No Code AI and Machine Learning: Building Data Science Solutions

Innovate with AI and Machine learning to accelerate your business effectively and swiftly.

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Program Designed and Developed By:

Program Highlights

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18th May 2024

Start Date

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3+

Hands-on Projects

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6-12 hrs per week

Hours of Effort

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8

MIT CEU's

Why join the No Code AI and Machine Learning Program

Learn from world-renowned MIT faculty
  • Recorded by the very best, for you to learn at your own pace.
  • A uniquely crafted no code approach towards mastering Data Science, Machine Learning and AI.
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Personalized Mentorship and Support
  • Live mentorship and guidance from AI and data science practitioners on weekends.
  • Collaborative yet personalised sessions in small groups.
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Practical, Hands-on Training
  • 3+ industry-relevant projects and 15+ real-world case studies.
  • A powerful e-portfolio for professionals to walk away with.
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Working Professional
Recorded lectures from world-renowned MIT faculty for you to learn at your own pace
Career Support
Live mentorship and guidance from AI and data science practitioners on weekends
Salary Hike
3+ industry-relevant projects and 15+ real-world case studies
Industry Project
A uniquely crafted no-code approach towards mastering Data Science, Machine Learning, and AI

Unique No Code Approach

There’s a great deal of interest in learning and applying machine learning and AI techniques across various industries. New no-code platforms are designed to allow various industries to create solutions, that would have previously required programming, using intuitive, interactive user interfaces allowing users to quickly classify information, perform data analysis, and create accurate data predictions with models.


Here are some of the tools that you will learn to use in this program.

  • Rapid Miner makes machine learning processes very reliable, easy, and efficient to use with its vast number of plugins and data analysis techniques. You can do everything from providing multiple datasets to model deployment through this platform.

  • Ikigai is a no code tool used to build data science workflows and visualizations through modular functional blocks. It helps you drag and drop to build workflows involving different data sources. This tool allows humans to make decisions and then view the results in an AI-charged dashboard/spreadsheet.

  • Teachable Machine enables professionals to employ cutting-edge technologies like Computer Vision and Deep Learning without having to write a single line of code. Make smart predictions with your customer data and design winning solutions. It also offers good integration with Google Drive and Google’s TensorFlow deep learning library.

  • Dataiku is a No Code data science platform that enables teams to build data products without coding. It automates the entire data science workflow, from preparation to deployment, using a visual interface and pre-built connectors to data storage systems and cloud platforms.

  • KNIME is a No Code platform for data analytics that enables visual pipeline building. It features pre-built nodes for processing, analysis, and visualization, and allows drag-and-drop pipeline construction. KNIME also has a marketplace for community-contributed extensions and integrations.

No Code Data Science and AI Program for Business Leaders

Learn to make AI and Data Science work towards the growth of your business.

Certificate of Completion from MIT Professional Education

MIT bootcamp certificate

Curriculum

  • Curriculum designed by MIT faculty in Data Science and Machine Learning
  • Become a Data Science decision maker by learning Deep Learning, Machine Learning, Recommendation Systems, and more.

The No Code AI and Machine Learning: Building Data Science Solutions Program lasts 12 weeks. The program will begin with blended learning elements, including recorded lectures by MIT Faculty, case studies, projects, quizzes, mentor learning sessions, and webinars.

Download Curriculum Download
Week 1
Module 1: Introduction to the AI Landscape

    To offer a general overview of the four blocks upon which this No Code AI and Machine Learning Program is focused.

    • Understanding the data: What is it telling us?
    • Prediction: What is going to happen?
    • Decision Making: What should we do?
    • Causal Inference: Did it work?
Week 2
Module 2: Data Exploration - Structured Data

    To learn the basic principles of applying data exploration techniques, such as dimensionality projection and clustering on structured data.

