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Fundamentals of Artificial Intelligence

Fundamentals of Artificial Intelligence

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Recorded content
Of Total 10 Hrs.
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Duration
3 Months (50 hours)
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LIVE sessions
4 Workshops
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Hands-On Learning
With Practice Modules
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Certificate
With License

Overview

This Fundamentals of Artificial Intelligence/Deep Learning/Generative AI Models training course teaches attendees how to use the Python programming language to build modern machine learning (ML) applications that incorporate the latest ML technologies such as generative AI, deep learning, natural language processing, and computer vision.

Learners are introduced to the basic concepts of Python, such as variables, data types, functions, and control flow. They also learn how to use the Anaconda computing environment, which comes with many valuable tools for data science.

Objective

  • Understand the basics of machine learning
  • Prepare data for machine learning
  • Build and evaluate machine learning models
  • Apply machine learning to real-world problems
  • Explore the latest trends in machine learning
  • Review core Python concepts
  • Use the Anaconda computing environment
  • Import and manipulate data with Pandas
  • Perform exploratory data analysis with Pandas and Seaborn
  • Understand Artificial Neural Networks (ANNs) and deep learning
Join Next Batch Starting on September 10th

Outline

  • • Anaconda Computing Environment
  • • Importing and manipulating Data with Pandas
  • • Exploratory Data Analysis with Pandas and Seaborn
  • • NumPy ndarrays versus Pandas Dataframes
    • • Developing predictive models with ML
    • • How Deep Learning techniques have extended ML
    • • Use cases and models for ML and Deep Learning

  • • Components of Neural Network Architecture
  • • Evaluate Neural Network Fit on a Known Function
  • • Define and Monitor Convergence of a Neural Network
  • • Hyperparameter tuning
  • • Evaluating Models
  • • Scoring New Datasets with a Model

  • • Preprocessing Tabular Datasets for Deep Learning Workflows
  • • Data Validation Strategies
  • • Architecture Modifications to Managing Over-fitting
  • • Regularization Strategies
  • • Deep Learning Classification Model example
  • • Deep Learning Regression Model example

  • • What happens if we do not have a rectangle of data as the input?
  • • Pre-processing sequence data (i.e., time series) to use as inputs to feed-forward ANN
  • • Exploring model architectures that can handle sequence data
  • o Recurrent Neural Network (RNN)
  • o Long Short Term Memory (LSTM)
  • o Transformers
  • • Extending model architecture to handle heterogenous (Sequence and non-sequence) data

  • • Common use cases for text data and deep learning
  • • Exploratory Data Analysis on text data
  • • Cleaning/pre-processing text data
  • • Understanding word embeddings
  • • Text Classification models
  • o Bag of Words approach
  • o RNN / LSTM modeling approaches
  • • Transfer learning with text classification models: using BERT
  • o Using Hugging Face to start with state-of-the-science models
  • o Fine-tuning the model on your datasets

  • • Common AI use cases with images
  • • Exploratory Data Analysis on image data
  • • Pre-processing images
  • • Data augmentation with existing images
  • • Image classification examples
  • o Image classification with ANN
  • o Image classification with convolutional neural networks
  • • Image classification and transfer learning:
  • o Using Hugging Face to start with state-of-the-science models
  • o Fine-tuning the model on your datasets
  • • Image segmentation and transfer learning
  • o Using Hugging Face to start with state-of-the-science models
  • o Fine-tuning the model on your datasets

  • Generative AI fundamentals
    • • Generating new content versus analyzing existing content
    • o Example use cases: text, music, artwork, code generation
    • o Ethics of generative AI
    • • Sequence Generation with RNN
    • o Recurrent neural networks overview
    • o Preparing text data
    • o Setting up training samples and outputs
    • o Model training with batching
    • o Generating text from a trained model
    • o Pros and cons of sequential generation
    • • Overview of current popular large language models (LLM)
    • o ChatGPT
    • o DALL-E 2
    • o Bing AI
    • • Medium-sized LLM in your environment
    • o Stanford Alpaca
    • o Facebook Llama
    • o Transfer learning with your data in these contexts

Training Materials

All Machine Learning with Python students receive courseware covering the topics in the class.

Software Requirements

A programming language like Python for code development, popular machine learning libraries and frameworks such as TensorFlow and PyTorch for model building, data manipulation tools like Pandas and NumPy, version control using Git, database management if needed, text editors for quick code edits, virtual environments to manage dependencies, containerization with Docker for reproducibility, cloud computing platforms for scalable resources, GPU support for deep learning, development workflow tools like DVC and MLflow, documentation and collaboration tools, testing and debugging utilities, deployment frameworks such as Flask or serverless options, monitoring and logging tools, and security measures like PySyft for privacy-sensitive applications. The choice of specific tools and libraries depends on the project's requirements and goals.

Why Online Bootcamps

Develop skills for real career growth

Cutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills

Learn by working on real-world problems

Capstone projects involving real world data sets with virtual labs for hands-on learning

Learn from experts active in their field, not out-of-touch trainers

Leading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.

Structured guidance ensuring learning never stops

24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

FAQs

  • The following are the top ten industries that are heavily utilizing AI applications:
  • • Education
  • • Healthcare & Medicine
  • • Retail and E-commerce
  • • Food Technology
  • • Banking and Finance
  • • Logistics and Transportation
  • • Travel
  • • Real Estate
  • • Entertainment and Sports
  • • Manufacturing

    You will be entitled to acquire the Masters in AI certificate, which will attest to your AI engineer abilities, provided you meet the following minimum requirements.

    Course Course completion certificate Criteria
    Introduction to Artificial Intelligence Course Required 85% of Online Self-paced completion and Pass Assessment test at 80%
    Data Science with Python Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, a score above 75% in course-end assessment, and successful evaluation in at least 1 project
    Machine Learning Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom and successful evaluation in at least 1 project
    Deep Learning with Keras and TensorFlow Required Attend 1 Live Virtual Classroom and successful evaluation in at least 1 project and score 70% for course-end assessment.
    Advanced Deep Learning and Computer Vision Required Attend 1 LVC batch, Pass a Project, Pass an Assessment test 70%
    AI Capstone Project Required Attendance of 1 Live Virtual Classroom and successful completion of the capstone project

  • The courses for which you will receive IBM credentials are as follows:
  • • Python for Data Science
  • • Deep Learning with Keras and Tensorflow

  • Math principles such as statistics, probability, linear algebra, calculus, and Bayesian algorithms should be understood by professionals who want to start AI careers. Statistics, learning theory, problem-solving, classical mechanics, and language processing are all skills they'll need. It is also suggested that you know at least one programming language, data structure, and logic.

  • If you feel unsatisfied, you can cancel your ongoing enrollment. After deducting an administration charge, we will reimburse the course money. Please see our Refund Policy for more information.

  • SkillaMatrix JobAssist program is an India-specific offering in partnership with IIMJobs.com to help you land your dream job. With the JobAssist program, we will offer extended support for certified learners who are looking for a job switch or starting with their first job. Upon successful completion of the Artificial Intelligence Course, you will be eligible to apply for this program and your details will be shared with IIMJobs. As part of this program, IIMJobs will offer the following exclusive programs:
  • • IIMJobs Pro-Membership of 6 months for free
  • • Resume-building assistance to create a powerful resume
  • • Spotlight on IIMJobs for highlighting your profile to recruiters