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10 years ago, Rober Downey Jr. playing Iron Man is what got me interested in Artificial Intelligence (AI). He is my inspiration for Machine Learning and AI. But first, What is AI? And how do Machine Learning (ML), Deep Learning (DP), and Natural Language Processing (NLP) come into AI?
ML, DP, and NLP are subsets of AI; Machine Learning is a subset of AI, Deep Learning is a subset of Machine Learning, and Natural Language Processing overlaps ML and DL. Natural Learning Processing uses all these techniques, among other things.
What is Machine Learning?
“Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed” Arthur Samuel, 1959.
The definition of Machine Learning hasn’t changed since 1959, but what has changed is the computing power and the way we handle the data.
What’s the difference between Machine Learning and Python? Traditional Programming works in a rule-based model, while in Machine Learning the program will learn itself from a set of data we provide.
The engineering definition of Machine Learning as defined by Tom Mitchell in 1997 is: “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.”
Ergo, it is a computer program that learns from the data that we provide. The data is the only information we have that we feed the program. With respect to some task T, that would be the solution we are trying to get. Performance measures P is related to the Machine Learning model itself. When we create a Machine Learning Model and train it with the data, we need to make sure it is trained correctly, hence this is where performance measures come in. It is the metric to analyze our model.
We create a model, we train the model with a set of data, then we use another set of data which helps the model predict the solution we are looking for. We improve the model by checking the performance measures.
Machine Learning can be seen everywhere. It’s in even your pocket! A lot of applications on your phones use Machine Learning; If you are using g-mail, when you type an email, there is an auto-suggestion to complete your sentence, that is machine learning. Even when you forget to write a subject line, Machine Learning suggests the best subject line for your email.
Amazon Go uses Machine Learning along with other technologies to allow a queue-less grocery store. There is no cashier. You scan a QR code, take what you need, and leave. The different technologies used to calculate the price of the things you picked up and they charge your wallet.
Netflix also uses Machine Learning by suggesting movies and shows based on what you previously watched. Over 75% of what people watch on Netflix comes from recommendations.
Airbnb uses Machine Learning for a lot of things too. It suggests the appropriate pricing for hosts and helps customers find the right place for them.
Machine Learning is everywhere. AI is the future. Whether you know it or not, you are already using Machine Learning.
When to use Machine Learning?
Types of Machine Learning
Types of Supervised Learnings
Types of Unsupervised Learnings
7 Step Machine Learning Approach
Application of ML
To analyze images there are certain models to solve these kinds of problems such as CNN.
We can solve these kinds of problems using Semantic Segmentation.
Why should you learn ML?
To learn more in depth about Machine Learning and all its advantages, watch the full video of the mini class below.
Register now for our Data Science Course and learn more.
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