    • Asking the right questions to understand the data.
    • Understanding how data visualization makes data clearer.
    • Performing Exploratory Data Analysis using PCA.
    • Clustering the data through K-means & DBSCAN clustering.
    • Evaluating the quality of clusters obtained.
Week 3
Module 3: Prediction Methods - Regression

    To understand the concept of linear regression and how it can be used with historical data to build models that can predict future outcomes.

    • The idea of regression and predicting a continuous output.
    • How do you build a model that best fits your data?
    • How do you quantify the degree of uncertainty?
    • What do you do when you don’t have enough data?
    • What lies beyond linear regression?
Week 4
Module 4: Decision Systems

    To understand the concept of classification and understand how tree-based models achieve prediction of outcomes that fall into two or more categories.

    • Understand the Decision Tree model and the mechanics behind its predictions.
    • Learn to evaluate the performance of classification models.
    • Understand the concepts of Ensemble Learning and Bagging.
    • Learn how Random Forests aggregate the predictions of multiple Decision Trees.
Week 5 - Learning Break
Week 6
Module 5: Data Exploration - Unstructured Data

    To understand the concept of Natural Language Processing and how natural language represents an example of unstructured data, the business applications for this kind of data analysis, and how data exploration and prediction are performed on natural language data.

    • Understand the concept of unstructured data, and how natural language is an example.
    • Understand the business applications for Natural Language Processing.
    • Learn the techniques and methods to analyze text data.
    • Apply the knowledge gained towards the business use case of sentiment analysis.
Week 7
Module 6: Recommendation Systems

    To understand the idea behind recommendation systems and potential business applications.

    • Learn the concept of recommendation systems and potential business applications.
    • Understand the sparse data problem that necessitates recommendation systems.
    • Learn about potentially simple solutions to the recommendation problem.
    • Understand the ideas behind Collaborative Filtering Recommendation Systems.
Week 8
Module 7: Data Exploration - Temporal Data

    To understand the critical concept of temporal data, and its differences from structured and unstructured data, the idea behind Time Series Forecasting and the preprocessing required to obtain stationarity in Time Series.

    • Understand temporal data and how it represents a different data modality.
    • Understand the idea behind Time Series forecasting
    • Learn about the concept of Stationary Time Series, testing for stationarity and conversion techniques to transform non-stationary time series into stationary.
Week 9 - Learning Break
Week 10
Module 8: Prediction Methods - Neural Networks

    To understand the ideas behind Neural Networks, their introduction of non-linearities into the encoding and predictive process through a hierarchical structure, and the various steps involved in their forward propagation and back propagation cycle to minimize prediction error.

    • Understand the key concepts involved in Neural Networks.
    • Learn about the encoding process taking place in the neural network layers, and how non-linearities are introduced.
    • Understand how the forward propagation happens through the layered architecture of neural networks and how the first prediction is achieved.
    • Learn about the cost function used to evaluate the neural network’s performance, and how gradient descent is used in a backpropagation cycle to minimize error.
    • Understand the critical optimization techniques used in gradient descent
Week 11
Module 9: Computer Vision Methods

    To understand how images represent a spatial form of unstructured data and hence, a different data modality, how the Convolutional Neural Network (CNN) structure achieves generalized encoding abilities from image data and acquire an understanding of what CNNs learn.

    • Learn about spatial concepts of images such as locality and translation invariance.
    • Understand the working of filters and convolutions, and how they achieve feature extraction to generate encodings.
    • Learn about how these concepts are used in the structure of Convolutional Neural Networks (CNNs) and understand what CNNs actually learn from image data.
Week 12
Module 10: Workflows and Deployment

    To obtain additional perspective on how the same takeaways from the conceptual modules discussed prior have been applied in various business scenarios and problem statements by industry leaders who have achieved success in practical applications of Data Science and AI.

