Review of Python Courses (Part 30)

In Part 29, I summarized my Datacamp courses 86-88. Today I will continue with the next three.

As a reminder, I introduced you to my recent work learning Python here.

My course #89 was a case study in Python machine learning. This course covers:

• Introducing the challenge
• Exploring the data
• Looking at the datatypes (converting dtype for all dataframe categories)
• How do we measure success?
• It’s time to build a model (from sklearn.linear_model import LogisticRegression)
• Making predictions (from sklearn.multiclass import OneVsRestClassifier)
• A very brief introduction to NLP
• Representing text numerically (from sklearn.feature_extraction.text import CountVectorizer)
• Pipelines, feature and text preprocessing (from sklearn.pipeline import Pipeline, FeatureUnion)
• Text features and feature unions (from sklearn.preprocessing import FunctionTransformer, Imputer)
• Choosing a classification model (from sklearn.ensemble import RandomForestClassifier)
• Learning from the expert: processing
• Learning from the expert: a stats trick (from sklearn.preprocessing import PolynomialFeatures)
• Learning from the expert: the winning model (from sklearn.feature_extraction.text import HashingVectorizer)
• Next steps and the social impact of your work

My course #90 was Ensemble Methods in Python. Topics covered in this course include:

• Introduction to ensemble methods (from sklearn.ensemble import MetaEstimator)
• Voting (from sklearn.ensemble import VotingClassifier, VotingRegresssor)
• Averaging
• The strength of “weak” models
• Bootstrap aggregating
• Bagging classifier: nuts and bolts
• Bagging parameters: tips and tricks
• The effectiveness of gradual learning
• Adaptive boosting: award winning model (from sklearn.ensemble import AdaBoostClassifier, AdaBoostRegressor)
• Gradient boosting (from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor)
• Gradient boosting flavors (import xgboost as xgb; import lightgbm as lgb; import catboost as cb)
• The intuition behind stacking
• Build your first stacked ensemble
• Let’s mlxtend it (from mlxtend.classifier import StackingClassifier; from mlxtend.regressor import StackingRegressor)!

My course #91 was Data Analysis in Spreadsheets. This course covers:

• First function: ROUND
• Function composition: SQRT
• Functions and ranges: MIN, MAX
• Selecting ranges: SUM, AVERAGE, MEDIAN
• Multiple arguments: RANK
• String manipulation: LEFT, RIGHT
• String information: LEN, SEARCH
• Combining strings: CONCATENATE
• Date functions: WEEKDAY
• Comparing dates
• Combining functions
• Flow control: IF
• Nested logical functions: IF

I will review more courses next time.