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Product Name

Professional Diploma in Artificial Intelligence

Module Name

NICF-Introduction to Python and AI for Data Science (SF)

# Activities Duration (Hours)
1

E Learning 1

IU1 Introduction to AI

IU2 Introduction to Machine Learning

IU3 Introduction to Text Analytics

3
2

E Learning 2

IU4 Introduction to Natural Language processing 

IU5 Introduction to Image processing 

IU6 Introduction to Bots

3
3

Flipped Class 1

IU1 Introduction to AI

IU2 Introduction to Machine Learning 

IU3 Introduction to Text Analytics

3
4

Assignment 1

IU1 Introduction to AI

IU2 Introduction to Machine Learning 

IU3 Introduction to Text Analytics

3
5

Flipped Class 2

IU4 Introduction to Natural Language processing

IU5 Introduction to Image processing 

IU6 Introduction to Bots

3
6

Assignment 2 

IU4 Introduction to Natural Language processing

IU5 Introduction to Image processing  

IU6 Introduction to Bots

3
7

E Learning 3 

IU7 Python Basics 

IU8 Using Lists in Python 

IU9 Functions and Packages 

3
8

Flipped Class 3 

IU7 Python Basics 

IU8 Using Lists in Python 

IU9 Functions and Packages 

3
9

Assignment 3 

IU7 Python Basics 

IU8 Using Lists in Python 

IU9 Functions and Packages 

3
10

E Learning 4 

IU10 Programming and Statistics with NumPy

3
11

Elearning 5  

IU11 Plotting with Matplotlib 

IU12 Pandas in Python 

3
12

Flipped Class 4 

IU10 Programming and Statistics with NumPy 

IU11 Plotting with Matplotlib 

IU12 Pandas in Python 

3
13

Assignment 4

IU10 Programming and Statistics with NumPy

3
14

Assignment 5  

IU11 Plotting with Matplotlib

3
15

Assignment 6 

IU12 Pandas in Python 

3
16

Project Mentoring 1

3
17

Project Implementation Support 1

3
18

Project Implementation Support 2

3
19

Project Implementation Support 3

3
20

Project Implementation Support 4

3
21

Summative Assessment 

0.5
Total Duration
60.5
Product Name

Professional Diploma in Artificial Intelligence

Module Name

NICF-Applied Machine Learning (SF)

# Activities Duration (Hours)
1

E - Learning 1 

IU1 High level Data Science process

IU2 Introduction to Supervised Learning - Regression 

3
2

Flipped Class 1 

IU1 High level Data Science process 

IU2 Introduction to Supervised Learning - Regression 

3
3

Assignment 1

IU1 High level Data Science process 

IU2 Introduction to Supervised Learning - Regression 

3
4

E Learning 2

IU3 Introduction to Supervised Learning - Classification

IU4 Improving Machine Learning Models

3
5

Flipped Class 2

IU3 Introduction to Supervised Learning - Classification

IU4 Improving Machine Learning Models

3
6

Assignment 2

IU3 Introduction to Supervised Learning - Classification

IU4 Improving Machine Learning Models

3
7

E Learning 3

IU5 Decision Tree and Ensemble Methods 

IU6 Optimization-Based Methods 

IU7 Introduction to Unsupervised Learning - Clustering

3
8

Flipped Class 3

IU5 Decision Tree and Ensemble Methods 

IU6 Optimization-Based Methods 

IU7 Introduction to Unsupervised Learning - Clustering

3
9

Assignment 3

IU5 Decision Tree and Ensemble Methods 

IU6 Optimization-Based Methods

3
10

E- Learning 4 

IU8 The Research Process of providing data 

IU9 Planning for Analysis

3
11

E- Learning 5

IU10 Research Claims

IU11 Reliability and validity 

IU12 Correlational and Experimental Design

3
12

Flipped Class -4 

IU8 The Research Process of providing data  

IU9 Planning for Analysis

IU10 Research Claims 

IU11 Reliability and validity  

IU12 Correlational and Experimental Design

3
13

Assignment -4

IU8 The Research Process of providing data   

IU9 Planning for Analysis  

3
14

Assignment -5 

IU10 Research Claims

3
15

Assignment -6 

IU11 Reliability and validity   

IU12 Correlational and Experimental Design

3
16

Project Mentoring 1

3
17

Project Implementation Support 1

3
18

Project Implementation Support 2

3
19

Project Implementation Support 3

3
20

Project Implementation Support 4

3
21

Summative Assessment

0.5
Total Duration
60.5
Product Name

Professional Diploma in Artificial Intelligence

Module Name

NICF-Advanced Techniques in Data Analytics (SF)

