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Dec 10, 2022 - Mar 19, 2023
Course Length
12 Weeks
Course Fee
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In our age of information and insights, the success of a business relies on how well we utilize the data in creating algorithms and making predictions. As organizations realize this, no-code approaches to implementing emerging technologies are becoming popular and essential to know. A no-code approach to data science and AI can be a game-changer. MIT Professional Education offers the No Code AI and Machine Learning: Building Data Science Solutions Program to utilize the full power of AI and build intelligent solutions from data without having to write a single line of code.


In this 12-week program, you will learn to use AI and Machine Learning to make data-driven business decisions by understanding the theory and practical applications of supervised and unsupervised learning, neural networks, recommendation engines, computer vision, etc. Leverage the power of AI and data science without writing a single line of code.

Upon successfully completing the program, you will receive a certificate of completion from MIT Professional Education.

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.

Contact Great Learning for more information at or call +1 617 860 3529

Learning Outcomes
  • Gain a holistic understanding of AI landscape for variety of business use cases
  • Gain strong conceptual understanding of most widely used algorithms
  • Ability to build practical AI solutions using no code tool
  • Gain practical insights into various nuances involved in implementing AI solutions in the industry
  • Develop critical thinking and problem solving skills required to tackle business problem with AI

Program Curriculum 

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.

These 12 weeks will be distributed in the following manner:

Module 1: Introduction to AI Landscape
Module 2: Data Exploration - Structured Data, Networks, and Graphical Models
Module 3: Prediction Methods - Regression
Module 4: Decision Systems
Module 5: Data Exploration - Unstructured Data
Module 6: Recommendation Systems
Module 7: Data Exploration - Temporal Data
Module 8: Prediction Methods - Deep Learning and Neural Networks
Module 9: Computer Vision Methods
Module 10: Workflows and Deployment

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 of 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 system
  • 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 involving Neural Networks
  • Learn about the encoding process taking place in the neural network layers and how non-linearities are introduced
  • Understand how 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 back propagation 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.

Who Should Attend
  • Business leaders who want to learn how AI & ML solutions can be built with no code platform
  • Operations and Product Managers interested in leading with data and developing quick proof of concept solutions to drive new initiatives off the ground
  • Entrepreneurs, Consultants, and Solution-builders who want the ability to quickly build working prototypes or solutions for clients and stakeholders to establish feasibility and viability
  • Working professionals with non-technical background aspiring to lead AI and data-driven teams and build innovation initiatives using AI and ML technologies

In today’s fast-changing business landscape, acquiring industry knowledge and skills through traditional, concept-led courses can be time-consuming and ineffective. With mentored learning, you can accelerate your learning, increase productivity, have a better grasp of the subject and discover new problem-solving perspectives. 

Mentors of the Applied Data Science program come with years of experience from top organizations and help you understand best practices and deliver the actionable know-how you need to succeed in your new role. You will be able to quickly gauge the topics taught by MIT Faculty, and gain insights into how these topics are applied at an organizational level. You will also solve real business problems with guidance from your mentors and will be ready to hit the ground running as soon as you walk out of the program.

How do mentorship sessions work?

Mentoring sessions occur in small groups that are called micro classes. You will be grouped with learners with similar years of experience and backgrounds so that the mentors can determine the right pace of teaching, level of techniques, and relevant case studies, to use in order to maximize the benefit. When learning in groups, you can also garner how a practical skill like data science is applied to different industry-specific problems. In addition, you will work with the mentors every weekend to brush up on topics and revisit the concepts you covered earlier.

Highlights of what mentors add to your learning experience:

Build tangible skills through interactive, hands-on coding walkthroughs
Gain Industry expertise from experienced data scientists at globally renowned companies such as Microsoft, Cognizant, Mu Sigma, etc.
Connect theoretical concepts to actual practical examples of how these analytic techniques are used across various industries
Engage with your mentor on a deeper level and get support and guidance when making the transition into a data science career
Prepare for your interviews and foster data-driven problem-solving within teams 

As an aspiring data science professional, Python and statistics play a valuable part in your toolkit. The pre-work lets you acquire foundational knowledge in these subjects and have a better understanding of concepts that are mandatory in data science, such as data handling, feature engineering, and predictive analysis. It helps you best understand the concepts during the live virtual sessions with MIT faculty. Quickly cope with the latest data science practices and implement them as you walk through the program. 

Who is the pre-work for?
The pre-work is specially designed for learners new to Python and basic statistics who do not have a very high programming background. It is also ideal for those looking to brush up on their programming knowledge and those who want to know the foundations required to kickstart their data science learning journey.

What does the pre-work comprise?
The pre-work will introduce you to Anaconda, an open-source data science software package that is the easiest way to execute Python for data science. This open-source platform contains the Jupyter Notebooks tool, which provides an interactive way to write and implement code using the Python programming language. 

Prework consists of 7.5 hours of video content along with practice quizzes.