Review of Python Courses (Part 10)

In Part 9, I summarized my Datacamp courses 25-27. Today I will continue with the next three.

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

My course #28 was Supervised Learning with scikit-learn. This course covers:

• Supervised learning
• Exploratory data analysis [pd.plotting.scatter_matrix()]
• The classification challenge (creating arrays, from sklearn.neighbors import KNeighborsClassifier)
• Measuring model performance (from sklearn.model_selection import train_test_split, datasets)
• Introduction to regression (from sklearn.linear_model import LinearRegression)
• The basics of linear regression
• Cross-validation (from sklearn.model_selection import cross_val_score)
• Correlation
• Simple regression (from scipy.stats import linregress) and its limits
• Regularized regression (from sklearn.linear_model import Ridge, Lasso)
• How good is your model (from sklearn.metrics import classification_report, confusion_matrix)?
• Logistic regression and the ROC curve (from sklearn.metrics import roc_curve)
• Area under the ROC curve
• Hyperparameter tuning (from sklearn.model_selection import GridSearchCV)
• Hold-out set for final evaluation
• Preprocessing data [pd.get_dummies(df)]
• Handling missing data (from sklearn.preprocessing import Imputer, from sklearn.pipeline import Pipeline)
• Centering and scaling (from sklearn.preprocessing import scale, StandardScaler)

My course #29 was Introduction to Natural Language Processing in Python. This course covers:

• Introduction to regular expressions
• Introduction to tokenization (from nltk.tokenize import word_tokenize, sent_tokenize)
• Advanced tokenization with regex
• Charting word length with nltk
• Word counts with bag-of-words (from collections import Counter)
• Simple text preprocessing (from nltk.corpus import stopwords, from nltk.stem import WordNetLemmatizer)
• Introduction to gensim (from gensim.corpora.dictionary import Dictionary)
• Tf-idf with gensim (from gensim.models.tfidfmodel import TfidfModel)
• Named entity recognition
• Introduction to SpaCy
• Multilingual NER with polyglot (from polyglot.text import Text)
• Classifying fake news using supervised learning with NLP
• Building word count vectors (from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer)
• Training and testing a classification model with scikit-learn (from sklearn.naive_bayes import MultinomialNB)
• Simple NLP, complex problems

My course #30 was Building Chatbots in Python. This course covers:

• Introduction to conversational software (respond function, sleep method from time module)
• Creating a personality
• Text processing with regular expressions
• Understanding intents and entities (re.compile)
• Word vectors
• Intents and classification (from sklearn.svm import SVC)
• Entity extraction
• Robust NLU with Rasa (from rasa_nlu.converters import load_data)
• Virtual assistants and accessing data
• Exploring a DB with natural language
• Incremental slot filling and negation
• Stateful bots
• Asking questions and queuing answers
• Frontiers of dialog technology

I will review more classes next time.