Learn from the masters!

What we can learn from the masters
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They are all still in order of completion. I live in Brisbane, Australia. It took me five years to do a three-year undergraduate degree.

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I thought I wanted to be a doctor. Probably because I was following what my friends were doing rather than creating my own path. And I love food. Marrying my love for food and fitness with my studies was one of the best I ever did. I learned how to learn. I was still under the impression studying would always be a painful task. Wisdom must be learned not taught. This whole revolution of Artificial Intelligence AI fascinates me. AI, coupled with other technologies has the power to make the world a better place.

It also has the power to the world a worse place. Advancements in AI have been happening for decades. Computing power has only recently reached a stage where it is accessible enough to enable people like me to work on these technologies from my bedroom. Rather than stand by and watch this paradigm shift happen without fully understanding it, at the start of , I decided to start learning about it.

I went in reverse when I began my studies.

Learn From the Masters

I dived straight in the deep end literally and signed up for a Deep Learning course without ever writing a single line of Python code. I scared myself to the point I emailed support asking whether I was eligible for a refund. After learning more about the field, I matured a little and started to get an idea of where I want to take my studies. More thought is starting to go into where I spend my time. My focus is a foundation of knowledge I can use to build things. If you have any advice for me, including courses I should look at or skills I should work on, please feel free to let me know in the comments, my email or Twitter.

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At the beginning of , I had no idea what data science was, let alone AI. I wrote my first line of Python code in February.

Henri Cartier-Bresson

These courses are to give me a foundation across Python programming, Computer Science and Data Science. I had a slight interest in AI and machine learning but his introductory videos on YouTube got me hooked. This was the first course I started with. Sometimes a more traditional route is needed rather than always being in front of a screen. The last time I did a statistics course was the first semester of my undergraduate degree.

I failed. Naked Statistics has helped me start to think with more of a statistics mindset. Hands-on Machine Learning is another incredible resource. This book is most aligned with what I do day to day as a Machine Learning Engineer.

Lessons you can learn from Henri Cartier-Bresson:

I wish I had this book when I started out. You can read it in a day. My high school math skills needed some refining once I started learning about Machine Learning and Deep Learning.

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Thanks to various frameworks and libraries, much of the math work is done behind the scenes. Having a general understanding of the math that goes into these technologies has helped me immensely. My personality type is ENFP. Too long in front of a computer screen and I get weird. Siraj Raval is a wizard. Watching his videos led to me documenting my learning journey on YouTube. But I figured it out the hard way.

The Learning How to Learn course on Coursera should be a compulsory course for every student.

Learning from the Masters

Learning How to Learn is the ultimate skill because you can apply it to anything else. The use of cloud technologies is a requirement for any machine learning engineer or data scientist. If you want people to use the things you make, you have to distribute them. Cloud computing helps you do that. Shared common workspace is also available for students to form study groups, work on project teams and socialize. In addition, students gather regularly for brown bag discussions to share knowledge and research findings. Whereas some aspects of the program are fixed e.

As the program progresses, students are given increasing options for electives, and they join research teams composed of faculty and PhD students, allowing for in-depth research experience. In addition to extensive course offerings, research projects provide invaluable opportunities for student participation in innovative investigations of learning and teaching in schools, workplaces and other settings.

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As part of the curriculum, students conduct research with faculty projects or, occasionally, with external organizations. The Learning Sciences MA and PhD programs are interdisciplinary programs in the School of Education and Social Policy drawing from cognitive science, education and computer science research. Through course work and research apprenticeships, students are exposed to the three major themes that permeate the research and theory in the learning sciences: sociocultural context, cognition and design. Examining the social, organizational and cultural dynamics of learning and teaching situations, including classrooms, schools, school districts, museums, corporations and homes.

Constructing scientific models of the structures and processes of learning and teaching by which organized knowledge, skills and understanding are acquired.