    Generative AI

    Objective: To understand how Generative AI has gotten popular in recent times, its usability in enterprise projects, how it is different from conventional AI, its evolution, and also acquire an understanding of how modern techniques like LLMs work

    •  Grasp key concepts and the history of Generative AI models
    • Be able to differentiate between Generative and Discriminative models
    • Gain an intuitive understanding of what Large Language Models are, and how tools like ChatGPT are developed
    • Stitch together different Generative AI solutions to improve your efficiency at the workplace

     

    Prompt Engineering

    Objective: To understand how you can use prompt engineering to improve the quality of the outputs you are getting from Generative models

    • Learn how generative AI and prompt engineering can be applied to analyze and extract insights from text data
    • Interact with Generative-AI powered tools efficiently using different types of prompting techniques
    • Identify & Use relevant prompting techniques as per the use-case at hand
    • Explore real-world examples of how organizations leverage generative AI and prompts for various applications
Certificate of Completion from MIT Professional Education

Hands-on Projects

Following a learn by doing pedagogy, the No Code AI and Machine Learning Program offers you the opportunity to apply your skills and knowledge in real-time through 3+ industry-relevant projects and 15+ real-world case studies.

Below are samples of potential project topics.

Real Estate

Melbourne Housing Data Analysis

Area of Project Exploratory Data Analysis
Small Summary To extract insights on the key factors that affect the selling price and the other characteristics of houses in Melbourne Australia
Tools & Concepts: RapidMiner, EDA, Descriptive Statistics
Learn more

Entertainment

Showtime Viewership Prediction

Area of Project Prediction methods - Regression
Small Summary To analyze the data and start off by coming up with a Linear Regression model to determine the driving factors for first-day viewership.
Tools & Concepts: Rapidminer, Linear Regression
Learn more

Hotels

HotelBooking Cancellation Prediction

Area of Project Decision System
Small Summary To predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.
Tools & Concepts: Rapidminer/Dataiku/ KNIME, Decision tree, Random Forest
Learn more

Sales

Sales Lead Conversion prediction

Area of Project Decision System
Small Summary To build an ML model to help identify which leads are more likely to convert to paid customers
Tools & Concepts: Rapid Miner, Data pre-processing, EDA, Linear regression
Learn more

Healthcare

Hospital Length-Of-Stay prediction

Area of Project Prediction Methods - Regression
Small Summary To predict the LOS of a patient using the data available during admission and after running a few tests.
Tools & Concepts: Rapidminer/Dataiku/KNIME, Linear Regression
Learn more

E-Commerce

Product Recommendation System

Online E-commerce websites like Amazon use different recommendation models to provide different suggestions to different users. Amazon currently uses item-to-item collaborative filtering, which scales to massive data sets and produces high-quality recommendations in real-time. In this project, you will use similar concepts to create your own product recommendation system.
Tools & Concepts: Content-Based Recommendation Systems, Collaborative Filtering, Python
Learn more

Health care

Brain Tumor Identification

Area of Project Computer Vision Methods
Small Summary To Classify MRI Scans into two classes i.e., healthy or having brain tumor.
Tools & Concepts: Teachable Machine, ANN, CNN
Learn more

Telecom

Spam detection

Area of Project Data Exploration Unstructured Data
Small Summary To extract meaningful insights from the data and build a classification model to predict whether an SMS is 'spam' or not
Tools & Concepts: Rapidminer/Dataiku, Text preprocessing, Sentiment analysis, Bag of word, Lemmatization, Vectorization
Learn more

MIT Faculty

Learn from world-renowned MIT faculty in the field of Data Science, Machine Learning, and Artificial Intelligence.

Program Faculty

Munther

Munther Dahleh

Program Faculty Director, MIT Institute for Data, Systems, and Society (IDSS)

Stefanie

Stefanie Jegelka

X-Consortium Career Development Associate Professor, EECS and IDSS, MIT

Devavrat

Devavrat Shah

Professor, EECS and IDSS, MIT

John N.

