Duration: 80 hours / 10 days

24 Lessons

This bootcamp is designed to provide participants with a comprehensive understanding of Python for various applications in data science, machine learning (ML), artificial intelligence (AI), and deep learning (DL). Participants will learn essential Python programming skills and explore libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, and Keras to analyze data, build ML models, and develop AI applications.

This 10-day course outline provides a structured curriculum covering essential topics in Python for data science, machine learning, artificial intelligence, and deep learning. With a combination of lectures, hands-on exercises, and project work, participants will gain practical skills and experience to apply Python effectively in various domains.

DAY 1: INTRODUCTION TO PYTHON FOR DATA SCIENCE

Module 1: Introduction to Python Programming

Module 2: Data Manipulation with NumPy and Pandas

Module 3: Data Visualization with Matplotlib and Seaborn

Hands-on Data Analysis Project

DAY 2: ADVANCED DATA ANALYSIS TECHNIQUES

Module 1: Data Preprocessing and Cleaning

Module 2: Exploratory Data Analysis (EDA)

Module 3: Statistical Analysis with SciPy

Hands-on Data Analysis Project (Continued)

DAY 3: INTRODUCTION TO MACHINE LEARNING

Module 1: Introduction to Machine Learning

Module 2: Supervised Learning Algorithms

Module 3: Model Evaluation and Validation

Hands-on Machine Learning Project

DAY 4: ADVANCED MACHINE LEARNING TECHNIQUES

Module 1: Ensemble Learning Techniques

Module 2: Unsupervised Learning Algorithms

Module 3: Introduction to Deep Learning

Hands-on Deep Learning Project

DAY 5: INTRODUCTION TO ARTIFICIAL INTELLIGENCE

Module 1: Introduction to Artificial Intelligence (AI)

Module 2: Natural Language Processing (NLP)

Module 3: Introduction to Computer Vision

Hands-on AI Project

DAY 6 – 10: ADVANCED TOPICS AND PROJECT WORK

Module 1: Advanced Topics in Data Science and AI

Module 2: Project Work and Mentoring

Module 3: Project Work and Collaboration

Module 4: Project Presentations and Evaluation

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Duration: 24 hours / 3 days

6 Lessons

• Learn about Apache Spark and the Spark 3.0 architecture
• Build and interact with Spark DataFrames using Spark SQL
• Learn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectively
• Read, transform, and understand data and use it to train machine learning models
• Build machine learning models with MLlib and ML
• Learn how to submit your applications programmatically using spark-submit
• ML Algorithms: common learning algorithms such as classification, regression, clustering, and collaborative filtering
• Featurization: feature extraction, transformation, dimensionality reduction, and selection
• Pipelines: tools for constructing, evaluating, and tuning ML Pipelines
• Persistence: saving and load algorithms, models, and Pipelines
• Utilities: linear algebra, statistics, data handling, etc

DAY 1

Module 1: Introduction to Spark

Module 2: Resilient Distributed Datasets

DAY 2

Module 3: DataFrames

Module 4: Prepare Data for Modeling

DAY 3

Module 5: Introducing MLlib

Module 6: Introducing the ML Package

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These courses are designed to provide participants with a comprehensive understanding of generative artificial intelligence (AI) techniques and algorithms.

Version 1 – View the Curriculum

Version 2 – View the Curriculum

Version 3 – View the Curriculum

Duration: 80 hours / 10 days

10 Lessons

Generative AI refers to a category of artificial intelligence models designed to generate new data similar to the data they were trained on. These models can create content across various domains, including text, images, music, and even code.

Day 1: Generative AI And Its Industry Applications

Day 2: Foundations of Python Programming

Day 3: Python Modules And Object-Oriented Programming

Day 4: Introduction to NumPy and Pandas For Data Manipulation

Day 5: Introduction to TensorFlow and Keras

Day 6: Understanding Generative Adversarial Networks (GANs)

Day 7: Variational Autoencoders (VAEs)

Day 8: Conditional GANs and VAEs

Day 9: Style Transfer and Image Generation

Day 10: Natural Language Processing (NLP) with Generative Models

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Duration: 48 hours / 6 days

12 Lessons

Duration: 16 hours / 2 days

6 Lessons

This course is designed for junior engineers who want to learn about generative artificial intelligence (AI) techniques and algorithms. Participants will gain a fundamental understanding of generative models and how they can be applied to generate new data samples, images, text, and other types of content. The course includes hands-on labs to provide practical experience in building and training generative AI models.

This two-day course provides a condensed yet comprehensive introduction to generative AI for junior engineers. Through a combination of lectures and hands-on labs, participants will gain a solid understanding of fundamental generative models and practical experience in building and training generative AI models using Python and popular deep learning frameworks.

DAY 1: INTRODUCTION TO GENERATIVE AI

Module 1: Overview of Generative AI

Module 2: Fundamentals of Probability and Statistics for Generative AI

Module 3: Introduction to GANs

Hands-on Lab: Building and Training a Simple GAN

DAY 2: ADVANCED GENERATIVE MODELS

Module 1: Variational Autoencoders (VAEs)

Module 2: Autoencoders and Variants

Module 3: Adversarial Autoencoders (AAEs)

Hands-on Lab: Training a Variational Autoencoder (VAE)

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Duration: 14 hours / 2 days

6 Lessons

This course is tailored for senior engineers seeking an in-depth understanding of generative artificial intelligence (AI) techniques and algorithms.

Participants will delve into advanced generative models such as generative adversarial networks (GANs), variational autoencoders (VAEs), and autoregressive models. The course includes hands-on labs to provide practical experience in building and training sophisticated generative AI models.

This two-day course provides senior engineers with a deep dive into advanced generative AI concepts and techniques. Through a combination of lectures, hands-on labs, and discussions, participants will gain practical experience and expertise in building and deploying sophisticated generative AI models for various applications.

DAY 1: ADVANCED GENERATIVE MODELS

Module 1: Introduction to Generative AI

Module 2: Generative Adversarial Networks (GANs)

Module 3: Variational Autoencoders (VAEs)

Hands-on Lab: Building and Training GANs and VAEs

DAY 2: ADVANCED TOPICS AND APPLICATIONS

Module 1: Autoregressive Models and Beyond

Module 2: Conditional Generative Models and Style Transfer

Module 3: Advanced Applications of Generative AI

Hands-on Lab: Advanced Generative AI Applications

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Duration: 8 hours / 1 day

4 Lessons

This one-day course is designed for managers and decision- makers who want to gain a high-level understanding of generative artificial intelligence (AI) techniques and their applications.

Participants will learn about the fundamentals of generative AI, its potential impact on business operations, and how to leverage generative AI technologies effectively in their organizations.

By the end of the course, participants will have gained a comprehensive understanding of generative AI concepts, applications, and strategic considerations.

They will be equipped with the knowledge and insights needed to effectively assess, plan, and implement generative AI initiatives within their organizations.

Module 1: Introduction to Generative AI

Module 2: Applications of Generative AI

Module 3: Generative AI Business Strategy

Module 4: Managing Risks and Ethical Considerations

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