No Code AI and Machine Learning: Building Data Science Solutions offered by MIT University
- Private University
168 acre campus- Estd. 1861
No Code AI and Machine Learning: Building Data Science Solutions at MIT University Overview
Duration | 12 weeks |
Total fee | ₹1.94 Lakh |
Mode of learning | Online |
Course Level | UG Certificate |
No Code AI and Machine Learning: Building Data Science Solutions at MIT University Highlights
- Earn a certificate of completion from MIT
No Code AI and Machine Learning: Building Data Science Solutions at MIT University Course details
- For Business leaders who want to learn how AI & ML solutions can be built with no code platform
- For Operations and Product Managers
- For Entrepreneurs, Consultants, and Solution-builders
- For Working professionals with non-technical background
- 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
- 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
- This program leverages MIT's leadership in innovation, science, engineering, and technical disciplines developed over years of research, teaching, and practice
No Code AI and Machine Learning: Building Data Science Solutions at MIT University Curriculum
Introduction to the AI Landscape
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?
Data Exploration - 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
Prediction Methods - Regression
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?
Decision Systems
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
Data Exploration - Unstructured 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
Recommendation Systems
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
No Code AI and Machine Learning: Building Data Science Solutions at MIT University Faculty details
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No Code AI and Machine Learning: Building Data Science Solutions at MIT University Contact Information
77 Massachusetts Ave, Cambridge, MA 02139, USA
Cambridge ( Massachusetts)