[FreeCoursesOnline.Me] MANNING - Machine Learning for Mere Mortals
File List
- U09M08 Regularization.mp4 272.5 MB
- U09M03 Outliers.mp4 204.6 MB
- U08M02 Chaining together models.mp4 146.6 MB
- U09M05 What should we do with outliers_.mp4 119.1 MB
- U09M07 Overfitting.mp4 104.2 MB
- U06M04 How image recognition relates to a neural network.mp4 95.1 MB
- U09M01 Improving performance in machine learning routines.mp4 89.8 MB
- U06M03 One hot encoding.mp4 84.5 MB
- U04M01 How clustering works.mp4 82.1 MB
- U05M01 What are neural networks, and how do they work.mp4 68.7 MB
- U06M02 How encoding non-numeric data works.mp4 64.8 MB
- U07M08 Preparing data_ Features engineering.mp4 61.4 MB
- U07M11 Geometry of the data space and the curse of dimensionality.mp4 58.6 MB
- U09M02 Using parallelization.mp4 57.3 MB
- U04M03 Introducing k-means.mp4 56.9 MB
- U04M02 Clustering algorithms.mp4 56.8 MB
- U06M01 Using Neural Networks.mp4 56.6 MB
- U08M01 The difference between an algorithm and a model.mp4 50.3 MB
- U01M07 Slope of a line.mp4 45.1 MB
- U05M04 What hidden layers are, and how to use them with TensorFlow Playground.mp4 43.9 MB
- U07M06 Analytics, stock prices, and other time series data.mp4 43.6 MB
- U01M03 Supervised learning.mp4 43.1 MB
- U07M02 Numeric representation of data.mp4 40.6 MB
- U09M06 Robustness and noise.mp4 40.3 MB
- U03M09 Tools and data for email classification.mp4 36.0 MB
- U05M05 What is the activation function in a neural network_.mp4 35.8 MB
- U02M05 Visualizing data.mp4 35.0 MB
- U01M01 The basics.mp4 34.7 MB
- U07M10 Feature selection.mp4 34.5 MB
- U04M06 Assigning Points to a Centroid in K-means).mp4 34.3 MB
- U07M04 Representation of image data.mp4 34.1 MB
- U01M02 Machine Learning versus Artificial Intelligence.mp4 33.0 MB
- U07M03 Text representation of data.mp4 31.8 MB
- U07M07 Preparing data_ finding the data set.mp4 31.3 MB
- U01M05 Reinforcement learning.mp4 28.4 MB
- U07M05 Representation of audio data.mp4 28.4 MB
- U01M08 Scalars, vectors, and tensors.mp4 26.5 MB
- U07M09 Principal Component Analysis_ The mathematical way to determine features.mp4 23.1 MB
- U02M06 Using linear regression to predict values.mp4 22.8 MB
- U02M01 Supervised learning.mp4 22.3 MB
- U07M01 Encoding and Representation.mp4 21.3 MB
- U03M07 Making it work in Tensorflow.mp4 21.2 MB
- U01M13 Install Tensorflow.mp4 18.3 MB
- U01M04 Unsupervised learning.mp4 16.2 MB
- U03M03 What makes a good feature_.mp4 15.5 MB
- U02M09 Letting Tensorflow do the hard work.mp4 14.2 MB
- U05M02 The Tensorflow Playground interface.mp4 14.0 MB
- U03M08 Creating a spam filter.mp4 13.7 MB
- U01M09 Matrices and matrix arithmetic.mp4 13.4 MB
- U05M03 Adding nodes to use multiple models in the TensorFlow Playground.mp4 12.8 MB
- U01M11 Install Python tools.mp4 11.2 MB
- U01M06 A quick math refresher.mp4 11.0 MB
- U03M04 Decision trees.mp4 10.6 MB
- U01M14 The projects.mp4 10.1 MB
- U03M05 K-nearest neighbor.mp4 9.9 MB
- U03M10 Classifying emails.mp4 9.2 MB
- U02M04 Minimizing cost functions.mp4 8.8 MB
- U03M06 Linear classification.mp4 7.9 MB
- U01M12 Create virtualenv environment.mp4 5.9 MB
- U03M02 What are features_.mp4 5.6 MB
- U03M01 More supervised learning.mp4 4.7 MB
- U02M02 Trend lines.mp4 4.6 MB
- U02M08 Working with matrices.mp4 4.6 MB
- U02M03 Cost functions.mp4 3.8 MB
- U01M10 Set up your computing environment.mp4 1.8 MB
- U02M07 More complicated functions.mp4 1.7 MB
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