Google Machine Learning Flashcards
AI
The ability of a digital computer to perform tasks that intelligent human beings perform
ML
Used by machines to make decisions based on data without getting specific instructions
Training Data
The data that creates the model for ML predictions
Model Training
The process of developing the a model for training data, with the goal of answering questions with the highest degree of accuracy
What are the seven steps of Machine Learning?
1) Gather the Data
2) Prepare the Data
3) Choose a Model
4) Training
5) Evaluation
6) Hyperparameter Training
7) Prediction
Gathering Data
Ensure you haven’t collected too much of any particular kind of data, split data into training (80%) and evaluation (20%), you may need to normalize or deduplicate data
Choosing a Model
Ensure you a picking a model that is suitable to the data that you want to collect
Training
Use X + W * Y + b where W is weight and b is bias. These values are manipulated to determine if predictions are accurate.
Evaluation
Once training is complete, model is evaluating based off test data against training
Hyperparameter Tuning
Fine tune your assumed parameters or hyperparameters to get higher accuracy
Prediction
Use your model for evaluation of data
Supervised Learning
Most common model, used when the training data and validation data is labeled and the task is learning how to set a label to input data.
Classification
A subclass of supervised learning, occurs when output data is a category (ex. apple, pear, orange)
Regression
A subclass of supervised learning, occurs when the output data is a value, such as cost and temperature
Unsupervised Learning
When training data is not labeled and model attemps to learn the structure of the data and export information or features that might be useful for classification. Accuracy can’t be measure but data can be moved into groups.
