How can you quickly learn AI and data science basics?

Choosing goal is most important step, There are multiple career option one can opt in field of data science and analytics based on their passion and steepness of learning curve to get there. I will give brief details- 1. Data analyst- SQL, excel, quantitively apt - 6months-1 year 2. Business analyst-business domain, excel, some visualization- 9months-2 year 3. Data Engineer- Sql, python, scala, databricks/Mapreduce etc. 1-2 years 4. Data Scientist- Statistical fundamentals, business context, SQL/R/Python- 1-1.5 years 5. ML Engineer - mathematical/statistical foundations, computational algorithms, CI CD basics-2-2.5 years These are all basic requirements to get started, along with other soft skills and BAU skills needed. So pick wisely!

In my experience, selecting a problem to solve, whether in your personal or professional life, can be an excellent avenue for acquiring new skills. I have learnt several AI and automation skills successfully this way. For example, when confronted with manual or repetitive tasks/processes, my mind instinctively searches for opportunities to automate them. I have learnt several AI based solutions while solving these automation problems super quick and effectively.

You can identify specific areas within Data that interest you, such as machine learning, data analysis or data engineering. Come up with an outline of the steps and skills that you would need to follow or learn. Start with learning the basics then proceed to practicing with projects.

When i switched to Data Science, i tried to find the sub-domain of DS field that interests me, which made the transition exciting. So try to figure out the sub-domain by looking at some basic projects from channels like Kaggle or Medium or Analytics Vidhya and choose the sub-domain like Computer Vision, NLP, Statistical Machine Learning etc. and then complete a course diligently from channels like Udacity, EdX, Coursera, Udemy to begin with. Once done buy a traditional book and read the basics related to Machine Learning. Start solving problems in Kaggle and start reading technical papers from Google scholar. These will be stepping stones, rest steps one can figured out by themselves based on their interests & requirements.

As for learning anything you need to decide why you are learning it where and how it will be useful. Same is with Learning goals on Data Science and AI. In my experience accelerated learning is always linked to certain goal it may be knowledge, new job opportunities, to be better at what you already do etc. I call these as triggers if you have identified you trigger you will be focused and goal oriented which will lead to accelerated learning.

Getting started is fairly easy. There’s an abundance of resources available online. My recommendation would be to look for reputable experts teaching a course or having published a book. Individual learning styles will vary. But booksmarts alone won’t be sufficient to succeed in the field. Exercises, reviews, etc can really help include some practical applications.

If someone is really interested in data science, do a 1 year distance learning PG Diploma in Applied Statistics from IGNOU. It costs less than 10k INR and the books are apt. If you are able to understand those concepts well and clear the exams by submitting assignments timely, then you are 70 percent done. This will make you clear about the interest you have towards the subject and will also save money without compromising on quality. Next If you have time, clear the exam for MStat or 1 year diploma Stat course from ISI. It is tough to Crack and will also take your time. But it is pocket friendly and it will be a strategic move. If you don't have time, you may select coursera courses and complete them.

When it comes to quickly learning AI and data science basics, finding the right resources is key. Over the years, I've found that a structured approach can be incredibly helpful. Start by assessing your own learning style, whether you're a visual learner who prefers videos and infographics, an auditory learner who enjoys podcasts and lectures, or a kinesthetic learner who benefits from hands-on projects. Look for resources that align with your style, and importantly, ensure they are tailored to your current skill level and learning objectives. Seek out courses or materials that offer interactive elements like quizzes, exercises, and projects, allowing you to apply what you've learned and receive feedback with practise.

Coursera and edX are reputable online platforms and resources for quickly learning AI and data science basics. They offer expert-led courses, flexible pathways, and educational opportunities for all financial levels. With world-class outcomes, learners can study at their own pace, switch between courses, and earn certificates. The platforms provide a wide range of courses, hands-on projects, and certificate programs, ensuring comprehensive learning. Learners can check their understanding through feedback, quizzes, exercises, and apply their skills in real-world scenarios. Coursera and edX are the ideal solutions for efficiently mastering AI and data science basics.

The internet offers a wealth of materials, including online courses, books, blogs, podcasts, and videos, each varying in depth, approach, and complexity. It's important to choose resources that align with one's individual learning style, whether it's visual, auditory, or kinesthetic, and that cater to the specific skill level and goals. For me, I look for materials on AI that include interactive elements like quizzes, exercises, and projects, which are essential for me in reinforcing my learning and applying concepts in real-world scenarios.

--Grasping Core Theories: It’s crucial to have a solid grasp of these fundamental theories as they form the backbone of how AI functions. Understanding these concepts demystifies how AI algorithms process data and make decisions. --Using Diverse Learning Methods: I advocate a multimodal learning approach. Reading materials, engaging with interactive visual content like diagrams and animations, and utilizing analogies help in making complex concepts more digestible. --Practical Application: While theory is essential, applying these concepts through hands-on projects or exercises is equally important. This approach helps me in solidifying the understanding and seeing how theoretical knowledge translates into real-world applications.