John N. Tsitsiklis

Clarence J. Lebel Professor, Dept. of Electrical Engineering & Computer Science (EECS) at MIT

Caroline

Caroline Uhler

Henry L. & Grace Doherty Associate Professor, EECS and IDSS, MIT

Learner Testimonials

Christian Ntsiba Gassuet

Mentored learning sessions, video content and explanation are good. Excellent, deep enough but not too deep for beginners. The pre-work course was and other modules document content were also very helpful as well as being very helpful to the students in the classroom.

Christian Ntsiba Gassuet

Data Engineer at National Grid

Zai Ortiz

The MIT No Code machine learning and artificial intelligence course with Great Learning is a well-paced, highly engaging and useful course. I highly recommend this course to anyone looking for a thought-provoking course that will give you the tools you need to bring a competitive edge into your workplace.

Zai Ortiz

Technical Writer at Wizeline

Vincent Li

This is the most informative, practical and foundational machine learning course I've taken so far. All the lecturers did a very good job at condensing the material into small videos that has multiple small quizzes and conclusion. Overall, it's a wonderful learning experience.

Vincent Li

Product Engineer at Qualcomm

Sylvia Jayakaran

Got immense support from my program manager, fantastic mentors, and professors. When I started the pre-work, it was a daunting challenge to manage my work alongside this course. By the time I went through the first module, I had an understanding of how to multi-task effectively.

Sylvia Jayakaran

Senior Manager - Artificial Intelligence Operations (AIOps) at Servus Credit Union

Sasikanth Nagalla

The assessment really tested our knowledge on the subject and foundations along with doing a project, that helped us with hands-on implementation.The key learnings for me to understand how recommendation engines are built on e-commerce websites and how classification models can help in managing fraud for a payment firm.

Sasikanth Nagalla

Payments Risk Data Science at Stripe

Rubbens Parchet

I joined the program with an interest in the AI and ML field but no previous knowledge. The program gave me a complete overview and understanding of the field and methodologies. The reliance on projects either as case studies during weekly sessions or as individual assignments is a real plus

Rubbens Parchet

Program Manager Sustainability at Philip Morris International

Linda Drabova

The learner management system made it all easy to navigate. The mentoring sessions were helpful and brought another perspective to the topic. I liked the curated articles, and the projects were reasonable as well.

Linda Drabova

business advisor at Metaorigin Labs

Your Learning Experience

The No Code AI and Machine Learning: Building Data Science Solutions serves as a platform for business leaders and AI enthusiasts to leverage an application-based pedagogy and solve real-world business problems.

Self-paced recorded sessions curated by MIT Faculty

  • Flexible Self Paced learning with recorded lectures
  • Designed and Delivered by MIT faculty
  • Learn to make AI-backed decisions for business growth

Personalised Mentorship and Support

  • Live mentorship by industry experts
  • Small groups of learners for personalized guidance and support
  • Interaction with like-minded peers from diverse backgrounds and geographies
  • Dedicated Program Manager for program support

Build your Data Science and AI Portfolio

  • 3+ industry-relevant projects and 15+ real-world case studies to gain practical experience
  • Translate learnings into solution-oriented skills

Program Fees

Program Fees:
2,850 USD

Apply Now

Payment Partners

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*Subject to partner approval based on regions & eligibility. dLocal for Brazil, Colombia & Mexico learners. Other partners for U.S. learners only.

Benefits of learning from us

  • Recorded lectures from MIT Faculty
  • Live Personalized Mentorship from leading AI experts
  • Comprehensive curriculum and World-class learning material
  • 3+ industry-relevant projects and 15+ real-world case studies
  • Unique no code approach
  • A dedicated program manager for program support

Payment and Refund Policy

Candidates can pay the course fee through Credit/Debit Cards and Bank Transfer. Please note that payment is accepted only in US dollars. For further details, please get in touch with your program Advisor.

Refund Policy

If the participant communicates that they will drop the course before the cohort start date, the fee paid will be returned in full, minus a $300 USD administrative fee. Cancellation requests and reimbursements will be carried out under the following criteria. Read More

Application Process

1

Fill the application form

Register your interest by filling in the online application form.

