Review of Python Courses (Part 23)

In Part 22, I summarized my Datacamp courses 65-67. Today I will continue with the next three.

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

My course #68 was Linear Classifiers in Python. This course covers:

• Introduction (import sklearn.datasets)
• Applying logistic regression and SVM (general process, from sklearn.svm import LinearSVC)
• Linear decision boundaries
• Linear classifiers: prediction equations
• What is a loss function (from scipy.optimize import minimize)?
• Loss function diagrams
• Logistic regression and regularization
• Logistic regression and probabilities
• Multi-class logistic regression
• Support vectors
• Kernel SVMs
• Comparing logistic regression and SVM (from sklearn.linear_model import SGDClassifier)

My course #69 was Analyzing Social Media Data in Python. While I found this somewhat interesting, it seemed to incorporate as much JSON as it did Python. I have a hard enough time studying one new language—adding a second on top of that made things even more confusing for me:

• Analyzing Twitter data
• Collecting data through the Twitter API (from tweepy import Stream, OAuthHandler, API)
• Understanding Twitter JSON
• Processing Twitter text
• Counting words
• Time series
• Sentiment analysis
• Twitter networks
• Importing and visualizing Twitter networks (import networkx as nx)
• Node-level metrics
• Maps and Twitter data
• Geographical data in Twitter JSON
• Creating Twitter maps (from mpl_toolkits.basemap import Basemap)

My course #70 was Fraud Detection in Python. This course covers:

• Introduction to fraud detection
• Increasing successful detections using data resampling (from imblearn.over_sampling import RandomOverSampler)
• Fraud detection algorithms in action (from imblearn.pipeline import Pipeline)
• Review of classification methods
• Performance evaluation (from sklearn.metrics import precision_recall_curve, average_precision_score)
• More performance evaluation (from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score)
• Adjusting your algorithm weights
• Performance evaluation (from sklearn.model_selection import GridSearchCV)
• Ensemble methods (from sklearn.ensemble import VotingClassifier)
• Normal versus abnormal behavior
• Clustering methods (from sklearn.preprocessing import MinMaxScaler; from sklearn.cluster import MiniBatchKMeans)
• Assigning fraud versus non-fraud
• Other clustering fraud detection methods (from sklearn.cluster import DBSCAN)
• Using text data (from nltk import word_tokenize; import string)
• Text mining to detect fraud (from nltk.corpus import stopwords; from nltk.stem.wordnet import WordNetLemmatizer)
• Topic modeling on fraud (from gensim import corpora)
• Flagged fraud based on topics (import pyLDAvis.gensim for use with Jupyter Notebooks only)

I will review more courses next time.