###### Google Cloud Certified Professional Machine Learning Engineer

ISBN: 978-1-64459-591-6Google GCPMLE.AE1

(ML-PYTHON.AP1) / ISBN : 978-1-64459-274-8

This course includes

Lessons

TestPrep

Lab

Instructor Led (Add-on)

AI Tutor (Add-on)

87
Review

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.

16+ Lessons | 44+ Exercises | 95+ Quizzes | 100+ Flashcards | 100+ Glossary of terms

55+ Pre Assessment Questions | 55+ Post Assessment Questions |

1

- 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
- End-of-Lesson Material

2

- 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 Plus-One Trick
- Getting Groovy, Breaking the Straight-Jacket, and Nonlinearity
- NumPy versus “All the Maths”
- Floating-Point Issues
- EOC

3

- 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

4

- A Simple Regression Dataset
- Nearest-Neighbors Regression and Summary Statistics
- Linear Regression and Errors
- Optimization: Picking the Best Answer
- Simple Evaluation and Comparison of Regressors
- EOC

5

- 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
- Break-It-Down: Deconstructing Error into Bias and Variance
- Graphical Evaluation and Comparison
- Comparing Learners with Cross-Validation
- EOC

6

- Baseline Classifiers
- Beyond Accuracy: Metrics for Classification
- ROC Curves
- Another Take on Multiclass: One-versus-One
- Precision-Recall Curves
- Cumulative Response and Lift Curves
- More Sophisticated Evaluation of Classifiers: Take Two
- EOC

7

- Baseline Regressors
- Additional Measures for Regression
- Residual Plots
- A First Look at Standardization
- Evaluating Regressors in a More Sophisticated Way: Take Two
- EOC

8

- Revisiting Classification
- Decision Trees
- Support Vector Classifiers
- Logistic Regression
- Discriminant Analysis
- Assumptions, Biases, and Classifiers
- Comparison of Classifiers: Take Three
- EOC

9

- Linear Regression in the Penalty Box: Regularization
- Support Vector Regression
- Piecewise Constant Regression
- Regression Trees
- Comparison of Regressors: Take Three
- EOC

10

- 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

11

- Models, Parameters, Hyperparameters
- Tuning Hyperparameters
- Down the Recursive Rabbit Hole: Nested Cross-Validation
- Pipelines
- Pipelines and Tuning Together
- EOC

12

- Ensembles
- Voting Ensembles
- Bagging and Random Forests
- Boosting
- Comparing the Tree-Ensemble Methods
- EOC

13

- Feature Selection
- Feature Construction with Kernels
- Principal Components Analysis: An Unsupervised Technique
- EOC

14

- Working with Text
- Clustering
- Working with Images
- EOC

15

- Optimization
- Linear Regression from Raw Materials
- Building Logistic Regression from Raw Materials
- SVM from Raw Materials
- Neural Networks
- Probabilistic Graphical Models
- EOC

- 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

- Encoding Text

- Building an Estimated Simple Linear Regression Equation