Artificial Intelligence

Overview

The adoption of Artificial Intelligence (AI) technologies is extensively expanding in recent times. Applications of AI encompass personal assistants, self-driving cars, surveillance systems, robotic manufacturing, financial services, cyber security, machine translation web search, video games, code analysis, and product recommendations. Such applications are intended to use AI techniques to interpret information from a wide variety of sources and use it to enable intelligent goal-directed behaviour. Frequently, modern AI encompasses self-learning systems that are trained on massive amounts of data, and/or interacting intelligent agents that perform distributed reasoning and computation. AI connects sensors with algorithms as well as human-computer interfaces and extends itself into big networks of smart devices. AI is a booming research field that is one of the powerful forces of today's economy and as such is having an increasing impact on society, both economic and social.


Considering the importance of AI and its application in real life, International College for Security Studies (ICSS) offers Certificate Program in Artificial Intelligence and Machine Learning. It is an intensive application-oriented, real-world scenario-based program in AI and ML. It is an intensive skill-oriented, practical training program required for building business models for analytics. It is designed to provide the participant adequate exposure to the diversity of applications that can be built using a wide range of techniques covered under this program. By blending standard classes with recitations and lab sessions, our program certifies that each student masters the theoretical foundations and acquires hands-on experience in each subject. Besides a core of theoretical units, the program embraces more specialty-oriented electives so that students can round up their skills on leading-edge applications and techniques.

Course Modules

  • 1.1 Fundamentals of AI and ML
  • 1.2 Programming Basics
  • 1.3 Decoding Artificial Intelligence
  • 1.4 Fundamentals of Machine learning and Deep learning
  • 1.5 Machine learning workflow
  • 1.6 Performance metrics
  • 1.7 Introduction to Expert and Fuzzy logic systems
  • 2.1 Data exploration (histograms, bar chart, box plot, line graph, scatter plot)
  • 2.2 Qualitative and Quantitative Data
  • 2.3 Measure of Central Tendency (Mean, Median and Mode),
  • 2.4 Measure of Positions (Quartiles, Deciles, Percentiles and Quantiles)
  • 2.5 Measure of Dispersion (Range, Median, Absolute deviation about median, Variance and Standard deviation), Anscombe's quartet
  • 3.1 Fundamentals of Python
  • 3.2 Python Data Structures
  • 3.3 Python Programming Fundamentals
  • 3.4 Working with Data in Python
  • 3.5 Working with NumPy Arrays
  • 4.1 Fundamentals of Data Analytics
  • 4.2 Statistical Analysis and Business Applications
  • 4.3 Python Environment Setup and Essentials
  • 4.4 Mathematical Computing with Python (NumPy)
  • 4.5 Scientific Computing with Python (SciPy)
  • 4.6 Data Manipulation with Pandas
  • 4.7 ML with Scikit–Learn
  • 4.8 NLP with Scikit Learn
  • 4.9 Data Visualization in Python using Matplotlib
  • 4.10 Web Scraping with Beautiful Soup
  • 4.11 Python Integration with Hadoop MapReduce and Spark
  • 5.1 Overview of ML
  • 5.2 Meaning and Definition
  • 5.3 Importance of ML
  • 5.4 Correlation between AI and ML
  • 5.5 Data Preprocessing
  • 5.6 Supervised Learning
  • 5.7 Feature Engineering
  • 5.8 Supervised Learning-Classification
  • 5.9 Unsupervised Learning
  • 5.10 Time Series Modelling
  • 5.11 Ensemble Learning
  • 5.12 Recommender Systems
  • 5.13 Text Mining
  • 6.1 AI and Deep Learning
  • 6.2 Artificial Neural Network
  • 6.3 Deep Neural Network and Tools
  • 6.4 Deep Neural Net Optimization, Tuning, and Interpretability
  • 6.5 Convolutional Neural Net (CNN)
  • 6.6 Recurrent Neural Networks
  • 6.7 Autoencoders
  • 6.8 Understanding of TensorFlow and Keras

Capstone Project: Artificial Intelligence

In this capstone, students will apply their deep learning knowledge and expertise to a real-world challenge. They will use a library of their choice to develop and test a deep learning model. They are required to load and pre-process data for a real problem, build the model, and authenticate it. Then learners will present a project report to showcase the legitimacy of their model and their skill in the field of Deep Learning.

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Program Details

By the end of this course, students will:

  • Gain a comprehensive understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing
  • Learn in-depth knowledge about Keras and TensorFlow, elements of a Keras model, Keras on GPU, and more
  • Gain expertise in mathematical computing using the NumPy and Scikit-Learn package
  • Implement deep learning algorithms and comprehend neural networks
  • Gain know-how in mathematical computing using the NumPy and Scikit-Learn package
  • Learn deep learning techniques like object detection using computer vision
  • Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modeling
  • Explore tools such as Keras to build computer vision applications
  • Comprehend how to apply machine learning and deep learning with Natural Language Processing (NLP)
  • Understand the fundamentals of speech recognition and do hands-on exercises
  • Carry out distributed and parallel computing using high-performance GPUs
  • Execute text-to-speech conversion with automated speech recognition
  • Work on voice-assistance devices and build Alexa skills
  • Learn about natural language understanding and natural language generation
  • Use Python and TensorFlow to know reinforcement learning theory
  • Learn how to resolve reinforcement learning issues with a diversity of approaches
  • Comprehend the rudiments of NLP using the most popular library, Python’s Natural Language Toolkit (NLTK)

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