This program offers learners the opportunity to gain expertise on the processes, patterns, and strategies needed for building effective learning systems. The program starts with an understanding of the fundamentals of Python and then introduces the essentials of machine learning with Python.
The focus of this program is to offer Learners the skills 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.
This program covers the following key areas and topics:
- Getting started with Python
- Working with the Python Interactive Shell and Writing and Running Simple Scripts
- Data Types
- Control Statements
- Functions
- Lists and Tuples
- Dictionaries and Sets
- Object-Oriented Programming
- Modules, Packages, and File Operations
- Error Handling
- PCAP Exam Objectives
- PCEP Exam Objectives
- An In-Depth Look at Machine Learning and the Technical Background
- Predicting Categories: Getting Started with Classification
- Predicting Numerical Values: Getting Started with Regression
- Evaluating and Comparing Learners
- Evaluating Classifiers
- Evaluating Regressors
- More Classification Methods
- More Regression Methods
- Manual Feature Engineering: Manipulating Data for Fun and Profit
- Tuning Hyperparameters and Pipelines
- Combining Learners
- Models that Engineer Features for Us
- Feature Engineering for Domains: Domain-Specific Learning
- Connections, Extensions, and Further Directions
As part of this program, Learners will complete the following hands-on labs and activities:
- Using the print Method
- Displaying a Statement Multiple Times
- Using Variable Assignment, Using Variables and Assigning Statements
- Displaying the Multiplication Table
- Using Arithmetic Operators
- Performing String Slicing Tasks
- Working with Strings and Manipulating Strings Using the strip Method
- Working with Lists
- Using Boolean Operators
- Working with the if Statement and while Statement
- Using the for Loop and the range Function
- Using Nested Loops
- Working with Function Arguments
- Using Lambda Functions
- Using List Methods and Tuple Methods
- Arranging and Presenting Data Using Dictionaries
- Combining Dictionaries
- Creating Intersections of Elements in a Collection
- Defining Methods in a Class
- Creating Class Attributes, Class Methods and Using Information Hiding
- Overriding Methods
- Practicing Multiple Inheritance
- Using Resources in a Module
- Identifying Error Scenarios
- Handling Errors
- Creating the Custom Exception Class
- 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
- 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
- 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
Optional Volunteer Externship Opportunity
Learners who complete this program are eligible to participate in an optional volunteer externship opportunity with a local company/agency/organization whose work aligns with this area of study in order to gain valuable hands-on experience. As learners progress through their eLearning program, an Externship Coordinator will reach out to coordinate placement.
Note: Additional documentation including health records, immunizations, drug-screening, criminal background checks, etc. may be required by the externship facility.
