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

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

Calling all curious minds! Start your Machine Learning with Python coding journey today, and become an expert engineer.

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

Lessons

TestPrep

Lab

Instructor Led (Add-on)

AI Tutor (Add-on)

87
Review

Learn Machine Learning with Python, a comprehensive training manual that teaches the fundamentals of coding with Python. Whether you want to improve your coding skills or you want an upgrade at your workplace, this is the ideal start. In this course, you’ll master the processes, patterns, and strategies of this user-friendly programming language. This Python ML course covers supervised learning paradigms, like classical algorithms, and regression techniques to evaluate performance metrics. Besides this, you’ll also learn feature engineering for converting raw data into meaningful features. Furthermore, you’ll also leverage the Python scikit-learn library along with other powerful tools. Practice on our Labs to solidify your understanding as you explore object-oriented programming, modules, error handling, and even file operations. By the end of this course, you'll be confidently writing Python ML scripts and resolving coding issues.

- Understand the fundamentals of supervised machine learning algorithms and their classification
- Evaluating performance metrics for assessing the efficacy of your models
- Using engineer features to convert raw data into meaningful ML algorithms
- Managing system performance by creating robust pipelines
- Apply ML to various data types
- Leverage Python scikit-learn library and other tools
- Use of advanced techniques like neural networks and graphical models

Get the support you need. Enroll in our Instructor-Led Course.

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

A

- 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

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This ML training course will transform you from a curious onlooker to a machine learning expert. There’s a lot you’ll be learning:

- Supervising ML algorithms; classification (spam filters), and regression (predicting prices)
- Build models, assessing their performances, and delivering results
- Master feature engineering
- Exploring data diversity
- Leveraging Scikit-learn and other python tools

You’ll learn these 2 algorithm categories:

- Classification - Support Vector Machines (SVM) * Random Forests * K-Nearest Neighbors (KNN) * Logistic Regression
- Regression* Linear Regression * Decision Tree Regression

No, this python course majorly focuses on core fundamentals of ML concepts and algorithms.