Intro to ML

In [5]:

    

# Define them, with examples


    

In [7]:

    

# Maths, Stats, Programming


    

In [8]:

    

# NlP, CV, RecSys, 
# Finance, health, social network


    

In [3]:

    

# Discuss Supervised, unsupervised, labelled, unlabelled, reinforcement, indirect interpretation,
# Generalised, non-genralised, lazy/active, whitebox,blackbox


    

In [4]:

    

# Talk about error function, loss function
# ML =f(x), eg: series 2,4,6,8,?,?,  # finding the best fitting function
# Max IG in  Decision Tree, MSE in LR


    

In [5]:

    

# Covered in previous session
# Explain the process of feature extraction, preprocessing, model selection, sklearn-workflow*(ext slide)


    

In [15]:

    

"""
data.split()
model.fit()
model.evaluate()
model.predict()
model.save()
"""


    

    
Out[15]:





'\ndata.split()\nmodel.fit()\nmodel.evaluate()\nmodel.predict()\nmodel.save()\n'

In [6]:

    

# Talk about A/B testing, load testing, user feedback, offline training, online scoring


    

In [7]:

    

"""
model.load()
model_api.predict_real-time(user'input)
"""


    

    
Out[7]:





"\nmodel.load()\nmodel_api.predict_real-time(user'input)\n"