Review of Python Courses (Part 32)

In Part 31, I summarized my Datacamp courses 92-94. Today I will continue with the next three.

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

My course #95 was Dimensionality Reduction in Python. This course covers:

• Introduction
• Feature selection vs. feature extraction
• t-SNE visualization of high-dimensional data (from sklearn.manifold import TSNE)
• The curse of dimensionality (from sklearn.model_selection import train_test_split; from sklearn.svm import SVC)
• Features with missing values or little variance (from sklearn.feature_selection import VarianceThreshold)
• Pairwise correlation (hide correlation matrix redundancy)
• Removing highly correlated features
• Selecting features for model performance (from sklearn.feature_selection import RFE)
• Tree-based feature selection (from sklearn.ensemble import RandomForestClassifier)
• Regularized linear regression (from sklearn.linear_model import Lasso)
• Combining feature selectors (from sklearn.linear_model import LassoCV)
• Feature extraction
• Principal component analysis (from sklearn.decomposition import PCA)
• PCA applications (from sklearn.pipeline import Pipeline)
• Principal component selection

My course #96 was Writing Efficient Python Code. Topics covered in this course include:

• Defining efficient
• Building with built-ins
• The power of NumPy arrays
• Examining runtime (%timeit)
• Code profiling for runtime [pip install line_profiler; %lprun -f foo(args)]
• Code profiling for memory usage (import sys, pip install memory_profiler)
• Efficiently combining, counting, and iterating (from collections import Counter; from itertools import combinations)
• Set theory
• Eliminating loops
• Writing better loops
• Intro to pandas dataframe iteration
• Another iterator method: .itertuples() [faster than .iterrows()]
• Pandas alternative to looping (use .apply() on an entire dataframe)
• Optimal pandas iterating (use .values to get array rather than series)
• Final tips

My course #97 was Machine Learning for Finance in Python. This course covers:

• Predict the future (e.g. stock price changes) with machine learning
• Data transforms, features, and targets (import talib)
• Linear modeling with financial data
• Engineering features (from sklearn.model_selection import ParameterGrid)
• Decision trees (from sklearn.tree import DecisionTreeRegressor)
• Random forests (from sklearn.ensemble import RandomForestRegressor)
• Feature importances and gradient boosting [np.argsort(); from sklearn.ensemble import GradientBoostingRegressor]
• Scaling data and KNN regression (from sklearn.preprocessing import scaler)
• Neural networks (from keras.models import Sequential; from keras.layers import Dense)
• Custom loss functions (import tensorflow as tf; import keras.losses)
• Overfitting and ensembling (from keras.layers import Dropout; from sklearn.metrics import r2_score)
• Modern Portfolio Theory (MPT) and efficient frontiers (review this complex code involving covariance)
• Sharpe Ratios, features, and targets
• Machine learning for MPT

I will review more courses next time.