• Admission closes on 19 Jun, 2024
  • Program Duration 4 months
  • Learning Format Online Bootcamp

Why Join this Program

Earn an Elite Certificate

Joint program completion certificate from Purdue University Online and Simplilearn

Leverage the Purdue Edge

Become eligible for a membership at the Purdue University Alumni Association

Learn Popular No Code ML Tools

Gain exposure to DataRobot, Dataiku, Amazon SageMaker Canvas, and other prominent tools

Career Support Services

Enhance your resume and showcase your profile to recruiters with career assistance services


Looking to enroll your employees into this program ?

No Code Machine Learning Overview

This program enables you to master no-code ML platforms, empowering you to perform data analysis, build models, and make data-driven decisions with ease. Gain hands-on experience with intuitive drag-and-drop interfaces, automated machine learning, and visual workflows.

Key Features

  • Simplilearn Career Service helps you get noticed by top hiring companies
  • Program completion certificate from Purdue University Online and Simplilearn
  • Access to Purdue's alumni association membership on program completion
  • 50+ hours of core curriculum delivered in live online classes by industry experts
  • Gain exposure to Amazon SageMaker Canvas, DataRobot, Dataiku, KNIME, and other prominent tools
  • Apply your knowledge through hands-on projects spanning various industries
  • Live online masterclasses delivered by Purdue faculty and staff

No Code ML Program Advantage

Gain a competitive edge with applied learning in the groundbreaking arena of no-code ML. This program enables you to make data-driven decisions using AI & ML without writing code, empowering you to build intelligent solutions with no-code platforms.

  • Program Certificate

    Partnering with Purdue University Online

    • Receive a joint Purdue-Simplilearn program certificate
    • Masterclasses delivered by Purdue faculty and staff
    • Become eligible for Purdue’s Alumni Association membership

No Code Machine Learning Details

Acquire a competitive edge through practical experience in the field of no-code ML. Gain hands-on experience across diverse subjects such as data collection, data cleaning, and machine learning algorithms. Additionally, delve into advanced topics such as ensemble methods, SVM, ANNs, and NLP.

Learning Path

  • Get started with the Professional Certificate Program in No Code Machine Learning, delivered jointly by Purdue University Online and Simplilearn. Kickstart your learning journey and explore the ability to build practical AI solutions using no-code tools.

    • Overview of Machine Learning and its Importance
    • Machine Learning Life Cycle
    • Machine Learning Challenges
    • Introduction to MLOps
    • Introduction to No-Code Machine Learning
    • Advantages and Limitations of No-Code ML
    • Popular No-Code ML Platforms
    • Key Features of No-Code ML Platforms
    • Working with Data in No-Code ML Platforms
    • Building Models with No-Code Tools
    • Data Sources and Datasets
    • Data Acquisition Techniques
    • Assessing Data Completeness, Consistency, and Accuracy
    • Automated Data Collection Tools
    • Data Import and Preprocessing using No-Code Tools
    • No-Code Tools for Data Transformation
    • Data Visualization Techniques without Coding
    • Data Cleaning Techniques using No-Code Platforms
    • Feature Engineering without Coding
    • Dimensionality Reduction
    • Handling Categorical Data
    • Balancing Imbalanced Datasets
    • Advanced Imputation Techniques
    • Advanced Outlier Detection and Treatment
    • Data Warehousing and ETL Processes
    • Supervised Learning Algorithms
    • Linear Regression and Polynomial Regression
    • Using No-Code Tools for Linear Regression
    • Logistic Regression and Classification Algorithms
    • Decision Trees, Random Forests and K-Nearest Neighbors
    • Building Classifiers using No-Code Tools
    • Unsupervised Learning Algorithms
    • Clustering Techniques
    • No-Code Clustering Tools and Visualizations
    • Dimensionality Reduction Techniques using No-Code Tools
    • Anomaly Detection and Outlier Analysis
    • Evaluation Metrics for Regression
    • Evaluation Metrics for Classification
    • No-Code Tools for Model Evaluation
    • Ensemble Learning Methods (e.g., Bagging, Boosting)
    • Support Vector Machines (SVM)
    • Introduction to Artificial Neural Networks
    • Building Blocks and Learning Process of ANNs
    • Convolutional Neural Networks (CNNs)
    • Recurrent Neural Networks (RNNs)
    • Attention Mechanism
    • Building and Training Neural Network Model with No-Code Tools
    • Text Analytics and Natural Language Processing (NLP)
    • Text Processing, Representation, and Sentiment Analysis without Coding
    • Building NLP Models using No-Code Platforms
    • Vector Embeddings
    • Cross-Validation Techniques (K-fold, Stratified, etc.)
    • Model Selection Strategies (Hyperparameter Tuning, Grid Search, Randomized Search, etc.)
    • Bias-Variance Tradeoff and Overfitting/Underfitting
    • Feature Selection Techniques without Programming
    • Model Interpretability and Explainability
    • Interpreting Model Outputs and Insights
    • Deploying Models without Coding
    • Integration with Web and Mobile Applications using No-Code Platforms
    • Model Monitoring and Management
    • Applications of No-Code Machine Learning in Various Industries
    • Case Studies in Finance (Fraud Detection, Credit Scoring)
    • Case Studies in Healthcare (Diagnosis, Treatment Recommendations)
    • Case Studies in Marketing (Customer Churn Prediction, Targeted Advertising)
    • Case Studies on Predictive Analytics
    • Case Studies on Image Recognition
    • Common Challenges of No-Code ML
    • Best Practices for ML Project Success
    • Ethical Considerations in No-Code ML Deployment

Skills Covered

  • Data Collection and Acquisition
  • Data Cleaning and Preparation
  • Exploratory Data Analysis EDA
  • Data Transformation Techniques
  • Data Integration Techniques
  • Supervised Learning Algorithms
  • Unsupervised Learning Algorithms
  • Techniques for Model Evaluation
  • Ensemble Learning Methods
  • Support Vector Machines
  • Artificial Neural Networks
  • Text Analytics
  • Natural Language Processing
  • Model Performance Optimization
  • Feature Selection
  • Model Interpretability

Tools Covered

Amazon SageMaker Canvas LatestDataiku-LatestData RobotKNIMEVertexAI-LatestRapidMinder-Latest

Program Advisors

  • Armando Galeana

    Armando Galeana

    Founder and CEO at Ubhuru Technologies

    A seasoned data science leader, with extensive experience in digital transformation. Throughout his career, Armando has leveraged his vast expertise in AI & ML to build infrastructure, create new lines of business and drive global implementations.

