No Code AI and Machine Learning: Building Data Science Solutions 

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

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