# Activities Duration (Hours)
1

E - Learning 1  

IU1 Quadratic Equations and functions 

IU2 Calculus foundations

3
2

Flipped Class 1 

IU1 Quadratic Equations and functions  

IU2 Calculus foundations

3
3

Assignment 1

IU1 Quadratic Equations and functions  

IU2 Calculus foundations

3
4

E Learning 2

IU3 Derivatives and optimization

IU4 Vectors

IU5 Matrices

 

3
5

Flipped Class 2

IU3 Derivatives and optimization 

IU4 Vectors

IU5 Matrices

3
6

Assignment 2

IU3 Derivatives and optimization 

IU4 Vectors

IU5 Matrices

3
7

E Learning 3

IU6 Statistics Fundamentals

IU7 Probability Fundamentals 

3
8

Flipped Class 3

IU6 Statistics Fundamentals 

IU7 Probability Fundamentals 

3
9

Assignment 3

IU6 Statistics Fundamentals 

IU7 Probability Fundamentals 

3
10

E- Learning 4 

IU8 Legal and ethical foundations in Data practice 

IU9 Bias in Data processing and Data privacy

IU10 Business and Ethical Data usage

3
11

E- Learning 5

IU11 Complex algorithms and accountability 

IU12 IRAC Case study 

3
12

Flipped Class -4  

IU8 Legal and ethical foundations in Data practice 

IU9 Bias in Data processing and Data privacy

IU10 Business and Ethical Data usage

IU11 Complex algorithms and accountability 

IU12 IRAC Case study 

3
13

Assignment -4

IU8 Legal and ethical foundations in Data practice 

IU9 Bias in Data processing and Data privacy 

 

3
14

Assignment -5 

IU10 Business and Ethical Data usage 

IU11 Complex algorithms and accountability  

 

3
15

Assignment -6 

IU12 IRAC Case study

3
16

Project Mentoring 1

3
17

Project Implementation Support 1

3
18

Project Implementation Support 2

3
19

Project Implementation Support 3

3
20

Project Implementation Support 4

3
21

Summative Assessment

0.5
Total Duration
60.5
Product Name

Professional Diploma in Artificial Intelligence

Module Name

NICF-Deep Learning Foundations (SF)

# Activities Duration (Hours)
1

E Learning 1

IU1 Introduction to Deep Learning

IU2 Setting up Deep Learning environment

3
2

E Learning 2

IU3 Image Classification problem

IU4 Multi Class Classification using Logistic Regression

IU5 Training and Evaluating Logistic regression

3
3

Flipped Class 1

Introduction to Deep Learning  and Multi Class Classification using Logistic Regression  

3
4

Assignment 1

IU1 Introduction to Deep Learning 

IU2 Setting up Deep Learning environment 

IU3 Image Classification problem 

IU4 Multi Class Classification using Logistic Regression 

IU5 Training and Evaluating Logistic regression

3
5

Flipped Class 2

IU6 Multi-Layer Perception

3
6

Assignment 2 

IU6 Multi-Layer Perception

3
7

E Learning 3  

IU7 Applications of Convolution Neural Network(CNN)

IU8 Training and Evaluating CNN model

3
8

Flipped Class 3 

IU7 Applications of Convolution Neural Network(CNN) 

IU8 Training and Evaluating CNN model

3
9

Assignment 3 

IU7 Applications of Convolution Neural Network(CNN) 

IU8 Training and Evaluating CNN model

3
10

E Learning 4 

IU9 Time series forecasting with Recurrent Neural Network(RNN) 

IU10 Training and Evaluating a recurrent model

3
11

Elearning 5  

IU11 Text Classification with LSTM

IU12 Training and Evaluating using Text Classification

3
12

 Flipped Class 4 

Time series forecasting  with RNN and Text Classification with LSTM

3
13

Assignment 4

IU9 Time series forecasting with Recurrent Neural Network(RNN)  

3
14

Assignment 5  

IU10 Training and Evaluating a recurrent model

3
15

Assignment 6 

IU11 Text Classification with LSTM

IU12 Training and Evaluating using Text Classification

3
16

Project Mentoring 1

3
17

Project Implementation Support 1

3
18

Project Implementation Support 2

3
19

Project Implementation Support 3

3
20

Project Implementation Support 4

3
21

Summative Assessment 

0.5
Total Duration
60.5
Product Name

Professional Diploma in Artificial Intelligence

Module Name

NICF-Reinforcement Learning Foundations (SF)

