Machine Learning with No-Code/Low-Code E-Learning

No-code and low-code Machine Learning are popular options as they require no coding or minimum coding experience. In this No/Low Code Machine Learning LearningKit, you will explore different no-code or lowcode Machine Learning platforms such as KNIME, RapidMiner, and BigQuery ML.

This Learning Kit with more than 20:13 hours of learning is divided into three tracks:

Course content

Track 1: Low-code Machine Learning with KNIME

In this track, the focus will be on low-code with KNIME. KNIME is a free, open-source data
analytics, reporting and integration platform. KNIME integrates various components for machine
learning and data mining through its modular data pipelining "Building Blocks of Analytics"
concept.

Courses:

Low-code ML with KNIME: Getting Started with the KNIME Analytics Platform

Course: 45 Minutes

• Course Overview
• Features of KNIME
• Machine Learning
• Viewing Sample Workflows in KNIME Community Hub
• Installing KNIME for Windows and Mac
• Opening a Sample Workflow from the KNIME Workspace
• Course Summary

Low-code ML with KNIME: Building Regression Models

Course: 1 Hour, 36 Minutes

• Course Overview
• Features of KNIME
• Machine Learning
• Viewing Sample Workflows in KNIME Community Hub
• Installing KNIME for Windows and Mac
• Opening a Sample Workflow from the KNIME Workspace
• Course Summary

Low-code ML with KNIME: Building Classification Models

Course: 2 Hours, 5 Minutes

• Course Overview
• Classification Models
• Reading and Exploring the Classification Dataset
• Removing Missing Values and Duplicate Data
• Detecting and Removing Outliers
• Removing Correlated Variables
• Converting Categorical Data to Numeric Values
• Preparing and Partitioning Data
• Training a Logistic Regression Model
• Improving Model Performance using Normalization
• Training a Random Forest Classification Model
• Oversampling Training Data using SMOTE
• Configuring Search Space for Hyperparameter Tuning
• Performing Hyperparameter Tuning
• Training an XGBoost Classification Model
• Course Summary

Low-code ML with KNIME: Building Clustering Models

Course: 1 Hour, 4 Minutes

• Course Overview
• Clustering Models
• Reading the Classification Dataset
• Imputing Missing Values and Checking Correlations
• Standardizing Data and Removing Outliers
• Performing K-means Clustering
• Visualizing Cluster Details
• Applying PCA and Performing 3D Visualization
• Finding the Optimal Number of Clusters
• Course Summary

Low-code ML with KNIME: Performing Time Series & Market Basket Analysis

Course: 1 Hour, 26 Minutes

• Course Overview
• Time Series Analysis
• Loading Data and Converting Date Types
• Computing and Visualizing Moving Averages
• Visualizing Data Quarterly and Monthly
• Decomposing Time Series Signals
• Inspecting and Removing Seasonality
• Fitting an ARIMA (1, 1, 1) Model
• Loading and Preparing Data
• Association Rules Learning
• Performing Association Rule Learning
• Course Summary

Assessment:

• Final Exam: Low-code Machine Learning with KNIME

Track 2: No-code Machine Learning with RapidMiner

In this track, the focus will be on no-code ML with RapidMiner. RapidMiner is a data science platform
designed for enterprises that analyses the collective impact of organizations’ employees, expertise, and
data. Rapid Miner's data science platform supports many analytics users across a broad AI lifecycle.

Courses:

No-code ML with RapidMiner: Getting Started with RapidMiner

Course: 46 Minutes

• Course Overview
• RapidMiner Features
• Supervised vs. Unsupervised Learning
• Reviewing the RapidMiner Website and Documentation
• Installing RapidMiner on macOS and Windows
• Exploring RapidMiner Studio
• Course Summary

No-code ML with RapidMiner: Performing Regression Analysis

Course: 1 Hour, 58 Minutes

• Course Overview
• Overview of Regression
• Loading and Summarizing Data with RapidMiner
• Computing Quality Measures and Statistical Summaries
• Visualizing Data with Univariate Visualizations
• Using Bivariate and Multivariate Visualizations
• Using Turbo Prep for Automated Data Preparation
• Using Auto Model for Model Training and Evaluation
• Cleaning Data and Converting Types
• Computing and Filtering Correlated Attributes
• Creating Subprocesses and Partitioning Data
• Selecting Attributes and One-hot Encoding
• Training a Linear Regression Model
• Comparing Performance for Multiple Models
• Tuning Random Forest Hyperparameters
• Course Summary

