Machine Learning and AI for Software Developers

Machine learning (ML) expands the boundaries of what’s possible by letting software perform tasks that can’t be accomplished algorithmically. From fraud detection and sentiment analysis to spam filtering and facial recognition, it touches lives every day. Deep Learning is a subset of Machine Learning that relies on deep neural networks. It is how computers identify objects in images, translate speech in real time, generate artwork and music, and perform other tasks that would have been impossible just a few short years ago.

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Together, these technologies comprise what is popularly known as Artificial Intelligence, or AI. Learn the basics of Machine Learning and Deep Learning, and discover how they can be used to solve business problems and write software that is smarter than ever before. And do your learning through a combination of lectures and hands-on exercises designed to skill you up quickly on the hottest technologies in software development.

Machine Learning
Learn what machine learning is, what types of machine-learning models there are, and how to use Scikit-learn to build simple unsupervised- and supervised-learning models using algorithms such as k-means clustering and k-nearest neighbours.
Regression Models
Learn how to build supervised-learning models that predict numeric values such as the age of a person in a photo or how much a house might sell for. Also learn how to score regression models for accuracy, how to handle categorical values in datasets, and how to use popular learning algorithms such as linear regression, random forests, and gradient-boosting machines (GBMs) in regression models.
Classification Models
Learn how to build classification models that predict categorical outcomes such as whether a credit-card transaction is fraudulent or what number a hand-written digit represents. Also learn about popular classification learning algorithms such as logistic regression, and get acquainted with precision, recall, sensitivity, specificity, confusion matrices, and other metrics used to score classification models.
Text Classification
Learn how to build machine-learning models that classify textual data – for example, models that predict whether an e-mail is “spam” or “not spam” and models that analyze text for sentiment. Also learn about the Naïve Bayes learning algorithm and how to use textual descriptions of products and services to build intelligent recommender systems.
Support-Vector Machines
Support-vector machines (SVMs) represent the cutting edge of statistical machine learning. They often succeed at finding separation between classes when others do not. Take a deep dive into SVMs, learning what they are and how they work. Also learn about hyperparameter tuning and data normalization, and then put your skills to work building an SVM facial-recognition model.
Principal Component Analysis
Principal component analysis, or PCA, is one of the minor miracles of machine learning. It’s a dimensionality-reduction technique that reduces the number of dimensions in a dataset without sacrificing a commensurate amount of information. You can use it to visualize high-dimensional data, obfuscate data, remove noise, and much more. Learn what PCA is, how it works, and how to apply it, and then put it to work building an anomaly-detection model that predicts failures in bearings.
Operationalizing Machine-Learning Models
Machine-learning models built in Python are easily consumed in Python apps, but consuming them in other languages such as C# and Java is not so straightforward. Learn about ONNX (Open Neural Network Exchange) and other ways to operationalize ML models so they can be consumed by any application, regardless of platform or programming language. Also see an alternative way to build ML models in C# using ML.NET, and novel way to add machine-learning capabilities to Microsoft Excel.
Deep Learning
Deep learning is a subset of machine learning that relies primarily on deep neural networks. Learn what neural networks are, how they work, and why they are continually advancing the state of the art in AI.
Neural Networks
Learn how to use Keras and TensorFlow to build and train sophisticated neural networks that perform regression and classification. Also learn how to save and load trained models, how to use dropout layers to combat overfitting, and how to use Keras’s callbacks API to customize the training process.
Image Classification with Convolutional Neural Networks
State-of-the-art image classification typically isn’t done with traditional neural networks. Rather, it is performed with Convolutional Neural Networks (CNNs), which excel at computer-vision tasks such as identifying objects in images. Learn what CNNs are and how they work, and learn how to use transfer learning to build sophisticated CNNs to solve domain-specific problems.
Facial Recognition and Object Detection
Convolutional neural networks can do more than just classify images; they play an important role in modern facial-recognition and object-detection systems, too. Learn how self-driving cars use advanced CNNs to recognize objects around them, and how to build an end-to-end facial recognition system by combining CNNs with popular face-detection algorithms.
Natural Language Processing
Deep neural networks have grown in sophistication to the point that they can process text and speech – sometimes more accurately than humans. Learn how to use Keras and TensorFlow to build and train neural networks that classify text, translate text, and perform other feats of NLP magic.

Sample Video

If you'd like to see Jeff Prosise in action, you can view the full video of the following session at our main conference held in May 2022.

(1 hour 15 mins)

Day 1

  • Machine Learning
  • Regression Models
  • Classification Models
  • Text Classification

Day 2

  • Support-Vector Machines
  • Principal Component Analysis
  • Operationalizing Machine-Learning Models
  • Deep Learning

Day 3

  • Neural Networks
  • Image Classification with Convolutional Neural Networks
  • Facial Recognition and Object Detection
  • Natural Language Processing

Jeff Prosise

Jeff Prosise

Jeff is a co-founder of Wintellect, who makes his living writing software and helping others do the same. He has written nine books and hundreds of magazine articles, trained thousands of developers at Microsoft, and spoken at some of the world’s largest software conferences. Jeff’s passion is teaching software developers how to build cloud-based apps with Microsoft Azure and introducing them to the wonders of AI and Machine Learning. Following the merger of Wintellect and Atmosera, Jeff serves as Chief Learning Officer for the combined companies. In his spare time, he builds and flies large radio-controlled jets and travels to development shops, universities, and research institutions around the world educating them about Machine Learning and AI.

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Interested in registering for this workshop?
Click here for details of the current price, which includes a substantial early-bird discount.