Self-help for spiritual seekers
Here’s a comprehensive list compiled by Class Central. Have fun! If you don’t know where to start from I suggest that you check out the free Manning ebooks, those are great. Also check out the list of 85 free Coursera courses with certificates and 365 data science free courses with certificates. No credit cards required for any of these purchases.
Free Online Learning Due to Coronavirus (Updated Continuously)
I was reading the book called Range: Why Generalists Triumph in a Specialized World. It’s written by David Epstein- an investigative reporter. Prior to becoming a reporter, he studied environmental science and astronomy. He rose to prominence by leveraging his science education into writing amazing sports articles.
Epstein noticed a phenomenon in sports stars which he later found in successful people from most other fields. The people at the top aren’t extremely focus on their area of expertise. Rather, they often invest significant time and effort in many other related or unrelated fields.
He starts his book with the contrast between Roger Federer and Tiger Woods. Tiger is obsessively focused whereas Roger dabbled into many other sports before focusing on tennis exclusively.
He explains this disparity in two of the world’s greatest sport stars by demonstrating the difference in their sports.
Apparently, domains of learning can either be wicked or kind. It is wicked when clear patterns don’t repeat themselves overtime. The stock market is wicked, so is tennis and most other sports. Whereas it is kind when clear patterns emerge constantly. Chess and golf are kind domains of learning.
Most of what we do as humans are wicked. That is why we have already built AI systems that beat the world’s bests in Chess, but most other domains of activity still elude them. Because, AI systems thrive mostly when there are repeating patterns to work with.
People with expertise in different domains show creativity, flexibility and adaptiveness. They use knowledge/skills from multiple domains to solve novel challenges.
As we prepare for AI to take over most of our jobs, it is the wicked domains of work that will still need humans. And those of us who has a good grasp on multiple domains of knowledge/skills, will thrive.
I’ve been interested in machine learning now a days. It fascinates me.
I think some of the best careers right now are in medicine, finance and technology. And if I break finance more the most careers in finance are analytical type jobs like market research analyst and best kind of tech jobs are like machine learning and data scientist.
Both data science and machine learning(ML) require programming knowledge and can be used together to help each other. Python seems to be an industry favorite as the programming language to do both of them. The best thing about Python is its easy but is as useful as anything else.
So, what is machine learning? Machine learning is to let computer systems learn from its environment and experiences without any prior explicit programming. That’s an over simplified answer but I guess it’s enough for the discussions here.
Self-driving cars, speech recognition, image recognition etc. are fruits of machine learning. Machine learning let’s machines achieve human level expertness in various jobs. Impressive huh! In some fields machines are even beating humans now ( which is a little scary I guess).
Algorithmic trading, portfolio management, fraud detection etc.
Planning, budgeting and forecasting, operational accounting, inter-company transactions, financial reporting, sentiment analysis etc.
Machine learning can free up time for the people in finance department so they can contribute more in strategic decision making.
If anyone is interested to check machine learning out I’d recommend two sources:
If you have any query please comment. Also I’d love to hear suggestions about courses to take my knowledge further, as I’m new to the programming world.
RaihanRiad 