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Machine Learning, Data Science and Deep Learning with Python, Go hands-on with in-demand machine learning, Tensorflow, Keras, artificial intelligence, and neural network techniques.
Created by Sundog Education by Frank Kane, Frank Kane
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What Will I Learn?
- Develop using iPython notebooks
- Understand statistical measures such as standard deviation
- Visualize data distributions, probability mass functions, and probability density functions
- Visualize data with matplotlib
- Use covariance and correlation metrics
- Apply conditional probability for finding correlated features
- Use Bayes' Theorem to identify false positives
- Make predictions using linear regression, polynomial regression, and multivariate regression
- Understand complex multi-level models
- Use train/test and K-Fold cross validation to choose the right model
- Build a spam classifier using Naive Bayes
- Use decision trees to predict hiring decisions
- Cluster data using K-Means clustering and Support Vector Machines (SVM)
- Build a movie recommender system using item-based and user-based collaborative filtering
- Predict classifications using K-Nearest-Neighbor (KNN)
- Apply dimensionality reduction with Principal Component Analysis (PCA) to classify flowers
- Understand reinforcement learning - and how to build a Pac-Man bot
- Clean your input data to remove outliers
- Implement machine learning, clustering, and search using TF/IDF at massive scale with Apache Spark's MLLib
- Design and evaluate A/B tests using T-Tests and P-Values
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