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DATA SCIENCE, DEEP LEARNING, & MACHINE LEARNING WITH PYTHON

Go hands-on with the neural network, artificial intelligence, and machine learning techniques employers are seeking!

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Created by Sundog Education by Frank Kane, Frank Kane
Last updated 9/2017
English
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
The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We’ll cover the machine learning and data mining techniques real employers are looking for, including:
  • Deep Learning / Neural Networks (MLP’s, CNN’s, RNN’s)
  • Regression analysis
  • K-Means Clustering
  • Principal Component Analysis
  • Train/Test and cross validation
  • Bayesian Methods
  • Decision Trees and Random Forests
  • Multivariate Regression
  • Multi-Level Models
  • Support Vector Machines
  • Reinforcement Learning
  • Collaborative Filtering
  • K-Nearest Neighbor
  • Bias/Variance Tradeoff
  • Ensemble Learning
  • Term Frequency / Inverse Document Frequency
  • Experimental Design and A/B Tests

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