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 2: Contextual Knowledge
Machine Learning Part I
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 first part of the program, the second part is in Machine Learning Part II. If you are interested in additional resources you can explore the Ramp-Up Guide: Machine Learning.
This is the first part of the program, the second part is in Machine Learning Part II. If you are interested in additional resources you can explore the Ramp-Up Guide: Machine Learning.
Courses in this program
1) Demystifying AI/ML/DL
After taking this set of courses, you’ll understand how artificial intelligence (AI) led to machine learning (ML), which then led to deep learning (DL).
Estimated effort 1 hour
English language
2) Machine Learning Essentials for Business and Technical Decision Makers
In this three-course curriculum, you will learn about best practices and recommendations for machine learning (ML). The course explores how to roadmap for integrating ML into your business processes, explores requirements to determine if ML is the appropriate solution to a business problem, and describes what components are needed for a successful organizational adoption of ML.
In this curriculum, you will learn to:
In this curriculum, you will learn to:
- Understand the basics of machine learning to help evaluate the benefits and risks associated with adopting ML in various business cases.
- Identify the data, time, and production requirements for a successful ML project.
- Describe how to adapt an organization to achieve and sustain success using ML.
3) Machine Learning for Business Challenges
Machine learning (ML) can help you solve business problems in ways that weren't possible before—but you've got to think big. Listen in as some of Amazon's own Machine Learning Scientists discuss how to make the most of ML. We'll cover ML terminology, business problems, use cases, and examples. By the end of this course, you'll have a better understanding of how to think about machine learning business challenges and decisions.
4) ML Building Blocks: Services and Terminology
These two courses clarify both the machine learning stack and the terms and processes that will help you build a good foundation in machine learning. You’ll explore the AWS ML stack through application use cases, platform services, frameworks, interfaces, and infrastructure. You’ll also learn how a business problem becomes a machine learning problem, and how data is moved and processed throughout the pipeline to train models and create predictions.
5) Process Model: CRISP-DM on the AWS Stack
The CRISP-DM model frames data science as a cyclical endeavor. Here, we'll walk through the CRISP-DM methodology and framework and then apply the model's six phases to your daily work as a data scientist with Jake Chen, a Data Science consultant with AWS.
6) Machine Learning Terminology and Process
This course introduces you to basic machine learning concepts and the machine process the data goes through. We explore each step in the machine learning process in detail and explain some of the common terms and techniques that occur during a phase of a ML project.
This course teaches you how to:
This course teaches you how to:
- Discuss the machine learning process.
- Highlight the basic ML terms used in a ML project.
- Describe each ML step in detail.
- Describe common techniques used in each step of a ML process.
7) Exploring the Machine Learning Toolset
No matter what your background or experience, you can use machine learning. In this course, we’ll show you some of the AWS machine learning services you can use to build models and add intelligence to applications.
8) The Elements of Data Science
Learn to build and continuously improve machine learning models with Data Scientist Harsha Viswanath, who will cover problem formulation, exploratory data analysis, feature engineering, model training, tuning and debugging, as well as model evaluation and productionizing.