Data Science & Deep Learning for Business 20 Case Studies

Data Science & Deep Learning for Business 20 Case Studies - Use Python to solve problems in Retail, Marketing, Product Recommendation, Customer Clustering, NLP, Forecasting & more! Machine Learning from Linear Regressions (polynomial & multivariate), K-NNs, Logistic Regressions, SVMs, Decision Trees & Random Forests Unsupervised Machine Learning with K-Means, Mean-Shift, DBSCAN, EM with GMMs, PCA and t-SNE Build a Product Recommendation Tool using collaborative & item/content based Hypothesis Testing and A/B Testing - Understand t-tests and p values Natural Langauge Processing - Summarize Reviews, Sentiment Analysis on Airline Tweets & Spam Detection To use Google Colab's iPython notebooks for fast, relaible cloud based data science work Deploy your Machine Learning Models on the cloud using AWS Advanced Pandas techniques from Vectorizing to Parallel Processsng Statistical Theory, Probability Theory, Distributions, Exploratory Data Analysis Predicting Employee Churn, Insurance Premiums, Airbnb prices, credit card fraud and who to target for donations Big Data skills using PySpark for Data Manipulation and Machine Learning Cluster customers based on Exploratory Data Analysis, then using K-Means to detect customer segments Build a Stock Trading Bot using re-inforement learning Apply Data Science & Analytics to Retail, performing segementation, analyzing trends, determining valuable customers and more! To use Google Colab's iPython notebooks for fast, relaible cloud based data science work Welcome to the course on Data Science & Deep Learning for Business 20 Case Studies! This course teaches you how Data Science & Deep Learning can be used to solve real-world business problems and how you can apply these techniques to 20 real-world case studies. Traditional Businesses are hiring Data Scientists in droves, and knowledge of how to apply these techniques in solving their problems will prove to be one of the most valuable skills in the next decade!