Welcome
IPN-Dharma AI Lab
This is an IPN CIC - DHARMA initiative to provide an Artificial Intelligence Laboratory to motivate researchers, professors and students to take advantage of the courses, resources and tools of the main technology platforms of the industry in the areas of Machine Learning, Data Science, Cloud Computing, Artificial Intelligence and Internet of Things with the purpose of generating a practical experience through a learning model between peers and by objectives.
Level 3: Building Solutions
Machine Learning Part II
This program is designed to help Data Scientists and Developers integrate machine learning (ML) and artificial intelligence (AI) into tools and applications. The digital training included in this program will expose you to the broadest and deepest set of machine learning services and supporting cloud infrastructure. This program can also help prepare you for the AWS Certified Machine Learning - Specialty certification exam.
This is the second part of the program, the first part is in Machine Learning Part I. If you are interested in additional resources you can explore the Ramp-Up Guide: Machine Learning.
This is the second part of the program, the first part is in Machine Learning Part I. If you are interested in additional resources you can explore the Ramp-Up Guide: Machine Learning.
Courses in this program
9) Machine Learning Security
At AWS, security is the highest priority. Controlling and managing permissions, as well as authorizing traffic is all part of building highly secure applications and environments on the AWS platform. This curriculum covers the AWS products and services that enable you to secure your applications and environments with specific topics detailing NACLs, security groups, AWS identity and access management, and encryption key management.
Estimated effort 2 hours
English language
10) Math for Machine Learning
To understand modern machine learning, you also need to understand vectors and matrices, linear algebra, probability theorems, univariate calculus, and multivariate calculus. This course, led by AWS Machine Learning Instructor Brent Werness, covers it all.
11) Developing Machine Learning Applications
In this curriculum, we’ll explore Amazon’s fully managed ML platform, Amazon SageMaker. Specifically, we’ll discuss how to train and tune models, how certain algorithms are built in, how you can bring your own algorithm, and how to build for particular use cases like recommender systems or anomaly detection.
12) Amazon SageMaker: Build an Object Detection Model Using Images Labeled with Ground Truth
In this course we’ll join Dr. Denis Batalov, worldwide AI/ML Tech Leader, as he shows you how to implement a machine learning pipeline using Amazon SageMaker and Amazon SageMaker Ground Truth. First you will create a labeled dataset, then you’ll create a training job to train your object detection model, and finally you will use Amazon SageMaker to create and update your model.
In this course, you will learn how to:
In this course, you will learn how to:
- Train a machine learning model using images labeled by Amazon SageMaker Ground Truth.
- Use Amazon SageMaker Ground Truth to identify the exact location of bees on individual images in a dataset.
- Train the object detection model using Amazon SageMaker in-built algorithms.
- Use an automated hyperparameter tuning job to find an optimal set of hyperparameters.
13) Data Science Capstone: Real World ML Decisions
Use machine learning to solve a real-life business challenge like one that the Amazon Studios team faced in the past. You’ll build, train, and test a machine learning model from the ground up—cleaning data, conducting feature engineering, comparing algorithms—and in the process, you’ll get a firsthand look at how Amazon employees who work with machine learning approach ML pipelines.
14) Exam Readiness: AWS Certified Machine Learning - Specialty
This course prepares you to take the AWS Certified Machine Learning – Specialty exam, which validates your ability to design, implement, deploy, and maintain machine learning (ML) solutions.
In this course, you’ll learn about the logistics of the exam and the mechanics of exam questions, and you’ll explore the exam’s technical domains. You’ll review core AWS services and key concepts for the exam domains:
By the end of this course, you will be able to:
In this course, you’ll learn about the logistics of the exam and the mechanics of exam questions, and you’ll explore the exam’s technical domains. You’ll review core AWS services and key concepts for the exam domains:
- Data Engineering
- Exploratory Data Analysis
- Modeling
- Machine Learning Implementation and Operations
By the end of this course, you will be able to:
- Identify your strengths and weaknesses in each exam domain so that you know what to focus on when studying for the exam.
- Describe the technical topics and concepts that make up each of the exam domains.
- Summarize the logistics and mechanics of the exam and its questions.
- Use effective strategies for studying and taking the exam.