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AI Foundation For Beginners

Objectives

• In this course, you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning, and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini-project.

• This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.

Target Audience

  • Consultants looking to integrate AI
  • Those looking to learn the essential aspects of next-gen concepts
  • Professionals seeking a job in the AI tech industry

Course Modules

Module 1: Supervised learning: classification and regression

  • Bias-variance trade-off
  • Logistic regression as a classifier
  • Measuring classifier performance
  • Support vector machines
  • Neural networks
  • Random forests

Module 2: Setting up an end-to-end supervised learning pipeline using scikit-learn

  • Working with data files
  • Imputation of missing values
  • Handling categorical variables
  • Visualizing data

Module 3: Unsupervised learning: clustering, anomaly detection

  • Principal component analysis
  • Autoencoders

Module 4: Advanced neural network architectures

  • Convolutional neural networks for image analysis
  • Recurrent neural networks for time structured data.
  • The long short-term memory cell

Module 5: Practical examples of problems that AI can solve, e.g.

  • Why do we need statistics?
  • Categories of statistics, statistical terminology, types of data, measures of central tendency, and measures of spread
  • Correlation and covariance, standardization and normalization, probability and the types, hypothesis testing, chi-square testing, ANOVA, normal distribution, and binary distribution

Module 6: Machine Learning

  • Image analysis
  • Forecasting complex financial series, such as stock prices,
  • Complex pattern recognition
  • Natural language processing
  • Recommender systems

Module 7: Software platforms used for AI applications:

  • TensorFlow, Theano, Caffe, and Keras
  • AI at scale with Apache Spark: Mila

Module 8: Understand limitations of AI methods: modes of failure, costs, and common difficulties

  • Overfitting
  • Biases in observational data
  • Missing data
  • Neural network poisoning

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    Program Schedules : Session Starts From

    9th July 2021

    Online Live

    23rd July 2021

    Classroom

    Certification (AI Foundation For Beginners)

    • In this course, you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning, and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini-project.

    • This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.

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