Course Summary
This course teaches you how to follow a machine learning workflow to create effective models through data preprocessing, feature engineering, model selection, and hyperparameter tuning.Key Learning Points
- Learn how to effectively preprocess and clean data for machine learning models.
- Understand the importance of feature engineering and how to create effective features.
- Gain knowledge on how to select the right model for your data and how to tune its hyperparameters.
Job Positions & Salaries of people who have taken this course might have
- USA: $113,309
- India: ₹955,416
- Spain: €42,000
- USA: $113,309
- India: ₹955,416
- Spain: €42,000
- USA: $120,931
- India: ₹1,079,020
- Spain: €45,000
- USA: $113,309
- India: ₹955,416
- Spain: €42,000
- USA: $120,931
- India: ₹1,079,020
- Spain: €45,000
- USA: $62,453
- India: ₹413,245
- Spain: €23,000
Related Topics for further study
Learning Outcomes
- Ability to effectively preprocess and clean data for machine learning models
- Understanding of feature engineering and how to create effective features
- Knowledge on how to select the right model for your data and how to tune its hyperparameters
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming in Python
- Familiarity with machine learning concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Applied Data Science with Python
- Data Science Methodology
Related Education Paths
Notable People in This Field
- Andrew Ng
- Fei-Fei Li
Related Books
Description
Machine learning is not just a single task or even a small group of tasks; it is an entire process, one that practitioners must follow from beginning to end. It is this process—also called a workflow—that enables the organization to get the most useful results out of their machine learning technologies. No matter what form the final product or service takes, leveraging the workflow is key to the success of the business's AI solution.
Knowledge
- Collect and prepare a dataset to use for training and testing a machine learning model.
- Analyze a dataset to gain insights.
- Set up and train a machine learning model as needed to meet business requirements.
- Communicate the findings of a machine learning project back to the organization.
Outline
- Collect the Dataset
- Follow a Machine Learning Workflow Course Introduction
- CAIP Specialization Introduction
- Collect the Dataset Module Introduction
- Machine Learning Datasets
- Data Structure Terminology
- Data Quality Issues
- Data Sources
- Guidelines for Selecting a Machine Learning Dataset
- ETL and Machine Learning Pipelines
- Overview
- Open Datasets
- Guidelines for Loading a Dataset
- Open Datasets Quiz
- Collecting the Dataset
- Analyze the Dataset
- Analyze the Dataset Module Introduction
- Dataset Content and Format
- Distributions
- Descriptive Statistical Analysis
- Central Tendency
- Variability and Range
- Variance and Standard Deviation
- Skewness
- Kurtosis
- Correlation Coefficient
- Visualizations
- Histogram
- Box Plot
- Scatterplot
- Maps
- Overview
- Guidelines for Exploring the Structure of a Dataset
- Statistical Moments
- Guidelines for Analyzing a Dataset
- Guidelines for Using Visualizations to Analyze Data
- Analyzing the Dataset
- Prepare the Dataset
- Prepare the Dataset Module Introduction
- Data Preparation
- Data Types
- Continuous vs. Discrete Variables
- Data Encoding
- Dimensionality Reduction
- Missing and Duplicate Values
- Normalization and Standardization
- Holdout Method
- Overview
- Operations You Can Perform on Different Types of Data
- Summarization
- Guidelines for Preparing Training and Testing Data
- Data Types Quiz
- Preparing the Dataset
- Set Up and Train a Model
- Set Up and Train a Model Module Introduction
- Design of Experiments
- Hypothesis Testing
- p-value and Confidence Interval
- Machine Learning Algorithms
- Iterative Tuning
- Bias and Generalizations
- Cross-Validation
- Feature Transformation
- The Bias–Variance Tradeoff
- Parameters
- Regularization
- Training Efficiency
- Overview
- Guidelines for Setting Up a Machine Learning Model
- Guidelines for Training and Tuning the Model
- Setting Up and Training the Model
- Finalize the Model
- Finalize the Model Module Introduction
- Know Your Audience
- Use Visualization to Present Your Findings
- Put Together a Machine Learning Presentation
- Communicate Your Findings Clearly
- Put a Model into Production
- Pipeline Automation
- Testing and Maintenance
- Overview
- Consumer-Oriented Applications
- Guidelines for Incorporating Machine Learning into a Long-Term Solution
- Finalizing a Model
- Apply What You've Learned
Summary of User Reviews
Learn about the machine learning workflow with Coursera's course. Users have found the course to be comprehensive and well-structured. The overall rating is high with positive feedback for the course content, structure, and instructor.Key Aspect Users Liked About This Course
The course is comprehensive and well-structured.Pros from User Reviews
- The course provides a good overview of machine learning concepts
- The instructor explains complex topics in an easy-to-understand manner
- The course is well-structured and easy to follow
- The course provides practical examples and exercises to reinforce learning
Cons from User Reviews
- Some users found the course to be too basic
- The course may not be suitable for advanced machine learning practitioners
- Some users found the course to be too theoretical with limited practical application
- The course may require additional resources for a deeper understanding of some topics
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