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Udemy Manning – Machine Learning for Mere Mortals

U01M01 The basics
U01M02 Machine Learning versus Artificial Intelligence
U01M03 Supervised learning
U01M04 Unsupervised learning
U01M05 Reinforcement learning
U01M06 A quick math refresher
U01M07 Slope of a line
U01M08 Scalars, vectors, and tensors
U01M09 Matrices and matrix arithmetic
U01M10 Set up your computing environment
U01M11 Install Python tools
U01M12 Create virtualenv environment
U01M13 Install Tensorflow
U01M14 The projects
U02M01 Supervised learning
U02M02 Trend lines
U02M03 Cost functions
U02M04 Minimizing cost functions
U02M05 Visualizing data
U02M06 Using linear regression to predict values
U02M07 More complicated functions
U02M08 Working with matrices
U02M09 Letting Tensorflow do the hard work
U03M01 More supervised learning
U03M02 What are features_
U03M03 What makes a good feature_
U03M04 Decision trees
U03M05 K-nearest neighbor
U03M06 Linear classification
U03M07 Making it work in Tensorflow
U03M08 Creating a spam filter
U03M09 Tools and data for email classification
U03M10 Classifying emails
U04M01 How clustering works
U04M02 Clustering algorithms
U04M03 Introducing k-means
U04M06 Assigning Points to a Centroid in K-means)
U05M01 What are neural networks, and how do they work
U05M02 The Tensorflow Playground interface
U05M03 Adding nodes to use multiple models in the TensorFlow Playground
U05M04 What hidden layers are, and how to use them with TensorFlow Playground
U05M05 What is the activation function in a neural network_
U06M01 Using Neural Networks
U06M02 How encoding non-numeric data works
U06M03 One hot encoding
U06M04 How image recognition relates to a neural network
U07M01 Encoding and Representation
U07M02 Numeric representation of data
U07M03 Text representation of data
U07M04 Representation of image data
U07M05 Representation of audio data
U07M06 Analytics, stock prices, and other time series data
U07M07 Preparing data_ finding the data set
U07M08 Preparing data_ Features engineering
U07M09 Principal Component Analysis_ The mathematical way to determine features
U07M10 Feature selection
U07M11 Geometry of the data space and the curse of dimensionality
U08M01 The difference between an algorithm and a model
U08M02 Chaining together models
U09M01 Improving performance in machine learning routines
U09M02 Using parallelization
U09M03 Outliers
U09M05 What should we do with outliers_
U09M06 Robustness and noise
U09M07 Overfitting
U09M08 Regularization