Image slides Hi! I’m Galuh. • Data Scientist at Gojek • Google Dev Expert in Machine Learning • Co-host podcast Kartini Teknologi (kartiniteknologi.id)
if pixel[5][7] is black and pixel [5][6] is black and pixel [5][8] is black and …: if pixel[6][7] is black and pixel[6][7] is black and …: return “panda” … … … else: return “cat” Photo credit: Photo by Damian Patkowski on Unsplash
if pixel[5][7] is black and pixel [5][6] is black and pixel [5][7] is black and …: if pixel[6][7] is black and pixel[6][7] is black and …: return “panda” … … … else: return “not cat” Photo credit: Photo by Dušan Smetana on Unsplash
Adapted from Introduction to ML Problem Framing Define a machine learning problem Construct & transform your dataset Train a model Use the model to make predictions
Define a machine learning problem Construct & transform your dataset Train a model Use the model to make predictions Adapted from Introduction to ML Problem Framing
Define your ML problem Type of problem Description Example Classification Pick one of N labels Cat, dog, horse, or bear Regression Predict numerical values House price Clustering Group similar examples News articles grouped into categories (unsupervised) Ranking Identify position on a scale Search result ranking Adapted from Introduction to ML Problem Framing
Define your ML problem Type of problem Description Example Classification Pick one of N labels Cat, dog, horse, or bear Regression Predict numerical values House price Clustering Group similar examples News articles grouped into categories (unsupervised) Ranking Identify position on a scale Search result ranking Adapted from Introduction to ML Problem Framing Example: “our problem is best framed as a classification problem, which predicts whether a picture will be in one of the four classes: cat, dog, horse, or bear.”
Define a machine learning problem Construct & transform your dataset Train a model Use the model to make predictions Adapted from Introduction to ML Problem Framing
What is a “model”? - A model maps examples to predicted labels - It is defined by weights that are learned during the training process - Once trained, you can use it to make predictions about data that it has never seen before Model Data Predictions
A very simplified example Iteration 1: 2*number of floors + 3*area size = predicted house price Model Data Predictions House #1: predicted: 200 million actual: 500 million difference: 300 million
A very simplified example Iteration 1: 2*number of floors + 3*area size = predicted house price Model Data Predictions Iteration 2: 4*number of floors + 6*area size = predicted house price House #1: predicted: 400 million actual: 500 million difference: 100 million
Example of neural network in TensorFlow model = tf.keras.models.Sequential( [ tf.keras.layers.Flatten(), tf.keras.layers.Dense(512, activation=‘relu’), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation=‘softmax’) ])
Adapted from Machine Learning Crash Course High Loss Low Loss - Arrows represent loss - Blue lines represent predictions How does a model get better? Loss
How do we know that our model is good enough? Metric - Evaluation metrics: • Accuracy • Mean Absolute Error • Root Mean Squared Error • … and more Actual Spam Actual Not Spam Predicted Spam 15 10 Predicted Not Spam 5 30 Accuracy: (Correctly classified spam emails + correctly classified not spam emails)/total emails = (15 + 30)/(15+10+5+30) = 75%
Define a machine learning problem Construct & transform your dataset Train a model Use the model to make predictions Adapted from Introduction to ML Problem Framing
Define a machine learning problem Construct & transform your dataset Train a model Use the model to make predictions Adapted from Introduction to ML Problem Framing
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