Earn Your Machine Learning Engineering and AI Certification

• Foundations

  • Program Overview

  • Laying the Foundations

  • Introduction to Python I

  • Data Visualization Detour

  • Introduction to Python II

  • Intermediate Python I

  • Intermediate Python II

  • Statistics I

  • Statistics II

• Core Curriculum 

  • Overview

  • Introduction to Machine Learning

  • Ethics and Bias

  • Creating Your Career Management Strategy (Optional)

  • Data Wrangling and Exploration

  • Introduction to SQL

  • Your Elevator Pitch and LinkedIn Profile (Optional)

  • Machine Learning with Scikit Learn

  • Model Evaluation

  • Effective Networking: Expanding Your Network (Optional)

  • Deep Learning

  • Resumes and Cover Letters (Optional)

  • Optimization

  • Informational Interviews (Optional)

  • Computer Vision

  • Natural Language Processing

  • Revisit Career Strategies Based On Your Goals (Optional)

  • Recommender Systems

  • Model Deployment

  • Preparing for and Getting Interviews (Optional)

  • Amazon Web Services (AWS) I

  • Amazon Web Services (AWS) II

  • Monitoring and Maintenance

  • Effective Interviewing for Machine Learning Engineers (Optional)

  • Salary Negotiation (Optional)

  • Congratulations!

We’ll teach you the most in-demand machine learning models and algorithms you’ll need to know to succeed as an MLE. For each model, you will learn how it works conceptually first, then the applied mathematics necessary to implement it, and finally you will get experience training and testing the models. We’ll walk you through the best practices for predictive optimization, like hyperparameter tuning, and how to evaluate your performance. You’ll learn how to pick the right model for the challenge you are facing, and critically, how to implement and deploy these models at scale.

1. Algorithms for both supervised and unsupervised learning

2. Gauging model performance using a variety of cross-validation metrics

3. Using AutoML to generate baseline models

4. Model selection and hyperparameter tuning

5. Bias in models and model drift

6. Deep learning techniques like convolutional, and recurrent neural networks, and generative adversarial networks

7. Recommendation systems

8. Tools: Scikit-Learn, Tensorflow, Pandas, AutoML systems, AWS

Throughout this course, you’ll be introduced to a variety of tools and libraries that are used in both data science and machine learning. These include everything from ML libraries to deployment tools. There will also be refreshers on software engineering best practices and foundational math concepts that every ML Engineer should know.

1. Python Data Science Tools include Pandas, Scikit-learn, Keras, TensorFlow, SQL

2. Machine learning engineering tools including TensorFlow, Flask, AWS, Docker, Kubernetes, FastAPI

3. Software engineering tools including continuous integration, version control with Git, logging, testing, and debugging

4. Working With data pipelines

A critical part of every machine learning engineer’s job is collecting, cleaning, processing, and transforming data. Without quality data, you can’t get quality insights. You’ll learn the best practices and tools for working with data at scale and how to transform a messy, sparse dataset into something worthy of modeling.

1. Exploratory data analysis

2. Cleaning and transforming data for ML systems at scale

3. Working with large data sets in SQL

Machine learning at scale and in production is an entirely different beast than training a model in Jupyter notebook. When you’re working at scale, there are a host of problems that can disrupt your model and its performance. We’ll teach you about the best practices for surmounting these challenges, how to write production-level code, as well as ensuring that you are getting quality data fed into your model.

1. Creating reliable and reproducible data pipelines to ensure your model is well fueled

2. Cloud-based services provided by AWS

3. The machine learning life cycle and challenges that can occur when integrating your model into an application

4. REST APIs, serverless computing, microservices, containerization

Deep learning is a subset of machine learning that uses neural networks with multiple layers to learn and extract complex patterns and representations from data. This advanced machine-learning technique powers many of today’s most cutting edge applications, including generating photorealistic faces of people who have never lived, machine translation, self-driving cars, speech recognition, and more. Deep learning models become more accurate when they are fed more data, so they are excellent for many business problems.

1. Overview of neural networks, backpropagation, and foundational optimization techniques like gradient descent

2. Neural network architectures

3. Transfer learning

4. Training neural networks using Keras and TensorFlow

5. Computer vision including convolutional neural networks, image segmentation, object detection, and generative adversarial networks

6. Natural language processing including large language models, sentiment analysis, and named entity recognition

Ethics and bias in machine learning refer to the principles, guidelines, and considerations surrounding the responsible and fair use of machine learning algorithms and models, ensuring that their deployment and outcomes uphold human values, avoid bias and discrimination, protect privacy, and prioritize transparency and accountability.

1. Algorithmic bias and fairness

2. Privacy concerns in ML

3. Model transparency and interpretability

4. Ethical considerations in ML research and deployment

5. Best practices for responsible AI development and deployment

Machine learning is an innovative field that combines software engineering, data science, and cognitive technologies to build intelligent systems that can “learn” and improve their own performance by working effectively with data.

Machine learning engineers work toward AI solutions by leveraging data sets and creating algorithms so that systems can learn from data and make predictions.

AI (Artificial Intelligence) and machine learning are related concepts within the field of computer science, but they are not the same thing. AI refers to the broader discipline of creating intelligent machines that can mimic human cognitive processes and perform tasks that typically require human intelligence. It encompasses various techniques and approaches to achieve this goal.

On the other hand, machine learning is a specific subset or technique within AI. It focuses on developing algorithms and statistical models that enable computers to learn and make predictions or decisions without being explicitly programmed for every specific task. In other words, machine learning is a method by which AI systems can acquire knowledge and improve their performance based on data inputs.

You must already have a programming, software engineering, or data science background and be willing to take the leap to master many software libraries, which often are built to train particular models.

In addition, you’ll need to know how to train models on large clusters, work with hardware components optimally, and batch ETL pipelines.

Besides the technical skills and proficiencies, a good machine learning engineer learns how to collaborate with members of a larger team—data scientists, data engineers, researchers, software engineers, and business stakeholders—to implement solutions. They must also hold strong ethics to ensure the AI solutions they work toward are for the greater good.

• Data Scientist

• NLP Scientist

• Business Intelligence Developer

• Human-Centered Machine Learning Designer

• Research Scientists/Applied Research Scientists

• Distributed Systems Engineer

Machine learning is a disciplined field that requires strong software engineering skills. To master the skills, you’ll need dedication, curiosity, and a drive to make the world a better place through AI.

According to Lightcast, machine learning engineers in Maryland with 0 to 3 years experience have a median advertised salary of $153,088.

Depending on your work history, background, job title, location, and what type of sector you work in, the salary can differ, especially as you reach a higher seniority level.

Source: Lightcast; Oct 2022 - Sep 2023; 0-3 years minimum experience required.

Yes, according to Market Research Future, Machine Learning Market share is expected to reach 106.52 Billion by 2030, with a CAGR of 38.76% during the forecast period 2020-2030.  Experts predict that there is an increasing skills gap between businesses that need to deploy AI products and the technical professionals with the proficiencies to do so.

A Machine Learning Engineer in Maryland  may earn around $127,492 per year, but the range typically falls between $114,312 per year and $142,079 per year according to Salary.com.