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Mastering AWS Certified AI Practitioner AIF-C01 - Hands On!

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Mastering AWS Certified AI Practitioner AIF-C01 - Hands On!

Includes practice test + exercises! AWS AIF-C01 Certification Prep Course. Bedrock, SageMaker, AI Fundamentals, and more

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6619 enrolled on this course

Last updated 11/2021

Sundog Education by Frank Kane

₹ 1499.0

₹ 2999.0

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30-Day Money-Back Guarantee

Lessons

Quizzes

Duration

13 Hours

Skill level

Beginner

Language

English

Certificate

Yes

Full lifetime access

Yes

Description

Get certified by Amazon
for your knowledge of
artificial intelligence and machine learning!
It's hard to imagine a certification that would carry more weight in today's era of generative AI.
The
AWS Certified AI Practitioner (AIF-C01 / AI1-C01)
exam isn't just for developers - it's aimed at a wide variety of roles in the technology space. Whether you're a PM, manager, sales or marketing professional, or developer - the concepts behind artificial intelligence, GenAI, and machine learning (ML) aren't as hard as you think. This course starts with the basics, explaining things in plain English and with simple examples.
No coding required!
We will go deep for those who want it, though.
Hands-on activities
will give you practice in building a custom chatbot using Amazon Bedrock and Knowledge Bases, building Guardrails for AI safety, and building a fully-fledged LLM agent armed with tools to extend an AI application - all just using the AWS console on the web. And demo videos walk you through training and using machine learning models using Amazon SageMaker.
You'll be fully prepared for the exam with an included 20-question
practice test
and
80 quiz questions
in the style of AWS exams. You'll also get a
PDF copy of the course slides
and a
PDF study guide
to further help you prepare for the exam.
Unlike other AWS certifications, there is a big business focus on this one. In addition to how the tech works, we'll also review best practices for the entire AI and machine learning lifecycle, the dimensions of "responsible AI," and best practices for security and governance with AI.
Some topics we'll cover include:
Fundamental concepts and terminologies of AI, ML, and Generative AI
How deep learning and large language models (LLM's) work
Evaluating and measuring AI models
Machine learning design principles
Machine learning operations (MLOps)
Use cases of AI, ML, and GenAI
Prompt engineering
Building machine learning pipelines with SageMaker
Common machine learning algorithms
Building generative AI applications with Amazon Bedrock
Retrieval Augmented Generation (RAG)
LLM Agents
Amazon Q Business, Q Developer, and Q Apps
High-level AWS AI and machine learning services
Model training and fine-tuning techniques
Responsible AI
AI Governance and Security
Although this is a course about AI, it is
not AI-generated!
You'll learn from a real human instructor who passed the real exam, with real human experience in building AI applications in a professional setting.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Instructor
Hey, I'm Frank Kane.
I have successfully passed the AIF-C01 exam myself
, and have ensured this course contains everything you need to know. I spent nine years working for Amazon from the inside as a senior engineer and senior manager, and I'm best known for my top-selling courses in "big data", data analytics, machine learning, AI, Apache Spark, system design, and Elasticsearch. I've been awarded
26 issued patents
in the field of machine learning.
I've been teaching on Udemy since 2015, where I've reached over 900,000 students all around the world!
I've worked hard to keep this course up to date with the latest developments in AWS's machine learning and artificial intelligence technologies, and to make sure you're prepared for the latest version of this exam. Let's dive in and get you ready!
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
This course also comes with:
Lifetime access to all future updates
9+ hours of video training
Course slides and study guide in PDF form
80 quiz questions to assess your readiness
A responsive instructor in the Q&A Section
Udemy Certificate of Completion Ready for Download
A 30 Day "No Questions Asked" Money Back Guarantee!
Join us in this course if you want to pass the AWS Certified AI Practitioner exam and master the world of AI and machine learning on the AWS platform!
Who this course is for:
Technologists seeking certification in artificial intelligence and machine learning technologies on Amazon Web Services
The AIF-C01 exam is aimed at a wide variety of roles, including sales and marketing, project managers, product managers, managers, and more - it's not just for developers.

What you'll learn

What to Expect on the AWS Certified AI Practitioner AIF-C01 Exam

Artificial Intelligence, Generative AI, and Machine Learning fundamentals

Machine Learning design principles

Use cases for AI, Generative AI (GenAI) and Machine Learning

Building Machine Learning systems and MLOps with SageMaker

Building Generative AI systems with Amazon Bedrock

Amazon Q Business and Q Developer

Model training and fine-tuning techniques with AWS

Evaluating and measuring your machine learning models

Responsible AI, security, and governance

Requirements

You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
No previous design experience is needed.
No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures

Expand All Sections

1-Introduction

1.1-Introduction

1.2-Udemy 101

1.3-Get Your Copy of the Slides and Study Guide

2-AI and Machine Learning Fundamentals

2.1-Taxonomy of Artificial Intelligence and Machine Learning Techniques

2.2-Supervised Learning Techniques

2.3-Evaluating Supervised Machine Learning with Train/Test and Cross Validation

2.4-Unsupervised and Self-Supervised Learning

Reinforcement Learning

2.5-The Bias / Variance Tradeoff

2.6-A Taxonomy of Machine Learning Techniques

2.7-Intro to Natural Language Processing (NLP)

2.8-History of Deep Learning

How Neural Networks Work

2.9-Training and Tuning Neural Networks

2.10-Convolutional Neural Networks (CNNs)

2.11-Recurrent Neural Networks (RNNs)

2.12-The Transformer Architecture and Self-Attention: How Generative AI Works

2.13-Transformers: Key Terms (temperature, context windows, max tokens, etc.)

