Machine Learning with Python
(MLPYTHON.AP1)
/ ISBN: 9781644592748
Machine Learning with Python
Enroll yourself in the Machine Learning Python course and lab to gain expertise on the processes, patterns, and strategies needed for building effective learning systems. The Machine learning course imparts skills that are required for understanding machine learning algorithms, models, and core machine learning concepts, evaluating classifiers and regressors, connections, extensions, and further directions. The study guide is equipped with learning resources to broaden your toolbox and explore some of the field’s most sophisticated and exciting techniques.
Lessons

16+ Lessons

44+ Exercises

95+ Quizzes

100+ Flashcards

100+ Glossary of terms
TestPrep

55+ Pre Assessment Questions

55+ Post Assessment Questions
Lab

35+ Performance lab
 Welcome
 Scope, Terminology, Prediction, and Data
 Putting the Machine in Machine Learning
 Examples of Learning Systems
 Evaluating Learning Systems
 A Process for Building Learning Systems
 Assumptions and Reality of Learning
 EndofLesson Material
 About Our Setup
 The Need for Mathematical Language
 Our Software for Tackling Machine Learning
 Probability
 Linear Combinations, Weighted Sums, and Dot Products
 A Geometric View: Points in Space
 Notation and the PlusOne Trick
 Getting Groovy, Breaking the StraightJacket, and Nonlinearity
 NumPy versus “All the Maths”
 FloatingPoint Issues
 EOC
 Classification Tasks
 A Simple Classification Dataset
 Training and Testing: Don’t Teach to the Test
 Evaluation: Grading the Exam
 Simple Classifier #1: Nearest Neighbors, Long Distance Relationships, and Assumptions
 Simple Classifier #2: Naive Bayes, Probability, and Broken Promises
 Simplistic Evaluation of Classifiers
 EOC
 A Simple Regression Dataset
 NearestNeighbors Regression and Summary Statistics
 Linear Regression and Errors
 Optimization: Picking the Best Answer
 Simple Evaluation and Comparison of Regressors
 EOC
 Evaluation and Why Less Is More
 Terminology for Learning Phases
 Major Tom, There’s Something Wrong: Overfitting and Underfitting
 From Errors to Costs
 (Re)Sampling: Making More from Less
 BreakItDown: Deconstructing Error into Bias and Variance
 Graphical Evaluation and Comparison
 Comparing Learners with CrossValidation
 EOC
 Baseline Classifiers
 Beyond Accuracy: Metrics for Classification
 ROC Curves
 Another Take on Multiclass: OneversusOne
 PrecisionRecall Curves
 Cumulative Response and Lift Curves
 More Sophisticated Evaluation of Classifiers: Take Two
 EOC
 Baseline Regressors
 Additional Measures for Regression
 Residual Plots
 A First Look at Standardization
 Evaluating Regressors in a More Sophisticated Way: Take Two
 EOC
 Revisiting Classification
 Decision Trees
 Support Vector Classifiers
 Logistic Regression
 Discriminant Analysis
 Assumptions, Biases, and Classifiers
 Comparison of Classifiers: Take Three
 EOC
 Linear Regression in the Penalty Box: Regularization
 Support Vector Regression
 Piecewise Constant Regression
 Regression Trees
 Comparison of Regressors: Take Three
 EOC
 Feature Engineering Terminology and Motivation
 Feature Selection and Data Reduction: Taking out the Trash
 Feature Scaling
 Discretization
 Categorical Coding
 Relationships and Interactions
 Target Manipulations
 EOC
 Models, Parameters, Hyperparameters
 Tuning Hyperparameters
 Down the Recursive Rabbit Hole: Nested CrossValidation
 Pipelines
 Pipelines and Tuning Together
 EOC
 Ensembles
 Voting Ensembles
 Bagging and Random Forests
 Boosting
 Comparing the TreeEnsemble Methods
 EOC
 Feature Selection
 Feature Construction with Kernels
 Principal Components Analysis: An Unsupervised Technique
 EOC
 Working with Text
 Clustering
 Working with Images
 EOC
 Optimization
 Linear Regression from Raw Materials
 Building Logistic Regression from Raw Materials
 SVM from Raw Materials
 Neural Networks
 Probabilistic Graphical Models
 EOC
Hands on Activities (Performance Labs)
 Plotting a Probability Distribution Graph
 Using the zip Function
 Calculating the Sum of Squares
 Plotting a Line Graph
 Plotting a 3D Graph
 Plotting a Polynomial Graph
 Using the numpy.dot() Method
 Displaying Histograms
 Defining an Outlier
 Calculating the Median Value
 Estimating the Multiple Regression Equation
 Constructing a Swarm Plot
 Using the describe() Method
 Viewing Variance
 Creating a Confusion Matrix
 Creating an ROC Curve
 Recreating an ROC Curve
 Creating a Trendline Graph
 Viewing the Standard Deviation
 Constructing a Scatterplot
 Evaluating the Prediction Error Rates
 Evaluating a Logistic Model
 Creating a Covariance Matrix
 Using the load_digits() Function
 Illustrating a Less Consistent Relationship
 Illustrating a Piecewise Constant Regression
 Manipulating the Target
 Manipulating the Input Space
 Calculating the Mean Value
 Displaying a Correlation Matrix
 Creating a Nonlinear Model
 Performing a Principal Component Analysis
 Using the Manifold Method
 Building an Estimated Simple Linear Regression Equation
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