THIS MORNING, I SPOKE OUT LOUD TO A DEVICE IN MY kitchen, told it to call a Car to take me to the office. As I was getting ready, it warned me to leave early due to traffic congestion on the way. A car showed up a few minutes later, and my smartphone buzzed to let me know it had arrived. And in a few years, that car might very well be driving itself.
Everything is amazing, everything is horrible, and it’s all moving too fast. We are heading hurriedly toward a world shaped by technology in ways that we don’t understand and have many reasons to fear.
But History tells us technology kills professions, but does not kill jobs. We will find things to work on that we couldn’t do before. But now we can accomplish this with the help of today’s amazing technologies.
The era of Artificial Intelligence has arrived, and we need to respond to this new challenge
How to achieve this???
IT’S NOT AS HARD AS YOU THINK.
Here are some steps to follow:
1. WATCH THE LEADERS
2. UNDERSTAND WHAT HAS MADE THE AI DEVELOPMENT SO POWERFUL
3. FUTURE JOBS
4. DEVELOP SKILLS FOR FUTURE JOBS
- AI is s already an integral part of consumer and enterprise products of technology players like Google, Amazon, Apple and others.
- 10 largest tech companies have acquired 50 AI companies in the last 5 years, targeting facial recognition startups, chatbots, chip makers, and more.
- IDC forecasts that worldwide spending on AI hardware, software and services will jump to $58 billion by 2021, up from just $12 billion in 2017.
- As per Gartner’s survey of 3,160 CIOs from 98 countries, by 2020, 85% of CIOs will be piloting AI programs through a combination of buy, build and outsource efforts.
Would you like to learn the Foundations of Machine Learning and Data Science ?
ATA and DevOps++ Alliance is hosting a program on Machine Learing and Data Science Foundation, CP-ML & DS on 22nd, 23rd and 24th June in Mumbai
Key Highlights of this program :
- The World of Machine Learning
- Setting up Environment for Machine Learning
(Anaconda, Python introduction, Numpy, Panda, Matpotlib, case studies and relevant exercises)
- Exploratory Data Analysis
(Need of Data, pre-processing of data, boxplot to visualize data, data and column relationships)
- Simple Linear Regression
(Using scikit-learn for Machine Learning basic concepts of linear regression, its application on a data set, accuracy of linear model, scikit-learn usage)
- Multiple Linear Regression
(How to solve multiple linear regression use case, applying linear regression on different datasets, selecting best feature for accuracy)
- Classification using Logistic Regression
(Apply logistic regression to different datasets using scikit-learn, where logistic regression can be applied. )
Find out more about the program at : http://cpmldsf.devopsppalliance.org/