2.14-Generative Adversarial Networks (GANs)

2.15-Diffusion Models

2.16-Residual Neural Networks and WaveNet

2.17-Quiz: AI and ML Fundamentals

3-Machine Learning Design Principles and Use Cases

3.1-Machine Learning Design Principles and Lifecycle

3.2-Business Goal Identification

3.3-Framing the Machine Learning Problem

3.4-Data Processing

3.5-Model Development, Training, and Tuning

3.6-Deployment

3.7-Monitoring

3.8-The AWS Well-Architected Machine Learning (ML) Lens

3.9-Machine Learning Ops (MLOps)

3.10-AI Use Cases

3.11-Computer Vision Use Cases

3.12-Generative AI Use Cases

3.13-Quiz: ML Design Principles and Use Cases

4-Prompt Engineering

4.1-Benefits of Prompt Engineering

4.2-Anatomy of a Prompt

4.3-Prompt Best Practices

4.4-Types of Prompts

4.5-Avoiding Prompt Mis-Use and Mitigating Bias

4.6-Quiz: Prompt Engineering

5-AI and Machine Learning with Amazon SageMaker

5.1-Set up an AWS Billing Alarm

5.2-Overview of Amazon SageMaker

5.3-Data Processing, Training, and Deployment with SageMaker

5.4-SageMaker Studio, SageMaker Debugger, SageMaker Experiments

5.5-SageMaker Autopilot

5.6-SageMaker Model Monitor and SageMaker Clarify

5.7-SageMaker Deployment Safeguards (and other features)

5.8-SageMaker Feature Store

5.9-SageMaker Lineage Tracking

5.10-SageMaker Data Wrangler

5.11-SageMaker Canvas

5.12-DEMO: SageMaker Studio, SageMaker Canvas, SageMaker Data Wrangler

5.13-Linear Learner, XGBoost, Seq2Seq

5.14-DeepAR, BlazingText, Obj2Vec, Object Detection

5.15-Image Classification, Semantic Segmentation, Random Cut Forest, NTM, LDA

5.16-KNN, K-Means, PCA, Factorization Machines, IP Insights

5.17-DEMO: Training and Inference with SageMaker and XGBoost

5.18-Quiz: Amazon SageMaker

6-Generative AI with AWS

6.1-Generative AI with Foundation Models and SageMaker JumpStart

6.2-Introduction to Amazon Bedrock

6.3-HANDS ON with the Bedrock Playground for Chat, Text, and Image Generation

6.4-Fine-Tuning Foundation Models with Bedrock

6.5-Bedrock Knowledge Bases and Retrieval-Augmented Generation (RAG)

6.6-HANDS ON with Bedrock Knowledge Bases

6.7-Bedrock Guardrails

6.8-HANDS ON with Bedrock Guardrails

6.9-LLM Agents and Bedrock Agents

6.10-HANDS ON with Bedrock Agents

6.11-More Bedrock Features

6.12-Bedrock Model Invocation Logging

6.13-Amazon Bedrock Pricing and Security

6.14-Amazon Q Developer (formerly CodeWhisperer)

6.15-Amazon Q Business

6.16-Amazon Q Apps and Pricing

6.17-Amazon Quicksight and Quicksight Q

6.18-Quiz: Generative AI with AWS

7-High-Level AWS AI Services

7.1-Amazon Comprehend

7.2-Amazon Translate

7.3-Amazon Transcribe

7.4-Amazon Polly

7.5-Amazon Rekognition

7.6-Amazon Forecast

7.7-Amazon Lex

7.8-Amazon Personalize

7.9-Additional AI and ML Services

7.10-Quiz: High-Level AI Services

8-Training, Tuning, and Measuring your Models

8.1-Fine-Tuning Foundation Models

8.2-Reinforcement Learning from Human Feedback (RLHF)

8.3-Preparing Data for Fine Tuning

8.4-Evaluation Techniques for Foundation Models

8.5-ROUGE, BLEU, and BERTscore metrics for LLM's

8.6-Choosing a Generative AI Evaluation Strategy

8.7-Machine Learning Model Evaluation: Precision, Recall, F1, RMSE

8.8-ROC Curves, AUC, P-R Curves

8.9-Measuring Regression Models (r-squared, RMSE, MAE)

8.10-Quiz: Training, Tuning, and Measuring your Models

9-Responsible AI, AI Governance, and AI Security

9.1-Dimensions of Responsible AI, and AWS Tools for Responsible AI

9.2-Best Practices for Responsible AI

9.3-SageMaker Clarify Metrics and Techniques

9.4-AI Governance and Service Cards

9.5-Responsible Model Selection

Responsible Agency

9.6-Responsible Dataset Preparation

9.7-Transparency of AI Models

9.8-AI and Human-Centered Design (HCD)

9.9-Defense In Depth

9.10-Additional AI Security Services

9.11-Security in Data Engineering

9.12-AWS Shared Responsibility Model

9.13-AI Compliance Concerns

9.14-Data Governance

9.15-AWS AI Governance Services

9.16-The Generative AI Security Scoping Matrix

9.17-Quiz: Responsible AI, Security, and Governance

10-Practice Exam

10.1-Practice Test (20 questions) for AWS Certified AI Practitioner AIF-C01

11-Wrapping Up

11.1-What to Expect: Exam Tips and Preparation

11.2-Overview of the Exam Guide

11.3-The New Question Types: Ordering, Matching, and Case Study

11.4-Bonus Lecture

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