Machine Learning is like human learning new knowledge. Human picks up new knowledge through experience, machines pick up new knowledge with data.
Machine Learning aims at building algorithms, and algorithms can be divided into 2 categories: Supervised Learning and Unsupervised Learning.
"Typical Linear Regression: Using X to predict Y, classic formula is: Y= aX + b(where a is a calculated weight for X, and B is a constant)"
"Logistic Regression would return a number between 0 and 1, which is actually probability. Typical use case: banks predicting how likely is a customer able to repay the loan"
The key difference between Supervised Learning and Unsupervised Learning is involving labeled data or not. In Supervised Learning, labeled data is used to train the algorithm and valid the results; In Unsupervised Learning, data is not labeled and input to the algorithm directly.
"Nearest neighbors clustering, the goal here is to find the similarity and to group them into certain categories"
Deep Learning has a lot of connections with Machine Learning, but they are not exactly the same thing in practice.
Machine Learning focus on tabular data, and Deep Learning handles untraditional data such as images, text, voice recordings. Deep Learning algorithms work on computer vision, speech recognition, NLP (Natural Language Processing) and so on.
Importance of the relation:
If you’re opening a bakery, it’s a great idea to hire an experienced baker well-versed in the nuances of making delicious bread and pastry. You’d also want an oven. While it’s a critical tool, I bet you wouldn’t charge your top pastry chef with the task of knowing how to build that oven; so why are you focused on the equivalent for machine learning??
If you’re innovating in recipes to sell food at scale, you need people who figure out what’s worth cooking / what the objectives are (decision-makers and product managers), people who understand the suppliers and the customers (domain experts and social scientists), people who can process ingredients at scale (data engineers and analysts), people who can try many different ingredient-appliance combinations quickly to generate potential recipes (applied ML engineers), people who can check that the quality of the recipe is good enough to serve (statisticians), people who turn a potential recipe into millions of dishes served efficiently (software engineers) and so on it goes.
"Remember without decision-making fundamentals, your decision will be at best inspired by data, but not driven by it"