I've always found that pairing the theory with relevant practice solidifies concepts learned. For example, if you're learning the theory of ML models such as SVMs, Naive Bayes, K-Means, etc., implementing one of them from scratch and seeing how it works on real data, can help you understand how machines learn and make decisions.

With the general availability of Large Language Models (LLM) like GPT-3.5, GPT-4, BARD, CLAUDE, etc., everyone can become a powerful AI user. If you know what needs to be done, all you need to do is ask these LLMs in plain english. Be it a formula in excel, or code in VBA, PowerQuery M, Python, JavaScript or HTML; LLM's are expert in providing you assistance through plain English conversation.

One beautiful way to remember and learn concepts is to create a mind map of the various terminologies and understand how they are interconnected. This will help us to demystify all the complex jargons and focus more on the core idea of any concept

In the dynamic landscape of data science and AI, it's crucial to navigate the evolving concepts effectively. 1. Focus on key aspects:Establish a robust foundation by mastering basic concepts, understanding limitations, and acknowledging challenges to discern optimal applications. 2. Gain a comprehensive overview of diverse domains such as NLP, reinforcement learning, computer vision, and time series analysis. Begin by cultivating expertise in one area. 3. Continuously expand your knowledge and stay abreast of trends within your chosen domain, ensuring a progressive and informed journey in this ever-evolving field.

Engaging in practical exercises, like debugging and testing, is essential to understand the nuances of programming for AI. Skills in data handling, including cleaning, manipulation, and analysis, are vital, and tools like NumPy, Pandas, and SQL can be instrumental. I try to sit in on any and all sessions with my engineers because it helps sharpen my strategy skills. Further, the ability to visualize data effectively using software like Matplotlib, coupled with model development skills using libraries like Scikit-learn, Keras, or PyTorch, is important as well.

I think diving straight into practicing a skill is the best way to learn even if we are talking about basics. Even running some basic ChatGPT API will unearth questions that you don't necessarily think about when reading about the topic. Python remains the language of choice for me as it is so easy to get started with. To that end I think knowing how docker works is essential to creating your own sandboxes without cluttering your personal laptop. The final step is then to ask yourself the question, what will this look like in production and appreciate the importance of frameworks in managing large projects.

The best way to learn is by doing, and to do this successfully you need to start small, build fast and share your work. Here is how it looks, start small means turning any idea into the smallest action item. If you start too big, the execution might be overwhelming. Pick a language, a simple dataset, a tutorial, and get started as fast as you can. Bonus: upload your work to GitHub. For sharing, leverage the “build in public” approach, which means sharing your milestones regardless of their size. •⁠ ⁠You finally created your first model, share it. •⁠ ⁠You created your first LLM Chatbot, share it. •⁠ ⁠You did XYZ, share it. This way you’re giving visibility to your journey, inspiring others, and becoming a subject-matter expert.

Select a bunch of small problems that you are interested in and see the process from start to end. You'll get quick exposure to the whole pipeline and become more familiar with each part. From here you have a platform to springboard into a larger project.

To master AI and data science, you've got to put your skills into action. Skills like coding in Python, cleaning data, and building models need practice. For instance, use Jupyter Notebook or Google Colab to work on projects. Get hands-on with tools like Pandas for data manipulation and Scikit-learn for model building. Practicing these skills on real problems is the key to becoming proficient in AI and data science.

Don't underestimate the power of a good community, especially when you're diving into something as big as AI and data science. When that first wave of excitement starts to chill out a bit, having folks around who get what you're going through can be a real game-changer. You might want to check out Discord channels for big names like OpenAI - it's pretty cool to see what projects get people fired up. Also, I've found success with local meetups. Nothing beats connecting with people in your area who are on the same journey. Remember, we're all in this together, and a little support goes a long way. So, keep at it and good luck!

A decade ago, in-person meetups were the best way to network and meet others who were doing what I wanted to be doing, and they were getting paid to do it, too! You'll meet your future coworkers, managers, and even your future hires this way. Online, ML Twitter is the best space for keeping an eye on the state of the art, and there are good subreddits for both research and learning that are active. Find your people. Ask them about their work and their research, and you'll accelerate your learning.

🧠 Networking and lifelong learning are essential in the field of AI and data science and joining a community is important to overcome challenges, learn from others or find opportunities. Online forums are a good option. The online platform Meetup includes several AI and data science meetups. ✅In person (non-remote) events on Meetup, only if you can attend the meeting venue in person. Depending on where you are located, it is possible that there may be some events in your city or nearby. ✅ Remote events on Meetup. It is possible to join from anywhere in the world 🌍 (just keep in mind the time in your time zone). ✅Hybrid events on Meetup. It may be possible to attend the event both in person or remotely.

