Artificial Intelligence & Data Science

Artificial Intelligence

As humans has ability to record data and process its pattern in brain, Artificial intelligence is the ability that can be imparted to computers which enables these machines to understand data, learn from the data, and make decisions based on patterns hidden in the data, or inferences that could otherwise be very difficult (to almost impossible) for humans to make manually. AI also enables machines to adjust their “knowledge” based on new inputs that were not part of the data used for training these machines.

Data Science

Data Science, is perhaps the most spelled word from today's industry and brings enormous benefits to entire business operations. Proven techniques like Python and R language with the right fitment of various algorithms make powerful supervised and unsupervised programs to bring intended benefits. This module teaches basic statistics, Python, various algorithms, and required machine learning and deep learning fundamentals to solve real-time business issues. The right amount of practical experience will help the participants to apply the theories in a useful context.

  • Maths and Statistics – Application in DS
  • Basic of Python
  • Supervised and unsupervised learning techniques
  • Evolution of AI
  • An introduction of Neural Network
  • Natural Language processing – an overview
  • Introduction to networking, Data Communication
  • Origin of Cloud Computing, Basic Concepts and Terminology
  • Goals and Benefits, Risks and Challenges, Cloud Characteristics
  • Cloud Delivery Models, Cloud Deployment Models
  • Data center Technology
  • Virtualization
  • Cloud Applications
  • Introduction to Cybersecurity & Ethical Hacking
  • Cryptography & it's Types
  • Computer Networks - Architecture, Threats & Attacks
  • Application & Web Security - Architecture, Threats & Attacks
  • Vulnerability Analysis & System Hacking - An overview
  • Sniffing, Hijacking - An Introduction
  • What is Robotic Process Automation?
  • Scope & Techniques of Automation
  • Techniques of automation
  • Future of Automation
  • Benefits of RPA
  • Components of RPA
  • RPA platforms
  • About UiPath
  • UiPath Studio
  • UiPath Robot
  • UiPath Orchestrator
  • Downloading and installing UiPath Studio
  • Learning UiPath Studio Projects
  • Task Recorder
  • Advanced UI Interactions
  • Input Methods
  • Output Methods
  • Step-by-Step examples using Recorder
  • Intoduction: Big Data and Hadoop
  • Concepts of HDFS
  • MapReduce – An introduction
  • Apache Hive
  • HBase – An overview
  • Apache PIG - Apache SPARK
  • Demo and Hands on Project
  • Introduction to Python
  • Programming in Python
  • Python for Data Science
  • Visualization in Python
  • Exploratory Data Analysis
  • Inferential statistics
  • Hypothesis Testing
  • EDA Case Study
  • Introduction to Machine Learning
  • Supervised Learning
  • Linear Regression
  • Assignment: Linear Regression
  • Logistic Regression
  • Support Vector Machine
  • Unsupervised learning
  • Principal Component Analysis
  • Clustering
  • K-Means Clustering
  • Hierarchical Clustering
  • Assignment: Unsupervised + Supervised
  • Case Study
  • Tree Models
  • Featurization, Model Selection & Tuning
  • Model performance measure
  • Bagging and Boosting
  • Hyperparameter tuning
  • Introduction to Neural Networks
  • Neural Networks Assignment
  • Recommendation Systems
  • Computer Vision
  • NLP
Trainer profile

Dr.S.Famila M.E.,Ph.D.

Principal Educationist

She started her career as Lecturer and explored her knowledge in the Industry as a technocrat, She is pursuing her Ph.D in Anna University. She has 14 years of experience in teaching, 3 years in the Industry and 5 years Experience in Research in Machine and Deep learning , She owns separate Research solutions and is guiding many researches in the new coming technology.

Certificate of Completion