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Discussion Road to AIAP: Mastering the Art of Productivity and Unwavering Motivation

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meldrick_wee
(@meldrick_wee)
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In this forum post, I'd like to share the strategies I've personally employed for productivity and maintaining motivation during my preparation for AIAP.  Additionally, I'm eager to hear from fellow AI/ML practitioners about the techniques they've found successful in their own learning journeys. Let's exchange ideas and learn from each other's experiences to enhance our collective growth in this dynamic field of data science.
 
During my preparation for AIAP, I was balancing a full-time job, personal life, attending night classes for a graduate diploma in analytics, and self-studying data science. This necessitated not only careful planning and discipline but also the drive to persevere through challenges. I quickly realised that to achieve success in my independent learning journey, it would be crucial to implement productivity techniques to make every study session count and maintain motivation to fight burnout.
 
When I first delved into the realm of machine learning algorithms, my ambitious goal was to cover four topics every week. However, I soon encountered obstacles in staying on track and achieving my weekly goals, primarily due to distractions and a tendency to procrastinate. I found myself habitually reaching for my phone to check messages, mindlessly scrolling through social media, or even playing with my cat — doing anything but focusing on the topic I had intended to study.
 
Determined to overcome this productivity roadblock, I embarked on a quest to research and experiment with various strategies to break free from these unproductive habits. Through a process of trial and error, I identified several effective techniques that helped me regain control of my study sessions and make the most of my independent learning journey.
 
To combat procrastination, I adopted a "just start" mindset. Instead of overthinking, I set achievable, bite-sized goals, such as committing to just 15 minutes of focused study. This brief yet purposeful investment of time often served as a catalyst, propelling me into the much sought-after "zone" or "flow" state where learning became enjoyable and highly productive. This approach often led to gaining momentum, and I found the motivation to study for more extended periods. I discovered that breaking the initial barrier of resistance was the key to fighting procrastination.
 
To minimize distractions, I created a dedicated study space in my home. Everytime I needed to study, I would sit at the exact same spot. To further eliminate potential interruptions, I powered down my mobile devices and diligently gathered all essential study resources in advance, ensuring everything I needed was within reach. I always had a cup of invigorating coffee and a bottle of water next to me to keep me hydrated and alert throughout my study sessions. Additionally, I made it a habit to visit the washroom before settling down to study, further eliminating any reason to leave my seat during the session.
 
By incorporating these thoughtful strategies, I was able to create an environment that fostered uninterrupted focus and significantly boosted my productivity. These seemingly small adjustments ultimately had a profound impact on my overall learning experience. I found myself fully engaged in learning, free from distractions, and able to make meaningful progress in mastering the subjects at hand.
 
During your learning journey, there may be moments when your motivation to study wanes. This could be due to the burnout from managing both work and studies or the overwhelming complexity of data science topics. I, too, have faced these challenges, and I am happy to share some valuable tips that have personally helped me overcome these obstacles and reignite my passion for learning.
 
Firstly, I went beyond the traditional to-do list by creating a priority list. I began by writing down all the tasks I wanted to accomplish in a week and then re-ordered them from the most to the least important. This method allowed me to concentrate my efforts on the most crucial study tasks for that week, ensuring I made meaningful progress.
 
Secondly, I refined my study goals by breaking them into smaller, highly specific tasks. For instance, instead of listing "Study K-Nearest Neighbors algorithm" as a to-do item, I would break it down into more focused tasks like "Understand the four distance measures in the KNN algorithm" and "List the pros and cons of the KNN algorithm." This approach made my goals more manageable, leading to a greater sense of accomplishment as I completed each task.
 
Lastly, I sought to establish a support network comprising like-minded individuals passionate about data science. I was fortunate to have a close friend who was also preparing for AIAP, and together, we shared study materials and held each other accountable. To connect with others, consider utilizing this forum to form study groups, or join data science communities like Kaggle, where you can interact with fellow enthusiasts and learn from their experiences. By building a support network, you can leverage the power of collective motivation and knowledge to further your learning journey.
 
