understanding of complex problems. For beginners, Python stands out as an excellent choice due to its simplicity and extensive library support across various areas in artificial intelligence. Subsequently, explore other languages like C++, C#, or Java based on your specific requirements and interests.

Python would be a good start and the most demanded nowadays. You may consider another option if you have previous experience (MATLAB, C, Java).

Dive into AI algorithms like linear regression, decision trees, and deep learning. Understand their principles and how they're implemented in AI applications.

Visualizations are a fantastic way to grasp the logic behind algorithms. They make abstract concepts more concrete by revealing how data is manipulated. Here's a tip: Explore videos and interactive simulations. These tools can show you how algorithms adjust parameters to fit each data point into the model while minimizing errors. This approach can solidify your understanding of the algorithm's decision-making process.

Dive into exploring various algorithms based on your chosen area of interest. Start with simpler algorithms like linear regression or binary search and gradually move on to more complex ones such as neural networks or genetic algorithms.

A way to get started is to dive into some of the foundational algorithms. Many of the state-of-art algorithms tend to build upon these. One way that works for me is to just search them online and watch some tutorial videos online.

Begin your exploration of algorithms by delving into simpler ones. You can conveniently find pre-implemented algorithms along with their datasets on platforms like GitHub. Engage in reading and comprehending the logic behind these straightforward algorithms, and challenge yourself by attempting to recode them.

The best way to learn algorithms is to practice coding them. Coding algorithms will help you reinforce your understanding, improve your skills, and test your knowledge. You can practice coding algorithms by using online platforms, tools, or environments that allow you to write, run, and debug your code. Some examples are Jupyter Notebook, Google Colab, Kaggle, Codecademy, and HackerRank. You can also practice coding algorithms by working on projects, challenges, or competitions that involve real-world data and scenarios. These will help you apply your learning to practical problems and learn from others.

It takes a hands-on approach to master algorithms; actually writing and solving problems helps you retain the principles, improves your programming skills, and verifies your theoretical understanding. Use cutting-edge resources like Google Colab, Jupyter Notebook, and websites like Kaggle, Codecademy, or HackerRank to practice coding in an interactive, real-time setting. Participate in real-world, data-driven projects, challenges, or competitions to put your theoretical knowledge to practical use. This will improve your problem-solving abilities and provide you the opportunity to gain insights from peers and real-world applications.

Practice implementing algorithms in your chosen language regularly. Leverage online platforms like LeetCode, HackerRank or Codecademy to solve algorithmic problems and gain hands-on experience. Don't shy away from tackling challenging problems; they help reinforce your understanding and problem-solving skills.

You can implement A.I in you studying/practicing routine by using a chat model like ChatGPT. It’s a great tool to bounce ideas off of. It can explain concepts that are not making sense to you. And it can give you coding problems that are tailored to your difficult level, while providing hints, tips, tricks when asked for them.

First step even before going towards learning algorithms, is about understanding the possibilities. Learn what are the types of models and what they can do and what they can't do. Also learn how to use them. This needs to be from the perspective of user not exactly in the sense of mathematics. No need to understand how algorithms internally work. Once we know how to use them automatically the perspective on developing would slow form

Stay updated with the latest advancements in AI and algorithms by following relevant blogs, research papers, and online courses. Continuous learning is crucial in this rapidly evolving field, so allocate time for self-study and exploration of new concepts and techniques

Keep following the relevant topics and keep an eye on the upcoming research in the area of interest. Learn to develop the API to convert the models to the applications.

Machine learning is a rapidly evolving field, with new algorithms, tools, and methodologies emerging regularly. To stay ahead, it's essential to cultivate a mindset of continuous learning. Engage with the ML community through forums, conferences, and online platforms like GitHub. Follow thought leaders, researchers, and practitioners in the field to keep abreast of the latest developments. Experiment with new techniques by participating in Kaggle competitions, contributing to open-source projects. Continuously challenge yourself to explore unfamiliar domains and tackle complex problems. Remember, the journey in machine learning is not just about mastering concepts but also about adapting to innovation and embracing lifelong learning.

Learn Programming: Start with Python. Study Data Structures: Arrays, linked lists, trees, etc. Understand Basic Algorithms: Sorting, searching, recursion. Practice Implementation: Code simple algorithms. Explore Machine Learning: Start with linear regression, decision trees. Work on Projects: Build small AI projects. Engage Online Communities: Join forums, GitHub. Read Books and Papers: "Introduction to Algorithms," research papers. Take Online Courses: Coursera, Udacity, Udemy. Stay Curious and Practice: Keep learning and experimenting.