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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery | Frequency Of Classes |
|---|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based | Weekends |
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | MIT Cambridge |
The fees for course No Code AI and Machine Learning Building Data Science Solutions is -
| Head | Amount |
| Programme fees | Rs. 2,10,000 + GST |
The Syllabus
Module 1: 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?
Module 2: 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.
Module 3: 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?
Module 4: 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.
Module 5: Data Exploration - Unstructured 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.
Module 6: 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 problem.
- Understand the ideas behind Collaborative Filtering Recommendation Systems.
Module 7: Data Exploration - Temporal Data
- 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.
Module 8: Prediction Methods - Neural Networks
- 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
Module 9: Computer Vision Methods
- 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.
Module 10: Workflows and Deployment
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