Data Science & Deep Learning for Business™ 20 Case Studies

Use Python to solve problems in Retail, Marketing, Product Recommendation, Customer Clustering, NLP, Forecasting & more!

Created by: Rajeev D. Ratan
Data Scientist, Computer Vision Expert & Electrical Engineer

Created by: Nidia Sahjara, AI/ML Engineer | Lonely Pineapple AI Labs
Machine Learning Engineer | NLP Researcher

What Will I Learn?

• Understand the value of data for business
• Solve common business problems in Marketing, Sales, Customer Clustering, Banking, Real Estate, Insurance, Travel and more!
• Python, Pandas, Matplotlib & Seaborn, SkLearn, Keras, Tensorflow, NLTK, Prophet, PySpark, MLLib and 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!
• How to apply Data Science in Marketing to improve Conversion Rates, Predict Engagement and Customer Life Time Value

Requirements

• Familiar with basic programming concepts
• Highschool level math knowledge
• Broadband Internet connection

Target audience

• Begineers to Data Science
• Business Analysts who wish to do more with their data
• College graduates who lack real world experience
• Business oriented persons (Management or MBAs) who'd like to use data to enhance their business
• Software Developers or Engineers who'd like to start learning Data Science
• Anyone looking to become more employable as a Data Scientist