JUMP AHEAD – LEAD THE PACK
You're a professional in AI/ML, data science, or a related field.
Get Professional Certifications from the industry authority!
The Intelligence Factory has created a collection of highly relavent, respected, and trusted industry certifications. Whether you earn one of the certifications or all of them, your employers and colleagues will know that the certifications underscore your experience, and also signify your mastery of the skills needed to innovate, pioneer, and lead others who work in this field.
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Machine Learning Engineer
For the Machine Learning Engineer certification, you will have three courses covering the following:
Data Engineering
In this course, students will be introduced to the various types of data commonly utilized in Artificial Intelligence and Machine Learning problems. In addition, the students will apply standard processes commonly found in data extraction, data transformation and data loading methods commonly utilized in AI applications. Students will construct a data ingestion pipeline using a data warehouse and prep data for use in a predefined Machine Learning problem.
Predicting Customer Buying Behavior (Supervised Machine Learning)
In this course, students will be introduced to Machine Learning and basic Classification and Regression concepts using the Python programming language and various open source visualization tools. Students will predict the probability of customer retention and predict the sales capacity of various chain stores in a given location throughout one year. Students will also use learn to deploy their predictive models in a basic, predefined Machine Learning pipeline for making predictions using real-time data.
Detecting Machine Failure
In this course, students use Python to apply unsupervised Machine Learning algorithms to unlabeled data to ascertain if anomalies are present in real-time streaming data being generated by a piece of industrial machinery. Students also query and analyze a Neo4J Graph Database to ascertain hidden relationships among a large set of chatbot data to better understand how chatbots and Natural Language Processing are used in AI.
Deep Learning Engineer
For the Deep Learning Engineer certification, you will have three courses covering the following:
Getting Ready for Deep Learning: Deep Learning Concepts & Environment
In this course, students will be introduced to building a deep learning environment in order to establish a cloud based structure for deployment and to automate the setup process for streamlined implementation. In addition, students will work on their first deep learning project involving neural networks to classify customer handwriting in order to predict missing digits which will incorporate utilizing open source datasets, as well as creating and testing a model and building a training loop.
Developing Communicative Systems and Chatbots: Deep Learning Language
In this course, students will be introduced to computational linguistics utilizing common word representation models word2vec, developing a chatbot utilizing a Natural Language Pipeline (NLP), and understanding DeepSpeech2 for building a speech recognition system. All projects within this course will align computation linguistics principles to address specific customer needs in the retail sector.
Training Your Computer to See: Deep Learning Vision
In this course, students will be introduced to effective object recognition solutions utilizing Open CV and TensorFlow and automated image captioning. By the end of this course, students will be able to train computer models to observe patterns in images and apply this pattern recognition to real-world problems.
Autonomous Systems Engineer
For the Autonomous Systems Engineer certification, you will have three courses covering the following:
Pattern Recognition & Probabilistic Programming
In this course, students will be introduced to Probabilistic Graph Models and Probabilistic Programming while learning about and implementing Bayesian estimation of dynamical systems. In addition, the student will apply machine learning for pattern recognition, prediction, and sequential updating of knowledge captured by the programs.
Working with Randomness in Machine Learning
In this course, students will be introduced to the key elements of machine learning, namely: Credit Assignment Problems and Regularization. Assigning credit, or blame, is to calculate how model parameters should be estimated, or updated, to fit the training data. Regularization smooths and tempers these updates to avoiding overfitting the training data and promote generalization when presented with new data. Of course, the latter is the whole point. Stochastic processes and optimization come into play much like how a smith tempers steel. The student will learn how and why machine learning is essentially stochastic optimization where randomness can work with, rather than against, us. In so doing, the student will learn to apply probabilistic programming and stochastic optimization methods to latent, state-based model estimation. The student will also learn to develop learning algorithms for sequence data and apply the generated programs to applications that involve time series predictions and sequential decision making.
Continuous Learning in Real-Time
In this course, students will learn how to use probabilistic programming to build models and supporting algorithms that learn sequentially under uncertainty. The goal is to learn a Markov decision process that informs the program on how to optimally interact with its real-world environment in real-time. The student will learn how this underlying stochastic process is constructed with latent-variable graph models that enjoy exact or approximate numerical solutions. The type of solution depends on certain properties a given graph possesses. The student will learn how to use a high-level language for probabilistic programming that emits coded graph model descriptions accompanied with the appropriate learning and inference algorithm. We call this innovation the “model of computation” and executing the generated program performs the exact/approximate marginalization to compute answers to statistical questions. Students will learn how to apply these tools and machinery to engineer AI systems and becomes the basis for online learning and operations with streaming data. The student will take away an understanding of how such systems learn to perform sequential tasks, improve incrementally, and interact with the world online in real-time. Moreover, this formal, model-based framework introduced in this course has application to machine learning engineering that is principled, transparent, and hence, verifiable for safety-critical systems.