ML important because of the explosion of data- we need automated systems that can make sense and respond to changes in the data
ML is everywhere-
AI is now an expected feature in new products- personalized, insightful, and self-correcting
Developing AI models has becoming easier with tools such as TensorFlow
AI is using data(training) to answer questions(predictions)
Training- we use our data to fine-tune predictions of a model, that can answer questions about previously unseen data.
Associated article "What is Machine Learning?" → https://goo.gl/Dbxo6M
Create a model to determine if a drink is wine or beer.
2 features: Color, alcohol%
Step 1: Gather data samples- quality and amount of data will determine accuracy of model
Step 2: Data Preparation-
· put gathered data into a table,
· randomize the order,
· clean up data,
· equal amounts of samples, normalize( all data on same scale)
· split data into training data set and test set (80/20)
Step 3: Choosing a model-
There are various models depending on the application
We have a 2-feature system- small layer model
Step 4: Training the model
Model: Y= m*X +b
where x is data, m is slope, and b is y intercept(bias)- training the model consists of finding optimal values for m and b
w= weights matrix (slope)
b= bias matrix
we start with random values for m and b, then adjust them depending upon the resulting errors
Step 5: Evaluation
Model is tested on new data(test set)
Step 6: Adjust Hyper parameters, Learning Rate( amount parameters are adjusted on each step
Step 7: Make predictions using new data