# Activities Duration (Hours)
1

E Learning 1

IU1 Introduction to Reinforcement Learning

IU2 Applications of Reinforcement Learning

3
2

E Learning 2

IU3 Bandit Framework

IU4 The Reinforcement Learning Problem

3
3

Flipped Class 1

IU1 Introduction to Reinforcement Learning 

IU2 Applications of Reinforcement Learning 

IU3 Bandit Framework 

IU4 The Reinforcement Learning Problem

3
4

Assignment 1

IU1 Introduction to Reinforcement Learning 

IU2 Applications of Reinforcement Learning 

IU3 Bandit Framework 

IU4 The Reinforcement Learning Problem

3
5

Flipped Class 2

IU5 Markov Decision Process

IU6 Basics of Dynamic Programming(DP)

3
6

Assignment 2

IU5 Markov Decision Process 

IU6 Basics of Dynamic Programming(DP)

3
7

E Learning 3 

IU7 Monte Carlo Learning(MC)

IU8 Temporal Difference Learning(TD)

IU9 Comparison between DP, MC and TD

3
8

Flipped Class 3 

IU7 Monte Carlo Learning(MC) 

IU8 Temporal Difference Learning(TD) 

IU9 Comparison between DP, MC and TD

3
9

Assignment 3 

IU7 Monte Carlo Learning(MC) 

IU8 Temporal Difference Learning(TD) 

IU9 Comparison between DP, MC and TD

3
10

E Learning 4 

IU10 SARSA algorithm  vs Q-Learning Algorithm

3
11

Elearning 5  

IU11 Q Learning with Linear function Approximation

IU12 Policy gradient methods and Actor critic methods 

3
12

Flipped Class 4 

IU10 SARSA algorithm  vs Q-Learning Algorithm 

IU11 Q Learning with Linear function Approximation 

IU12 Policy gradient methods and Actor critic methods 

3
13

Assignment 4

IU10 SARSA algorithm  vs Q-Learning Algorithm 

3
14

Assignment 5  

IU11 Q Learning with Linear function Approximation 

3
15

Assignment 6 

IU12 Policy gradient methods and Actor critic methods 

3
16

Project Mentoring 1

3
17

Project Implementation Support 1

3
18

Project Implementation Support 2

3
19

Project Implementation Support 3

3
20

Project Implementation Support 4

3
21

Summative Assessment 

0.5
Total Duration
60.5
Product Name

Professional Diploma in Artificial Intelligence

Module Name

NICF-Develop Applied AI Solutions (SF)

# Activities Duration (Hours)
1

E Learning 1

IU1 Introduction to NLP

3
2

E Learning 2

IU2 Neural Models for Machine Translation and Conversation Generation

3
3

Flipped Class 1

IU1 Introduction to NLP 

IU2 Neural Models for Machine Translation and Conversation Generation 

3
4

Assignment 1

IU1 Introduction to NLP  

IU2 Neural Models for Machine Translation and Conversation Generation 

3
5

Flipped Class 2

IU3 Deep Semantic Similarity Model and Its Applications

3
6

Assignment 2 

IU3 Deep Semantic Similarity Model and Its Applications

3
7

E Learning 3 

IU4 Natural Language Understanding

3
8

Flipped Class 3 

IU4 Natural Language Understanding

3
9

Assignment 3 

IU4 Natural Language Understanding

3
10

E Learning 4 

IU5 Deep Reinforcement Learning

3
11

Elearning 5  

IU6 Vision-Language Multimodal Intelligence  

3
12

Flipped Class 4 

IU5 Deep Reinforcement Learning 

IU6 Vision-Language Multimodal Intelligence  

3
13

Assignment 4

IU5 Deep Reinforcement Learning

3
14

Assignment 5  

IU6 Vision-Language Multimodal Intelligence  -1 

3
15

Assignment 6 

Vision-Language Multimodal Intelligence  - 2

3
16

Project Mentoring 1

3
17

Project Implementation Support 1

3
18

Project Implementation Support 2

3
19

Project Implementation Support 3

3
20

Project Implementation Support 4

3
21

Summative Assessment 

0.5
Total Duration
60.5