- Day 1: Basics for handling and analyzing stock market data (Source)
- Day 2: Finding correlations between multiple stocks (Source)
- Day 3: Additive Models and Time Series Prediction with Prophet (Source)
- Day 4: Creating Dataset of 100 NSE Stocks
- Day 5: Web scraping location on U-Bahn Stations in Berlin
- Day 6: Predicting Breast Cancer - Data Exploration
- Day 7: Predicting Breast Cancer - KNN Classification
- Day 8: Predicting Breast Cancer - Random Forest Classification
- Day 9: Predicting Breast Cancer - Neural Network Classification
- Day 10: Predicting Breast Cancer - Model Evaluation
- Day 11 & 12: Data Visualization - Annotating data (Source)
- Day 13: Data Visualization - Making data visualizable
- Day 14: Data Visualization - Working with Tabular Data
- Day 15: Data Visualization - Working with Gridded Data
- Day 16: Data Visualization - Working with Collections of Data
- Day 17: Data Visualization - Network Graphs
- Day 18: Data Visualization - Geographical Data
- Day 19: Data Visualization - Networks and Geoviews
- Day 20: Data Visualization - Customer interactivity
- Day 21: Bayesian hypothesis testing (Source)
- Day 22: Bayesian multi-group comparison
- Day 23: Bayesian hierarchical modelling
- Day 24: Book recommender system
- Day 25: Customer lifetime value
- Day 26: Customer segmentation with RFM
- Day 27: Customer segmentation for online retail
- Day 28: Cross selling groceries
- Day 29: Predicting bike sharing demand
- Day 30: Finding sample size for study
- Day 31: Predict a building's Energy Star Score - EDA
- Day 32: Predict a building's Energy Star Score - Modelling
- Day 33: Predict a building's Energy Star Score - Evaluation
- Day 34: Feature engineering with featuretools
- Day 35: Building DNNs with Keras in R
- Day 36: Sentiment analysis of movie reviews
- Day 37: Bayesian optimization
- Day 38: Feature ranking methods
- Day 39: Predicting house price - EDA
- Day 40: Predicting house prices - Linear regression
- Day 41: Predicting house prices - Deep learning
- Day 42: Association Rule Mining
- Day 43: Clustering algorithms
- Day 44: Modelling churn
- Day 45: Jupyter on docker
- Day 46: Forecasting avocado prices - fbprophet
- Day 47: Forecasting avocado prices - statsmodels
- Day 48: Forecasting ozone levels in Madrid
- Day 49: Fast Food in Cincinnati
- Day 50: Clustering user preference
- Day 51: Tensorflow - First Steps
- Day 52: Tensorflow - Synthetic Features and Outliers
- Day 53: Tensorflow - Validation
- Day 54: Tensorflow - Feature Sets
- Day 55: Tensorflow - Feature Crosses
- Day 56: Tensorflow - Logistic Regression
- Day 57: Tensorflow - Sparsity and L1 Regularization
- Day 58: Tensorflow - Introduction to Neural Networks
- Day 59: Tensorflow - Sparse Data and Embeddings
- Day 60: Tensorflow - Classifying Handwritten Digits with Neural Networks
- Day 61: Tensorflow - Improving Neural Net Performance
- Day 62: Analysing CRAN log files
- Day 63: Predicting Loan Success
- Day 64: Building Interest Rates Calculator
- Day 65: Visualizing colleges in California
- Day 66: Predict diabetes risk
- Day 67: Predict future sales
- Day 68: Predict wine quality
- Day 69: Predictive Customer Analytics - Attrition Patterns
- Day 70: Predictive Customer Analytics - Lifetime Value
- Day 71: Predictive Customer Analytics - Grouping Customer Support Problems
- Day 72: Predictive Customer Analytics - Predicting Prospect Propensity
- Day 73: Predictive Customer Analytics - Recommending Items to Users
- Day 74: Numpy Essentials
- Day 75: Pandas Essentials
- Day 76: Python Data Analysis Essentials
- Day 77: Machine Learning 101
- Day 78: Trading Cryptocurrencies with momentum trading strategy
- Day 79: Predicting Republican and Democratic Donations
- Day 80: Exploring World Happiness Report
- Day 81: Creating FiveThirtyEight Graphs
- Day 82: Auto ML with TPOT
- Day 83: EDA with Sales Data
- Day 84: Inspecting worse offender - Dodger Stadium
- Day 85: Bayesian A/B testing
- Day 86: Time series analysis
- Day 87: California Wildfires
- Day 88: Display calendar with python
- Day 89: Anonymize data with pandas
- Day 90: Exploring correlations with corr
- Day 91: Fraud detection using autoencoders
- Day 92: Published patents? 2011 China emerges as a leader
- Day 93: China - 2 million patents for invention
- Day 94: t-SNE
- Day 95: Classifying Consumer Finance Complaints
- Day 96: Item Store demand forecasting - prophet
- Day 97: Item Store demand forecasting - lightgbm
- Day 98: Movie review sentiment analysis
- Day 99: MNIST CNN with Keras
- Day 100: Brazilian ecommerce customer lifetime value prediction
Files
2018
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