2

Application Screening

Your application will be reviewed to determine if it is a fit with the program.

3

Join the program

If selected, you will receive an offer for the upcoming cohort. Secure your seat by paying the fee.

Upcoming Application Deadline

Applications are reviewed on a rolling bases and closed once the requisite number of learners enroll for the upcoming cohort. Apply early to secure your seat.

Deadline: 25th Apr 2024

Apply Now

Cohort Start Date

Frequently Asked Questions

Program Details
What is the required weekly time commitment?

The program is divided into 10 modules, with a total of 80 study hours. Most participants are expected to spend an average of 6-12 hours per week on program activities.

Is the program completely virtual?

Yes, the program has been designed to meet the needs of working professionals so that you can learn how to leverage AI and machine learning methods from the convenience of your home within 12 weeks.

What are the best No Code AI tools in the market?

RapidMiner, KNIME, Ikigai, and Teachable Machine are some of the best open-source, free-to-use, No-Code AI tools in the market. Cloud platforms like Amazon Web Services also offer free tiers to carry out a limited amount of exploration using the No Code AI tools.

What is the application of no-code AI in different industries?

No code AI has allowed a broader range of business employees to own their automation and build new software applications without coding experience. Various sectors such as ITeS, Education, BFSI, marketing, advertising, FMCG, and manufacturing have adopted the no-code AI and ML approaches. Here’s how leading industries are leveraging no-code AI approaches:

 

  1. Finance: Helps streamline many processes like loan decisions and customer experience for banks and financial institutions. Predicts financial risks, customer churn prediction, and plans a better customer experience.
  2. Marketing: Assist in building models to craftily sort and analyze data in meaningful ways to make informed decisions. For example, marketers can segregate data about customer activities and lifetime value using no-code AI to tailor a Facebook ad to find a potential customer. 
  3. Healthcare: Encourages new collaboration between doctors and patients to give unprecedented insight into patient health, the no-code AI tools empower healthcare professionals to build customized healthcare solutions.
  4. Education: Keep track of the courses offered to the registrants to streamline the entire admission process. The No-Code approach can help schools keep up with the workload, improve their reach to students and increase overall efficiency across the university
  5. Technology: Trace where a cyber attack is coming from. Tech professionals can utilize No Code AI platforms to detect attackers and block them by using OGS of port map data.
Will this program provide similar career outcomes to a program that includes coding like Python?

The outcomes of this program would be similar to any Data Science program, i.e., to build the capability to develop data-driven solutions, interpret data outputs like an AI consumer, and develop problem-solving skills for use cases in Artificial Intelligence and Machine Learning. Python and RapidMiner are merely the tools utilized to implement these solutions. The only difference is that this program would not require you to develop programming skills during the learning journey, as the implementations are carried out using No Code AI tools.

What kinds of projects and case studies will I work on in this program?

The case studies and projects are based on multiple industry sectors including Education, Healthcare, IT, Finance, Retail, Research, and many more.

Does the program reflect the latest technology developments in No Code AI?

Yes, all the topics in this course are based on the latest technology developments in No Code AI. The program includes multiple No Code tools such as RapidMiner, Dataiku, KNIME and Teachable Machine, which you can use to implement business solutions to various data modalities and problem statement paradigms in Artificial Intelligence and Machine Learning.

Will I receive a transcript or grade after completion of the program?

No, the No Code AI and Machine Learning Program is an online program offered by MIT Professional Education - Digital Plus Programs in collaboration with Great Learning. Since it is not a degree/full-time program offered by the university, there are no grades or transcripts for this program. You will receive marks on each assessment to test your understanding and marks on each module to determine your eligibility for the certificate. Upon successful completion of the program, i.e. after completing all the modules as per the eligibility of the certificate, you are issued a certificate from MIT Professional Education.

