Advanced Deployment Scenarios with TensorFlow

AI-ML colleagues, this Advanced Deployment Scenario with TensorFlow Specialization builds upon our TensorFlow in Practice Specialization. Build high-demand.skill in: TensorFlow Serving, Machine Learning and Federated Learning, TensorFlow Hub and TensorBoard. Skill-based training modules address: 1 - TensorFlow Extended (Serving, Installing TF Serving1m, TensorFlow Serving summary, Setup for serving2m, Serving, Predictions, Passing data to serving, Getting the predictions back, Running the colab and Complex model), 2 - Sharing pre-trained models with TensorFlow Hub (Introduction to TF Hub, Transfer learning, Inference1m, Module storage, Text based model, Word embeddings, Experimenting with embeddings, Colab1m, Classify cats and dogs and Transfer learning), 3 - Tensorboard: tools for model training (Tensorboard scalars, Callbacks, Histograms, Publishing model details, Local tensorboard, Looking at graphics in a dataset, More than one image, Confusion matrix and Multiple callbacks, and  4 - Federated Learning (Training on mobile devices, Data at the edge, How it works, Maintaining user privacy, Masking, APIs for Federated Learning, Example of federated learning and Outro).

Enroll today (individuals & teams welcome): https://tinyurl.com/w9bzc2ks 

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

Using Machine Learning in Trading and Finance

Colleagues, the Using Machine Learning in Trading and Finance program provides the foundation for developing advanced trading strategies using machine learning techniques. You will review the key components that are common to every trading strategy, no matter how complex and be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading. By the end of the course, you will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it. Develop high-demand skills in Algorithmic Trading, Python Programming and Machine Learning. You should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).

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

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

Data Management with Python and SQL (Micro Bachelor’s Degree)

Colleagues, the Data Management with Python and SQL program build the foundational skills they need to succeed in data management using the Python programming language and SQL (structured query language). Module 1: Scripting with Python (8–10 hours per week, for 16 weeks) Explore fundamental programming with hands-on activities that help you build applications using Python.The Python programming language is extremely powerful and commonly used to automate time-intensive activities/tasks for users. This makes Python a good skill to have for any job that requires automation to replace data in a file, rename multiple file names, update Excel spreadsheets or mine data from web pages. Python can be used as a stepping stone to enter some of the most exciting industries including data science, artificial intelligence, machine learning, software or full-stack development. Module 2 Structured Database Environments with SQL (8–10 hours per week, for 16 weeks) Discover Structured Query Language (SQL) programming basics in relation to database management and data manipulation. This course can provide you with an overview of topics like joins, database schemas, database design and importing data into a database.

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

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

Self-Driving Cars with Duckietown (Training)

AI-ML colleagues, The first robotics and AI MOOC with scale-model self-driving cars. Learn state-of-the-art autonomy with your own real robot (Duckiebot). Self-driving cars with Duckietown is a practical introduction to vehicle autonomy. It explores real-world solutions to the theoretical challenges of automation, including their implementation in algorithms and their deployment in simulation as well as on hardware. Using modern software architectures built with Python, Robot Operating System (ROS) and Docker, you will appreciate the complementary strengths of classical architectures and modern machine learning-based approaches. The scope of this introductory course is to go from zero to having a self-driving car safely driving on a road. This course is presented by Professors and Scientists who are passionate about robotics and accessible education. It uses the Duckietown robotic ecosystem, an open-source platform created at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and now used in over 80 universities worldwide. After this course you will be able to program your Duckiebots to navigate (without accidents!) in road lanes of a model city with rubber-duckies-pedestrians-obstacles using predominantly computer vision based techniques. 

Enroll today (individuals & teams welcome): https://fxo.co/BVR9  

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

Data Science - Professional Certificate (Harvard University)

DS colleagues, the HarvardX Data Science certification program prepares you with the necessary knowledge base and useful skills to tackle real-world data analysis challenges. The program covers concepts such as probability, inference, regression, and machine learning and helps you develop an essential skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with Unix/Linux, version control with git and GitHub, and reproducible document preparation with RStudio. Learn the fundamental R programming skills, statistical concepts (such as probability, inference, and modeling and how to apply them in practice), gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr, essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio, implement machine learning algorithms, and grasp fundamental data science concepts through motivating real-world case studies.  Skill-based training modules include: 1) R Basics, 2) Visualization, 3) Probability, 4) Inference and Modeling, 5) Productivity Tools, 6) Data Wrangling, 7) Linear Regression, 8) Machine Learning, and 9) Capstone Project. 