Being surrounded by people with same interests who can mentor you, advise you, and guide you is probably the single biggest factor that will contribute to your success. Where to find them? Wherever you can. I'm a bit old school here but what works best for me is friends I make at work, conferences, and meetups.

Start tasks on GitHub to put your knowledge into practice and get suggestions from the community. this sentence is paraphrased in the most casual style possible: Join in the fun of data competitions on DrivenData or Zindi to test your skills on real problems. Check out online communities like DataCamp for interactive courses that teach you how to use data in real-life situations. Immerse yourself in practical lessons on Kaggle, a place where you can test your skills on real data sets. Also, check out blogs written by experts and take part in webinars and virtual meet-ups to get to know professionals in the field.

Tackling real-world problems is one of the most effective ways to learn data science and AI. Here's how to get started: 1. Use Kaggle as a resource: - Access genuine problems and datasets - Engage with a community for feedback 2. Explore public datasets: - There are plenty of public data sets available such as housing, transportation, and weather - Use these to practice 3. Create your own project: - Formulate important questions - Analyze data and build models to find answers 4. Share your findings: - Present your work to the community - Gather feedback for improvement 5. Benefit from the process: - Deepen your understanding of data science and AI concepts - Continuously improve your skills

I'm often asked how one can get started with real-world projects to apply the learnings. Here are two practical ways you can do this today: 1. Look for problems you can solve with AI in your current job. Even if your organization doesn't use data science, look for meaningful problems that you can solve on your own with data science. Scout for software licenses that you can leverage within your org or if they allow use of freely available open-source software. With persistence, you can find a way to apply AI and build a portfolio. 2. Look for crowdsourcing sites such as Kaggle that list real-world problems with actual datasets. This is as real as an industry problem can get (minus the data quality issue). Apply your learnings with this.

Getting started by working on problems that you see in real life is a good start for a beginner. Often times you will see that the idea you think is novel has actually been implemented by somebody before and that’s good for you because now you have a guide to follow in your implementation of it. In other cases, if your idea is novel, you can start your implementation of it and you find that people who are passionate about your project will help you out will help you out with that by contributing to it

To make the most out of your project, aligned with your goals. In what industry would you like to work? Is there a dataset available for that industry, and if so, can you build a model with it? These types of questions will also help you during your interview process, and can be the differential factor when making a hiring decision. You probably are empowered at this point, and eager to put your knowledge to the test, my advice to you is, don't struggle alone. In real-world scenarios, you will often have a team or a leader to consult. So don't be afraid to leverage engineering articles, papers, conferences, etc. Focus on finishing the project, and overcome any obstacle in your way to success. Bonus: document your obstacles and solutions.

For just starting out I would recommend using kaggle. It has certain advantages : You don't have to worry about setting up libraries, Jupiter notebook, private environments, dependency issues, downloading large models, downloading and accessing large datasets or just in short maintaining any software. Just go to the dataset section of kaggle and you can explore what methodologies other people used, you can learn from them, copy their notebooks, make changes to them, add new models and methodologies. The kaggle community is a good place for growth and you can find people who'll help you out, give advice and tips and will help you become proficient.

To learn basics of ai and data science, it is important to know, what comes under basics: 1. Maths 2. Statistics 3. Coding & ML Algorithms 4. Data analysis You start by studying some topics from maths and statistics, if these parts are good, you can easily relate to any ML or AI algorithms and understand them quickly. After that, you can move to coding it on any dataset. Hope it helps. You can follow this for studying any topics in AI/Data science.

To kickstart your learning journey in AI and Data Science, hone your skills in mathematics, statistics, Python/R/Java programming, knowledge of ML algorithms and SQL. Next, start with deep learning niches like CV and NLP followed by cloud technologies.

I cannot emphasize enough that have strong foundations in mathematics and coding(DSA, Python and Java language). Also, in addition, one should be passionate about AI and data science. As there will be times when you won't understand why an algorithm is not working and feel down. But this passion of yours will not let you stop. I have been through the process and it takes a lot of hours to understand the intricacies of AI/ML/DS. It is always an upward learning process where you have to define your career goal and stay focused on it.

First get a birds eye view of various things that come under the umbrella of data science. Then start taking baby steps to ace them one at a time. Quickly, you can grab what things exist however it will take some time to get used to the fundamentals and algorithms working. I would suggest start with articles on medium as they are written with a goal of making you understanding without overwhelming you. Then take up a Coursera course and enhance your learnings by doing projects. An amazing book to dive deep into the concepts - "ISLR" Happy learning journey:)