 

Will I have to spend extra on books, virtual learning materials, or license fees?

No. All required learning material is provided online through our Learning Management System. But given this field is vast and ever-changing, there is always more you can read and there will be a list of recommended books and other resources made available to you for your additional reading pleasure.

Application Process and Eligibility
What are the prerequisites for this No Code AI and Machine Learning program?

The prerequisites of the program include fundamentals of mathematics and statistics. If you do not possess either (or both) of them, you are expected to put in extra effort to learn them before the commencement of the program to be able to cope with the curriculum by MIT Professional Education. We, from Great Learning, will provide you with pre-work including sessions by Dr. Abhinanda Sarkar (Learning Instructor - Prework) and Dr. Georg Huettenegger (Industry Professional - Prework) that can be useful in understanding the fundamentals of mathematics and statistics.

What skills are needed to excel in no-code AI?

No programming or advanced mathematics knowledge is required to participate in the No Code AI and ML program. Familiarity with basic statistics is recommended to get the most out of the program.

What is the Application process?

Simply complete your online application form and then the Great Learning program team will review it to determine your fit with the program. If selected, you will receive an offer for the upcoming cohort and can then secure your seat by paying the fee

Fee and Payment
Can my employer sponsor the program fee?

We accept corporate sponsorships and can assist you with the process. For more information, please reach out to us at ncai.mit@mygreatlearning.com.

What is the refund policy?

If you communicate that you will drop the course before the cohort start date, the cost will be returned in full, minus a $300 USD administrative fee. Cancellation requests and reimbursements will be carried out under the following criteria.

  • Communication period: before the initial start date of the program cohort. Requests submitted after this date will not be eligible for reimbursement.
  • Means of communication: Requests must be submitted by email to the following address: ncai.mit@mygreatlearning.com Participants will not be eligible for reimbursement after the initial start date of the program cohort.
  • Exceptions: refunds due to medical reasons or other justified reasons, including force majeure, during the communication and payment period (21 days from the program’s cohort start date) will be exempt from paying the administrative fee of $300 USD provided that the request is carried out within the communication period, sent to the email address indicated above, and accompanied by corresponding documentation (medical, police, or psychiatric reports, etc.). Once the request has been received, the academic committee will review it to determine whether or not it is admissible.
What are my payment options?

 

You can pay for the program through Bank Transfer and Credit/Debit Cards. You can also opt for easy monthly installments, with flexible, convenient payment terms. Reach out to the registration office at +1 617 860 3529 to learn more.

For further details, please get in touch with us at ncai.mit@mygreatlearning.com.

 

No code AI and machine learning
Why no code AI and machine learning?

Businesses are starting to adopt no-code approaches to reduce costs, improve the efficiency of their existing solutions and accelerate time to market. The no-code approach enables AI and ML for everyone, making processes more scalable. Even professionals with no coding experience can now apply these advanced technologies to build intelligent solutions and help make informed decisions.

What is the future of no-code AI and machine learning?

The post-pandemic shift has led to increased adoption of digital technologies. Gartner projects a 23% increase in the global market for no-code tools and development. There is a steady growth in the use of no-code approaches due to their effectiveness in addressing some of tech’s most significant challenges- digitizing workflows, improving customer and employee experiences, and boosting the efficiency of operational teams

Still have queries? Contact Us

Please fill in the form and a Program Advisor from Great Learning will reach out to you. You can also reach out to us at ncai.mit@mygreatlearning.com or +1 844 441 1717 (Toll-Free)

Application Closes 25th Apr 2024

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Check out the program and fee details in our brochure

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MIT Professional Education is collaborating with online education provider Great Learning to offer No Code AI and Machine Learning: Building Data Science Solutions. This program leverages MIT's leadership in innovation, science, engineering, and technical disciplines developed over years of research, teaching, and practice. Great Learning collaborates with institutions to manage enrollments (including all payment services and invoicing), technology, and participant support. Accessibility

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