Machine Learning & Data Science Algorithmic Programming Section
Algorithmic Quantitative Python - R programming for Data Science
Python & R programming
Quant Algorithmic Programming
Using hypothesis generation engineering, naïve bayes, random forest models and multiple machine learning algorithms used for prediction.
Python programming
Python Algorithmic programming
Python using Numpy, Pandas, Matplotlib, scikit-learn, Keras, Tensorflow, all of this used for Algorithmic Quantitative programming.
Python programming
Python Algorithmic programming
Supervised/Unsupervised learning, Classification, Clustering, Association Detection, Anomaly Detection, Dimensionality Reduction, Regression.
Python & R programming
Quanta Algorithmic Programming
Naive Bayes, K-nearest neighbours, Support Vector Machines, Artificial Neural Networks, K-means, Hierarchical clustering, Principal Components Analysis
Python programming
Python Algorithmic programming
Python using Linear regression, Logistics regression, Random variables, Bayes theorem, Bias-variance tradeoff .
Python programming
Python programming
Python using Alpha and Beta coefficients, regression’s explanatory power R^2, Markowitz Efficient frontier, Sharpe ratio, Monte Carlo simulations .
R programming
R Algorithmic Programming
Using Data Preprocessing, Linear Regression, Logistic Regression, Support Vector Machines, K-Means Clustering, Ensemble Learning, Natural Language Processing, Neural Nets.
Python programming
Python Algorithmic programming
Python using pandas-datareader and Quandl, Pandas Time Series Analysis Techniques, Volatility and Securities Risk, Efficient Frontier and Markowitz Optimization.
Python programming
Python Algorithmic programming
Python using EWMA (Exponentially Weighted Moving Average), Statsmodels, ETS (Error-Trend-Seasonality), ARIMA (Auto-regressive Integrated Moving Averages), Auto Correlation Plots and Partial Auto Correlation Plots, Sharpe Ratio .