Machine Learning with TensorFlow on Google Cloud Platform

ML & Cloud colleagues, join over 75k professionals enrolled in Machine Learning with TensorFlow on Google Cloud Platform. Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform.Gain high-demand skills in Tensorflow, Machine Learning, Feature Engineering, Cloud Computing, APIs,, Inclusive ML, Google Cloud Platform, Bigquery, Data Cleansing, Python Programming and Data Pipelines. Training modules include: 1)  How Google does Machine Learning, 2)  Launching into Machine Learning, 3) Introduction to TensorFlow, 4) Feature Engineering, and 5) Art and Science of Machine Learning

Enroll today (individuals & teams): https://tinyurl.com/y4kvqe9g 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (AIA)

Data Science: Data-Driven Decision Making

Colleagues, discover storytelling with data and make better business decisions using data wrangling, modelling, and visualisations in R in this Data Science: Data-Driven Decision Making program. Gain high-demand skills in Data wrangling, R programming, Developing data analysis workflows, Work with and visualise spatial and temporal data, Developing statistical models, Harvesting data, Tidying data, Data collection methods, Data visualisation and Data analysis. The three skill-based training modules include: 1) Wrangling and Workflow: Get an introduction to data science and learn how to make the most of your data through effective data wrangling and storytelling (4 weeks - 12 hours per week), 2) Modelling and Visualisation: Explore the role of data visualisation in data science, and learn how to use and apply data modelling techniques (4 weeks - 12 hours per week), and 3) Formats, Ethics, and Storytelling: Evaluate the challenges of data variety, ethics, and privacy on this course in Monash University's data science microcredential (4 weeks - 12 hours per week).

Enroll today (individuals & teams): https://fxo.co/AMzY 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (AIA)

Machine Learning Engineer for Microsoft Azure ($147k salary)

ML & Cloud colleagues, strengthen your machine learning skills and build practical experience by training, validating, and evaluating models with this Machine Learning Engineer for Microsoft Azure nanodegree program. According to Indeed the average MLE for MS Azure earns over $146k USD per year. The training modules - each with their own project - are the core of this program: 1)  Using Azure Machine Learning: Learn how to configure machine learning pipelines in Azure, identify use cases for Automated Machine Learning, and use the Azure ML SDK to design, create, and manage machine learning pipelines in Azure (Project: Optimizing an ML Pipeline in Azure), 2) Machine Learning Operations: Operationalizing machine learning, from selecting the appropriate targets for deploying models, to enabling Application Insights, identifying problems in logs, and harnessing the power of Azure’s Pipelines (Project: Operationalizing Machine Learning). and 3) Capstone Project: The program capstone gives you the opportunity to use the knowledge you have obtained from this Nanodegree program to solve an interesting problem. You will have to use Azure’s Automated ML and HyperDrive to solve a task (Project: Training and Deploying an ML Model in Azure). This program comes with an Experienced Project Reviewers, Technical Mentor Support and Personal Career Coach.

Register today (individuals & teams): https://fxo.co/AGNS 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (AIA)

Unsupervised Learning: Clustering

AI, ML colleagues, this  program will focus on Unsupervised Learning Clustering algorithms and methods for Machine Learning through practical examples and code. The course has code and sample data for you to run and learn from. It also encourages you to explore your own datasets using Clustering algorithms. Students who are interested in a practical introduction to clustering, a kind of unsupervised machine learning. Want an intuitive understanding of the theory behind clustering. Students can use these methods and algorithms for hot applications such as marketing analytics, customer segmentation, anomaly detection, fraud detection, and other practical applications in their respective fields. Skill-based training modules address: 1) K-Means Clustering, 2) Gaussian Mixture Models (GMMs), 3) Hierarchical Clustering, 4) Methods for Selecting Number of Clusters, 5) Evaluating the Quality of the Clustering, 6) Industry Applications, 7) Mini-Project: Pulling It All Together, and 9) Mini-Project Solution Preview, and 10) Solution Preview.

Enroll today (individuals & teams):https://tinyurl.com/rm6rx5v  

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (AIA)

Deep Learning with TensorFlow 2.0 (Certification & Training)

ML/DL colleagues, this Deep Learning with TensorFlow 2.0 Certification Training is curated by experienced industry professionals. This course will help you master popular algorithms like CNN, RCNN, RNN, LSTM, RBM using the latest TensorFlow 2.0 package in Python. You will work on real-time projects like Emotion and Gender Detection, Auto Image Captioning using CNN and LSTM, and many more. Skill-based training modules address: 1) Introduction to Deep Learning, 2) Getting Started with TensorFlow 2.0, 3) Convolution Neural Networks, 4) Regional CNN, 5) Boltzmann Machine & Autoencoder, 6) Generative Adversarial Networks (GANs), 7) Emotion and Gender Detection, 8) Introduction RNN and GRU, 9) Long Short-term Memory (LSTM), and 10) Auto Image Captioning Using CNN LSTM.

Enroll today (individuals & teams): https://tinyurl.com/y6d2t8hd  

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (AIA)