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.
Machine learning and AI techniques have generated significant interest across industries. With the emergence of no-code platforms, professionals from diverse sectors can now harness the power of these technologies without prior programming knowledge. These intuitive, interactive user interfaces enable users to efficiently classify information, conduct data analysis, and develop precise predictive models, eliminating the need for complex programming. In this program, you will learn to use different no-code tools to create innovative solutions.
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 ncai.mit@mygreatlearning.com or call +1 617 860 3529
- Gain a holistic understanding of the AI landscape for a variety of business use cases.
- Gain a strong conceptual understanding of the 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 problems with AI
Program Curriculum
The No-Code AI and Machine Learning Program is a 12-week course that offers a comprehensive learning experience. Esteemed MIT Faculty lead the program and incorporate a blended learning approach with recorded lectures, real-life case studies, hands-on projects, interactive quizzes, mentor-led sessions, and engaging 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
This module focuses on a general overview of the four blocks of the No Code AI and Machine Learning Program. This module covers the following:
Week 2
Module 2: Data Exploration - Structured Data
Learn the basic principles of applying data exploration techniques, such as dimensionality projection and clustering on structured data. This module will cover the following:
- 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
In this module, learners will understand the concept of linear regression and how it can be used with historical data to build models that predict future outcomes. Here’s what this module will cover:
- 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
In this module, learners will understand the concept of classification and understand how tree-based models achieve the prediction of outcomes that fall into two or more categories. Here’s what this module will cover:
- 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
In this module, learners will 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. Here’s what this module will cover:
- 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
In this module, learners will understand the idea behind recommendation systems and potential business applications. Here’s what this module will cover:
- 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
In this module, learners will 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. Here’s what this module will cover:
- 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
In this module, learners will 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 backpropagation cycle to minimize prediction error. Here’s what this module will cover:
- 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 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
In this module, learners will 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. Here’s what this module will cover:
- 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
In this module, learners will be able 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.
