or subscribe with
Join 3,500+ readers for one email each week.
Digests » 51
this week's favorite
Imagine that your team is meeting to decide whether to continue an expensive marketing campaign. After a few minutes, it becomes clear that nobody has the metrics on-hand to make the decision. You chime in with a solution and ask Amazon’s virtual assistant Alexa to back you up with information: “Alexa, how many users did we convert to customers last month with Campaign A?” and Alexa responds with the answer. You just amplified your team’s intelligence with AI. But this is just the tip of the iceberg.
You’ve just completed your first machine learning course and you’re not sure where to begin applying your newfound knowledge. You could start small by playing with the Iris dataset or combing through the Titanic records (and this is probably the first thing that you should do). But what’s more fun than jumping straight in and competing with random strangers on the internet for money?
Getting into machine learning, deep learning and artificial intelligence is not easy. These are all very cool and interesting topics, and they are being hyped lately, but like with software development, it is not for everybody. Some would say that if you are having a software development background, you are having a certain advantage. While this is true to an extent, this attitude can be a double-edged sword. Especially when it comes to math which supports all important concepts from these fields.
These days, nearly all the artificial intelligence-based products in our lives rely on “deep neural networks” that automatically learn to process labeled data.
I am a graduate of a master’s program and it has helped me get to where I am today; a manager of a data science team in a fortune 500 company. My company has had great success with hiring others from data science programs across the country, but recent rounds of interviews have highlighted a growing concern.