Artificial Intelligence — Choosing The Correct Learning Approach Based On Your Current Role

Almost every other day, either one of my colleague, college friend or an online contact from LinkedIn/Twitter will ask me “I have been reading a lot of hype around artificial intelligence and machine learning, I tried to read some of the articles and watched some videos but I really don’t know where to start. Can you help or share something?”.

It is difficult to give a structured answer. Learning possibilities are endless. It is totally crazy to learn everything about artificial intelligence. This field is so wide that it is easy to hit a roadblock because you started learning it in the wrong way (difficult way) without assessing your readiness.

Some people i know are thinking of joining months-long online/on-campus education programs while others have bought online subscriptions on Udemy and edX because these were heavily discounted. Those who are at executive level are getting their understanding on artificial intelligence by reading newsy type articles on Forbes, MIT Technology Review and other technology blog sites.

Complex emerging technologies like artificial intelligence (AI) has potential to create entirely new business models. Sales, marketing, technology, support — leaders and employees from all domains, need to learn artificial intelligence — but differently.

How different learning approach and resources are application to different roles — let’s look at it here.

Different People, Different Levels, Different Domains, Different Learning Approach

Allow me to create three roles and chart a learning map around artificial intelligence skills they need to learn.

This article just shares high level learning approach and some visualizations as a QuickStart document.

1️⃣Role1: Executives and leaders — should be prepared to act quickly to make informed decisions on how to use artificial intelligence for their own business, or how to employ artificial intelligence to increase customer value propositions.

2️⃣Role2: Specialists from similar fields — having high level of information technology awareness around their domains, familiar with data, statistics and programming — but very new to artificial intelligence. Wants to do hand-on within a week after learning artificial intelligence.

3️⃣Role3: Experienced or fresh graduates — ready to transform/create a career out of artificial intelligence. People can spend around 20 hours per week in learning new skills for a career transformation.

1️⃣ Role1: Executive and leaders

https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/an-executives-guide-to-ai

In Mckinsey’s language, to prepare to act quickly executives need to stay ahead in artificial intelligence race. 🔗 Executive guide to AI will prepare leaders to understand what is artificial intelligence and machine learning. When to use which type and their business use cases.

Executive guide to AI

Web pages are intuitively designed with more visualization and less verbiage so that executives can retain most of the content without getting in to the depth of each type.

Most industries are covered against where it is appropriately applicable

Even if you want to go a little deeper you will see that each type had business use cases stated.

These business use cases are very beneficial and directly enables business leaders to further talk to his peers for more actionable information.

A Little Deeper 💥 — For Executives and Business Leaders — Good To Have

It is critical to know how machine learning works and what is its relationship with artificial intelligence. Business leaders need to know how different types of machine learning algorithms affect and solve various industry problems.

It is also important to know how it is different from traditional way of solving problems using various information technologies.

PWC has this infographics that depicts the difference and relationship — at a very high level, but good enough for executives.

2️⃣ Role2: Specialists From Similar Fields

These people having high level of information technology awareness around their domains, familiar with data, statistics and programming — but very new to artificial intelligence. Such learners can do (and should do) hand-on projects within a week after learning basics of artificial intelligence and machine learning.

These specialists have capacity to learn more and in depth. Many of the roles, needed skills and business titles of the future are unknown to us. So, make sure you assess your interests and future roles before you make some commitment to yourself.

As I stated in my earlier blog you can start with beginners, then intermediate, afterward moving on to the expert level courses. Make sure you complete all projects and assessments. After completing learning, you need to build a few small projects, and gradually you will figure it out what you like most, what you can directly relate to your area of expertise

The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data

http://scikit-learn.org/stable/tutorial/machine_learning_map/

2️⃣Hands-on on Azure Machine Learning Studio and Azure AI Gallery For Role2

Azure Machine Learning Studio has a large library of algorithms from the regression, classification, clustering, and anomaly detection families. Each is designed to address a different type of machine learning problem.

Azure AI Gallery enables Microsoft’s growing community of developers and data scientists to share their analytics solutions.

These will help you quickly build Azure AI Solutions from solution templates, reference architectures and design patterns.

Azure AI Gallery also has 🔗Jupyter Notebooks, integrated with Machine Learning Studio, provide a canvas for quickly running code, visualizing data, exploring insights, and trying out ideas

💥2️⃣Microsoft Azure Machine Learning Capabilities and Algorithm Cheat Sheet For Role2

Azure Machine Learning Cheat Sheet
Azure Machine Learning Studio Capabilities Overview

3️⃣Role3: Experienced or Fresh Graduates

If you are ready to transform/create a career out of artificial intelligence skills and can spend around 20 hours per week in learning new skills, then let me share that this would be the most useful investment of your time.

Artificial Intelligence is an enormous field. It consists of multiple disciplines and a variety of tools and platforms. There is no limit to what these AI techniques can be applied across numerous domains and industries.

You need to assess what are your career objectives and interests? Do you crave to join research or academia and contribute or you prefer to build some great product/feature and launch it within next 18 months?

You have two ways to learn these emerging technologies:-
1️⃣Learn it yourself — MOOC at Udemy, coursera or edX
2️⃣Official academic qualification like Ph.D.

Chose MOOC (massive open online course) only if you can set a routine to learn every day — for months, and enjoy making your hands dirty playing with various AI tools.

Chose Ph.D. if you want to contribute back to the evolution of emerging technologies. Be warned that it could be laborious and you might not get quick returns. Premium university education could cost you huge and you may have to live away from home.

Career development and learning, both are no more a one time activity, it is now a life-long progress, and unique path for each individual.
University degrees from premium institutes will be always important but new learning methods with engaging content and virtual rooms solving real-world problems will give rise to superior, autonomous and futurist workers.

💥Career in Artificial Intelligence and Organization View For Role 1️⃣, 2️⃣ & 3️⃣

Small or big, organizations can’t ignore artificial intelligence. They will have 🔗Center of Excellence dealing with various emerging technologies like artificial intelligence, machine learning, robotics, quantum computing, internet of things, cognitive computing etc. No matter, what your think of artificial intelligence but recent technological development associated with new patterns of globalization is threatening to create a new tomorrow — forcing businesses to re-imagine their products and services, realize business efficiencies and drive customer experience to an altogether different level. Be a part of this change take all possible initiatives to learn emerging technologies like artificial intelligence, robotics and machine learning.

Unlike any traditional approach a career in artificial intelligence can be realized in many constructs and in virtually all industries. Medical, military, art, research, manufacturing, marketing, finance, and transportation — all these industries need artificial intelligence skills. Another area of immense opportunities includes ethics, philosophy, policy making, and civic planning.

Industry changes are coming fast and we need to face the truth. You have this opportunity to re-invent your career by learning artificial intelligence. It will be challenging but at the same time highly rewarding for your future career