  • Arijit Mitra

    Arijit Mitra

    Director and Head of Machine Learning & AI at Pegasystems

    Arijit is an engineering & product leader with expertise in building and deploying AI, NLP, GPT & LLMs at scale for Fortune 500 companies. As head of AI & ML at Pega, he owns the overall AI roadmap with a focus on AI applications across functions.

  • Amitendra Srivastava

    Amitendra Srivastava

    Chief Data Scientist at Intelytica

    Amitendra’s expertise lies in utilizing data analysis and machine learning techniques to solve complex business problems and drive strategic decisions. As Chief Data Scientist, he leverages the power of data to create value and drive innovation.

  • Ankit Virmani

    Ankit Virmani

    Data & ML Leader at Google

    Ankit is an ethical AI and data engineering enthusiast with 10+ years of experience at firms like Google, Amazon, and Deloitte. He serves as a member of the Forbes Technology Council, IU's Institute of Business Analytics, and AI 2030.


Career Support

Simplilearn Career Assistance

Simplilearn’s Career Assist program, offered in partnership with Talent Inc, is a service to help you to be career ready for the workforce and land your dream job in U.S. markets.
One-on-one Interview Service by TopInterview

One-on-one Interview Service by TopInterview

Get a Resume Makeover from TopResume

Get a Resume Makeover from TopResume

Reach numerous employers with ResumeRabbit

Reach numerous employers with ResumeRabbit

Complete Candidate Confidentiality Assured

Complete Candidate Confidentiality Assured

Industry Trends

No-code AI platforms are transforming businesses by democratizing AI capabilities. User-friendly interfaces enable individuals without coding skills to create AI applications, eliminating traditional complexities and enabling broader AI adoption.

Job Icon$225.91 bn

Expected global Machine Learning (ML) market size by 2030

Source: Fortune Business
Job Icon28.3%

The global no-code AI platform market’s projected CAGR from 2023-2033

Source: Future Market
Job Icon20 mn to 50 mn

Potential new jobs expected to be created by AI by 2030

Source: McKinsey & Co

Batch Profile

This No-code Machine Learning program caters to working professionals across different industries. Learner diversity adds richness to class discussions and interactions.

  • The class consists of learners from excellent organizations and diverse industries
    Industry Profile
    Information Technology - 43%Software Product - 13%Manufacturing - 20%Phrama & Healthcare - 7%BFSI - 7%Others - 10%

Admission Details

Application Process

The application process consists of three simple steps. An offer of admission will be made to the selected candidates and can be accepted by them by paying the admission fee.


Submit Application

Tell us a bit about yourself and why you want to do this program


Application Review

An admission panel will shortlist candidates based on their application



Selected candidates can begin the program within 1-2 weeks

Eligibility Criteria

For admission to this No Code Machine Learning program, candidates should:

Be at least 18 years old and have a high school diploma or equivalent
Can be from a programming or non-programming background
Preferably have 2+ years of professional work experience

Admission Fee & Financing

The admission fee for this program is $ 2,565.

Financing Options

We are dedicated to making our programs accessible. We are committed to helping you find a way to budget for this program and offer a variety of financing options to make it more economical.

Pay in Installments

You can pay monthly installments for Post Graduate Programs using Splitit, ClimbCredit or Klarna payment option with low APR and no hidden fees.


Other Payment Options

We provide the following options for one-time payment

  • Credit Card
  • Paypal

$ 2,565

Apply Now

Program Benefits

  • Certificate from Purdue University Online and Simplilearn
  • Live online masterclasses by Purdue faculty and staff
  • Access to Purdue’s alumni association membership
  • 50+ hours of curriculum delivered in live online classes
  • Exposure to DataRobot, Dataiku, KNIME, and other tools

Program Cohorts

Next Cohort

Got questions regarding upcoming cohort dates?

No Code Machine Learning AI Course FAQs

  • How long does it take to complete the Professional Certificate Program in No Code Machine Learning?

    The anticipated time to complete the No Code Machine Learning program is 3 to 4 months.

  • Who will be the faculty for the No Code Machine Learning Course?

    Industry experts from the data and AI domain lead the curriculum delivery for this No Code ML program, bringing real-world insights and practical knowledge to the classroom. They are selected only after a rigorous shortlisting process, including profile assessment, technical examination, and a training presentation.
    The distinguished faculty members from Purdue University deliver the masterclasses for this program.

  • What certificate will I receive after completing the Professional Certificate Program in No Code Machine Learning?

    Upon completing this No Code Machine Learning program, you will be awarded a joint certificate of completion from Purdue University Online and Simplilearn.

  • What are the eligibility requirements for the No Code Machine Learning program?

    You must meet the following requirements to join the online No Code ML program:

    • Be at least 18 years old and have a high school diploma or equivalent
    • Preferably have 2+ years of professional work experience, but not mandatory
    • Prior technology know-how or programming experience is not necessary

  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.