Machine Learning (ML) 1
The fundamentals of Machine Learning - Concepts, Theories and examples will be covered in this series.
To start off, we’ll begin with a recap of the ML Workshop in the Google & Arctiq Event, as well as a brief overview of Machine Learning and the ecosystem.
Tip of the Iceberg
On June 28th, 2018,
The tip of the iceberg glistened bright-
but the foundation lay beneath hidden from sight.
If the Google & Arctiq Event was the tip of the iceberg, the foundation would be the hard work underneath to ensure that it was a success. As the days closed in, the Arctiq team //collaborated in building the environment, demos and workshop content.
On June 14th 2018, we received the final confirmation for the event - this left us two weeks to put everything together. Aside from our normal hectic workload, the event squeezed itself into our lives. However, far from being discouraged and overwhelmed- #slackchannels were buzzing with activity late into the night; meetings were held after hours to discuss and build the most relevant and engaging content to share. It was truly a #teameffort.
We decided to go for the divide and conquer approach- each person became a part of one or more of the following domains:
- Morning Track Demos
- IaaS Workshop
- Artificial Intelligence/Machine Learning Workshop
Aly Khimji and I formed a team to deliver the Machine Learning Workshop. We went over the fundamentals, made the math portion as fun as possible by coupling it with cool demos, gave out lots of exercises for hands on experience and even made a quiz in the end to test your knowledge and win AI powered real-time translation Google Pixel Buds!
It was a huge success, with attendees taking notes, asking questions throughout and engaging in discussions. Many people reached out afterwards -leaving their cards for future workshops/training. The tip of the iceberg glistened bright indeed.
Artificial Intelligence in the Modern World
With the rise of big data, faster processors, cheaper and increased storage capacity, innovative algorithms and reduced cost of technology infrastructure, Artificial Intelligence (AI) have become integrated into our daily lives.
- Have you ever noticed that as soon as you search for something on Google, Facebook almost simultaneously bombards you with advertisements of the same/related products?
- Sometimes before you even buy your new car, auto insurance ads start popping up.
- After you watch a new movie on netflix, a whole category of Movies You May Enjoy appears.
- Or what begun as a 1 video Youtube break about Machine Learning turned into a 2 hour session… and now you’re watching conspiracy theories on if aliens exist - seriously. I just tried this.
The other day, I used Google Assistant and noticed that it was getting smarter everytime.
ME: OK GOOGLE, call Bob* GOOGLE: I found three numbers under Bob. 1, 2.. GOOGLE: Which one would you like me to call? ME: 1 - mobile
After three times, Google stops asking and just calls the mobile number, learning my preferences.
We have Artificial Intelligence to thank for all of this.
Of course, Hollywood has profited as well with a large variety of films- which prompts some imaginative people worrying that intelligent machines will turn on us in a battle of the fittest leading to our doom- not likely. In reality, machines operate in parallel to statistical algorithms - overseen by machine learning engineers and data scientists. One of the biggest challenges delaying AI is an inadequate supply of people with the necessary training and expertise. If you’re waiting for a sign, this is it!
Machine Learning & The Ecosystem
We often hear the buzz terms #ArtificialIntelligence, #BigData, #DataMining, #MachineLearning, #DeepLearning uttered in the news and social media- but what do they all mean and how do they tie together?
Computer Science encompasses everything interrelated to the design and use of computers. Within this space is the next broad field of Data Science, which comprises methods and systems to extract knowledge and insights from data through the use of computers. Next is Artificial Intelligence, the ability of machines to perform intellectual and cognitive tasks.
Machine Learning overlaps with Data Mining a sister discipline that focuses on discovering and unearthing patterns in large datasets (Big Data). Where Machine Learning focuses on the incremental process of self-learning, Data Mining narrows its efforts on cleaning up large datasets to uncover valuable new insight. Deep Learning (which we will explore in detail in future posts) is a sub field of Machine Learning based on learning data representations, as opposed to task-specific algorithms using artifical neural networks (ANNs).
Machine Learning (ML) 2 - All About Data
In the next blog, we’ll dive into Data. We’ll explore the Machine Learning toolbox and concepts such as Data Scrubbing and Data Management. How can we validate how well our model performed? What is Training Data, Test Data and what are the best practices for splitting up the data set? Stay tuned to find out!