In conclusion, the journey of independent learning can be both challenging and rewarding. By employing effective productivity strategies and staying motivated, you can set yourself up for success. Remember, as the famed motivational speaker, Zig Ziglar, once said, "You don't have to be great to start, but you have to start to be great." So, take the first step, embrace the challenges, and allow your passion for learning to guide you.
This topic was modified 2 months ago by meldrick_wee

   
victorgoh, Tai, Ella (aka Wati) and 2 people reacted
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(@darrenljw)
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Your post resonates with my experiences in learning AI removed link I'd like to add another technique I've found effective in my AI/ML learning journey: the Pomodoro Technique. This time-management method involves breaking study sessions into focused 25-minute intervals (called "Pomodoros") with short 5-minute breaks in between. After completing four Pomodoros, take a longer 15-30 minute break to recharge. This approach helps me maintain focus, avoid burnout, and track my progress more efficiently.


   
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meldrick_wee
(@meldrick_wee)
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@darrenljw Thanks for sharing your experience! I've personally tried the Pomodoro Technique as well, but I found that the standard length of time felt too short and sometimes interrupted my "flow" state. So, I experimented and discovered that breaking my study sessions into 90-minute intervals worked better for me. Everyone learns differently, so it's essential to explore various methods to find what works best for each individual.


   
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Tai
 Tai
(@tainc)
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Hey Meldrick, congrats on your achievement! It sounds like you've been on quite the journey. I can totally relate to your struggles. When I was getting ready for my AIAP program, I had a tough time finding the time and motivation to dive into such a foreign subject. And with all the endless options available now, it was easy to get stuck in a never-ending analysis paralysis loop, making it even harder to just start. (Andrew Ng's updated Coursera course definitely was a good introduction.)

But, you're right. The key is just to have the right mindset and take that first step, no matter how small. Even if you only have 15 minutes to spare, it's better to do something than nothing. And forget about trying to find the "perfect" course, lecture, textbook, or problem. As long as you start, you'll gain momentum and keep going.

In addition to having the right frame of mind, like Darren mentioned, I have found the Pomodoro technique to be very helpful in chunking my time and attention. Personally, like Meldrick, I've also come to be more flexible with the timings, extending the session and skipping breaks when needed. When you're coding, that's when you're in the zone (which often lasts for more than the standard 25 minutes), and you don't want to lose that momentum!


   
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(@benjaminp)
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@darrenljw Good morning! I use the pomodoro technique pretty extensively too. Like what was mentioned, one of the adjustments that I made was to ensure that I am flexible with it. If I am in the proverbial zone, I tend to just ignore the timer and go on. I'm going to guess that you've heard of Barbara Oakley's course?

One of the key benefits was the idea of having a firm idea of being "done" after the 25 minutes is up, it gets that racing against the clock feeling that seems to help me focus. Most websites which have the pomodoro implementation usually also offer the ability to write a small description of the pomodoro, this allows me to track my time and it provides good feedback as to whether I am over or underestimating the efforts of a particular tasks.

That being said, some of the tasks are also living at the bottom of my list for months now...😔


   
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 Rger
(@rger)
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@meldrick_wee bite size learning is what i adopted 5,6 years ago, the technique is stable and very enduring.  Juggling full time job (i'm an accountant), pursuing my programming hobby and training swimming daily means I too have to be a time management guru.  I've been learning nodejs for 5 months before i received an aiap invite and i just click it.  I realised i have to know python, as I am in the midst of learning nodejs, which i find its more fun to learn.  But since tech is developing so fast, and new languages are being interfaced regularly in new systems, just do it. The full time job is taxing too, so I adopted Dawyne Johnson's tranining routine, wake up at 4.30am, and spend 1 - 2 hours learning programming, before my morning swim, sleep at 9+ night before.  Now this routine is what I am used to, its slow, but the memory sticks longer with me.  Yes, sticking to a habitual corner helps me concentrate and focus too.  I think after the morning swim, my body is rejuvenated for the day's work again.  Why I am into this, is because I realised I am more excited developing and automating tasks then doing accounting itself, my motivation in programming starts there. AI seems to be the next progression of automation, no idea how it ends.   


   
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meldrick_wee
(@meldrick_wee)
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@rger

Your enthusiasm for learning and self-discipline to stick to a goal is inspiring! It's great to see that you're open to exploring new languages and technologies, even when it means transitioning from Node.js to Python. AI indeed seems to be the next progression in automation, and while we can't predict exactly how it will all unfold, it's exciting to be a part of this ever-evolving and extremely dynamic field.

Keep up the amazing work, and I wish you the best of luck in your journey. If you ever have any questions or would like to share your experiences, feel free to reach out here on the forum. We're all here to learn and grow together!


   
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