Enroll today (individuals & teams welcome): https://fxo.co/BVR7 

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

Advanced R Programming for Productivity & Machine Learning

Dev colleagues, this Advanced R Programming course begins with reading XML data and some common data manipulation operations using various base R functions and packages like plyr, comparing the speed of in memory calculations. He then demonstrates more advanced techniques for accomplishing the same task such as data.table, dplyr, Rcpp and parallel computation for increased speed. You will be equipped in Basic Aggregation, plyr, dplyr, data.table, Rcpp, Parallel processing, Web Graphics, Network Analysis, Text Mining and Advanced Document Creation. The seven core skill-based training modules include: 1) Reading XML Data, 2) Faster Group Operations, 4) Rcpp for Faster Code, 5) Advanced Machine Learning, 6) Network Analysis, 7) Web Graphics, and 8) Easier Presentations and Documents with RMarkdown. RStudio has made great advancements in creating documents and presentations, making the whole process easier than it was even just a few months ago. This lesson discusses the very easy steps to generate HTML, PDF and Word documents and HTML presentations.

Download your complimentary AI-ML Certification Guide (2021): https://tinyurl.com/3y5seaxe 

Enroll today (individuals & teams welcome): https://tinyurl.com/e8f32s5c 

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

Deep Learning with TensorFlow: Applications of Deep Neural Networks to Machine Learning Tasks

AI-ML colleagues this Deep Learning with TensorFlow program equips you to understand the Deep Learning machine-learning approach to life with interactive demos from the most popular Deep Learning library, TensorFlow, and its high-level API, Keras. Essential theory is whiteboarded to provide an intuitive understanding of Deep Learning’s underlying foundations, i.e., artificial neural networks. Paired with tips for overcoming common pitfalls and hands-on code run-throughs provided in Python-based Jupyter notebooks, this foundational knowledge empowers individuals with no previous understanding of neural networks to build powerful state-of-the-art Deep Learning models. The five skill-based training modules include: 1) Introduction to Deep Learning, 2) How Deep Learning Works, 3) Convolutional Networks, 4) TensorFlow Introduction, and 5) Improving Deep Networks.

Download your complimentary AI-ML Certification Guide (2021):

Enroll today (individuals & teams welcome): https://tinyurl.com/5dcwvz9x 

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

Feedforward Neural Networks (Training)

AI-ML colleagues, the Feedforward Neural Networks program will take you from the most basic concepts in neural networks to building and optimizing a complete neural network and using different tools to solve problems using Deep Neural Networks. Gain key skills in: Deep neural network, Activation Function and Types of Nonlinearities, Sigmoid Neuron Implementation, Forward Propagation Implementation, Parameters and Hyperparameters, and Neural Network using Keras. The ten skill-based training modules address: 1) Introduction and Overview, 2) Motivation behind Deep Learning, 3) A Simple Network, 4) Feed Forward Neural Network, 5) Backpropagation, 6) TensorFlow, 7) Improving the Neural Network, 8) Optimization, 9) Applications, and 10)  Summary and Conclusion. 

Download your complimentary AI-ML Certification Guide for 2021 here: https://tinyurl.com/rcdjsjnx 

Enroll today (individuals & teams welcome):https://tinyurl.com/5bnd8dj4 

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

Natural Language Processing with TensorFlow 2.0 Specialization

AI colleagues, the Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. Gain high-demand/highly marketable skills in Natural Language Processing, Tokenization, Machine Learning, Tensorflow and RNNs. Training modules include: 1) Sentiment in Text,, 2) Word Embeddings, 3) Sequence Models, and 4) Sequence models and Literature:  Taking everything that you've learned in training a neural network based on NLP, we thought it might be a bit of fun to turn the tables away from classification and use your knowledge for prediction. You will build a poetry generator trained with the lyrics from traditional Irish songs, and can be used to produce beautiful-sounding verse of its own!

Enroll today (individuals & teams): https://tinyurl.com/ylpafc6g 

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

Probabilistic Deep Learning with TensorFlow

DL colleagues, learn to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library.. Gain high-demand skills in Probabilistic Neural Networks, Deep Learning, Generative Models, Tensorflow and Probabilistic Programming Language (PRPL). The five training modules equip you in: 1) TensorFlow Distributions: Probabilistic modelling is a powerful and principled approach that provides a framework in which to take account of uncertainty in the data, 2) Probabilistic layers and Bayesian Neural Networks: Accounting for sources of uncertainty is an important aspect of the modelling process, especially for safety-critical applications such as medical diagnoses, 3) Bijectors and Normalising Flows: Normalising flows are a powerful class of generative models, that aim to model the underlying data distribution by transforming a simple base distribution through a series of bijective transformations., 4) Variational Autoencoders: Variational autoencoders are one of the most popular types of likelihood-based generative deep learning models. Two networks are jointly learned: an encoder or inference network, as well as a decoder or generative network, and 5) Capstone Project: Develop probabilistic deep learning models using tools and concepts from the TensorFlow Probability library such as Distribution objects, probabilistic layers, bijectors, and KL divergence optimisation. 

Download your complimentary AI Certification Guide for 2021 here: https://tinyurl.com/1l2soeh0 

Enroll today (individuals & teams): https://tinyurl.com/hjd51uc4 

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