No-code ML with RapidMiner: Building & Using Classification Models

Course: 1 Hour, 20 Minutes

• Course Overview
• Overview of Classification
• Loading and Summarizing Data
• Assigning Roles and Removing Useless Attributes
• Preparing Data using Turbo Prep
• Building Models using Auto Model
• Treating Missing Values and Removing Duplicate Rows
• Training and Evaluating a Logistic Regression Model
• Training and Evaluating Multiple Classification Models
• Deploying a Model Locally
• Course Summary

No-code ML with RapidMiner: Performing Clustering Analysis

Course: 1 Hour, 1 Minute

• Course Overview
• Overview of Clustering
• Loading and Visualizing Data
• Performing Clustering using Turbo Prep and Auto Model
• Preparing Data for Clustering
• Performing and Evaluating K-means Clustering
• Visualizing Clusters using Principal Components
• Hyperparameter Tuning for Optimal Number of Clusters
• Course Summary

No-code ML with RapidMiner: Time-series Forecasting & Market Basket Analysis

Course: 1 Hour, 43 Minutes

• Course Overview
• Overview of Clustering
• Loading and Visualizing Data
• Performing Clustering using Turbo Prep and Auto Model
• Preparing Data for Clustering
• Performing and Evaluating K-means Clustering
• Visualizing Clusters using Principal Components
• Hyperparameter Tuning for Optimal Number of Clusters
• Course Summary

Assessment:

• Final Exam: No-code Machine Learning with RapidMiner

Track 3: Machine Learning Using SQL with BigQuery ML

In this track, the focus will be on machine learning with BigQuery ML. BigQuery is Google's fully managed, serverless data warehouse that enables scalable analysis over petabytes of data. It is a Platform as a Service (PaaS) that supports querying using a dialect of SQL.

Courses:

Machine Learning with BigQuery ML: Building Regression Models

Course: 2 Hours, 4 Minutes

• Course Overview
• BigQuery ML Introduction
• Supervised and Unsupervised Machine Learning (ML)
• Creating a Google Cloud Platform (GCP) Account and Accessing BigQuery
• Regression Model Introduction
• Creating a Dataset Table and Loading Data
• Exploring and Visualizing Data with Looker Studio
• Processing Data with DataPrep - I
• Processing Data with DataPrep - II
• Training and Evaluating a Linear Regression Model
• Viewing and Evaluating and ML Model
• Training and Evaluating a Boosted Tree Regression Model
• Training and Evaluating a Random Forest Model
• Course Summary

Machine Learning with BigQuery ML: Building Classification Models

Course: 1 Hour, 47 Minutes

• Course Overview
• BigQuery ML Introduction
• Supervised and Unsupervised Machine Learning (ML)
• Creating a Google Cloud Platform (GCP) Account and Accessing BigQuery
• Regression Model Introduction
• Creating a Dataset Table and Loading Data
• Exploring and Visualizing Data with Looker Studio
• Processing Data with DataPrep - I
• Processing Data with DataPrep - II
• Training and Evaluating a Linear Regression Model
• Viewing and Evaluating and ML Model
• Training and Evaluating a Boosted Tree Regression Model
• Training and Evaluating a Random Forest Model
• Course Summary

Machine Learning with BigQuery ML: Building Unsupervised Models

Course: 1 Hour, 41 Minutes

• Course Overview
• BigQuery ML Introduction
• Supervised and Unsupervised Machine Learning (ML)
• Creating a Google Cloud Platform (GCP) Account and Accessing BigQuery
• Regression Model Introduction
• Creating a Dataset Table and Loading Data
• Exploring and Visualizing Data with Looker Studio
• Processing Data with DataPrep - I
• Processing Data with DataPrep - II
• Training and Evaluating a Linear Regression Model
• Viewing and Evaluating and ML Model
• Training and Evaluating a Boosted Tree Regression Model
• Training and Evaluating a Random Forest Model
• Course Summary

Machine Learning with BigQuery ML: Training Time Series Forecasting Models

Course: 57 Minutes

• Course Overview
• Time Series Analysis Introduction
• Loading and Visualizing Time Series Data
• Exploring and Understanding Data
• Fitting an ARIMA Model
• Using Windowing for Trend Smoothing
• Performing Multiple Time Series Forecasting
• Course Summary

Assessment:

• Final Exam: Machine Learning Using SQL with BigQuery ML