what(specific/objective)? is machine learning
Why(reason/explanation)? we need machine learning
Where(position/place)? should we use machine learning
When(time/moment)? should we require machine learning
How(doing/functioning)? to do machine learning
“Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed. It focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.”
These statistical models are mainly used to do 2 things:
Prediction: make predictions about the future based on data about the past
Inference: discover patterns in data
Supervised Learning: The major use case is Prediction. We provide a set of
training data including the input and output, then train a model that can
predict output from an unseen input.
Regression:
Classification:
Unsupervised Learning: The major use case is Pattern extraction(inference). We provide
a set of data that has no output, the algorithm will try to extract the
underlying non-trivial structure within the data.
Clustering:
Dimentionality Reduction:
Regression:
How much or How many?
Classification:
is this A or B?
Clustering:
How is this organized
Dimensionality Reduction:
Is all these features are relevent to my problem
Association:
How these items are associated to each other.
Step 1: Define Problem
Step 2: Gathering Data
Step 3: Hypothesis Testing
Step 4: Pre-processing, Cleaning, Exploring and Transforming Data
Step 5: Model Building
Step 6: Model Evaluation
Step 7: Model Deployment
Resources: