MP4 | Video: AVC 1280x720 | Audio: AAC 48KHz 2ch | Duration: 7H 17M | 13 GBGenre: eLearning | Language: EnglishThe Rough Cuts/Sneak Peek program provides early access to Pearson products and is exclusively available to Safari subscribers.

Content for titles in this program is made available throughout the development cycle, so products may not be complete, edited, or finalized, including post-production editing.

IntroductionLesson 1: What is Machine Learning?Learning objectives1.1 Install1.2 Understand the ML Libraries (new lesson, title TBD)1.3 Describe the techniques used in machine learning1.4 Understand the difference between "deep learning" and other ML techniques1.5 Understand classification versus regression versus.clustering and over/underfitting1.6 Perform dimensionality reduction, explain feature engineering, and utilize feature selection1.7 Distinguish categorical versus ordinal versus continuous variables1.8 Perform one-hot encoding1.9 Utilize hyperparameters and grid search1.10 Understand choose and metricsLesson 2: Exploring a Data SetLearning objectives2.1 Uncover anomalies and data integrity problems2.2 Clean and massage your data2.3 Choose features and a target2.4 Implement a train/test split and choose modelLesson 3: ClassificationLearning objectives3.1 Understand feature importances3.2 Establish cut points in a decision tree3.3 Utilize a common API3.4 Use a more encouraging dataset3.5 Compare multiple classifiers3.6 Understand more about feature importances3.7 Use multiclass classification3.8 Understand prediction probabilities and decision boundariesLesson 4: RegressionLearning objectives4.1 Sample data sets in scikit-learn4.2 Compare a gaggle of regressors4.3 Use linear models4.4 Understand the pitfalls of linear models4.5 Use non-linear regressorsLesson 5: ClusteringLearning objectives5.1 Compare clustering algorithms5.2 Cluster to test a hypothesis5.3 Cluster into N classes5.4 Cluster into an unknown number of categories5.5 Use density based clustering: DBScan and HDBScan5.6 Evaluate clusteringLesson 6: HyperparametersLearning objectives6.1 Explore one hyperparameter6.2 Explore many hyperparameters6.3 Use GridsearchCVLesson 7: Feature Engineering and Feature SelectionLearning objectives7.1 Understand a synthetic example7.2 Understand dimensionality reduction7.3 Use principal component analysis (PCA)7.4 Use other decompositions: NMF, LDA, ICA, t-dist7.5 Implement feature selection: Univariate7.6 Implement feature selection: Model-based7.7 Understand dimensionality expansion (polynomial features)