Artificial Intelligence-Machine Learning-Deep Learning - Career Transformation Guide (2022)

Colleagues, this Artificial Intelligence-Machine Learning-Deep Learning - Career Transformation Guide includes valuable information that enables you to accelerate your career growth and income potential - Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Community and Networking resources. Glassdoor estimates the average salary for a Machine Learning Engineer at $131,001 USD. Indeed lists 2091  openings with an averMachine Learning Engineer age nationwide salary of $131,276 USD. The San Francisco Bay Area is the high-end of the salary range at $193,485 with Eden Prairie, Minnesota at $106,780. The three critical success factors in our career transformation model. First, Get Certified. Professionals with best in-class skill sets combined with industry-leading certifications advance more rapidly than your peers and typically earn 3%-5% above their colleagues. Second, Get Published. Relevant, succinct and insightful articles on best practices in your technical domain or functional discipline enhance your credibility and integrity. And third, Get Connected. Developing and maintaining a robust professional network - locally and globally - bolsters your career persona and positions you as the “go to” subject matter expert in your field.

Download your copy and register today. Share with your colleagues!: https://tinyurl.com/2p996t8d 

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

Deep Learning with TensorFlow (Application of Neural Networks)

Colleagues, the Deep Learning with TensorFlow - Applications of Deep Neural Networks to Machine Learning Tasks program brings machine-learning 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 plus provided Python-based Jupyter notebooks. This intermediate-level program includes over 6 hours of instruction. Learn to Build Deep Learning models in TensorFlow and Keras, Interpret the results of Deep Learning models, Troubleshoot and improve Deep Learning models. Skill-based training modules include: 1)  Introduction to Deep Learning - overview of deep learning, its roots in artificial neural networks, and the breadth of transformative applications it produces and builds an introductory neural network, 2) How Deep Learning Works - main families of deep neural networks and their applications, shows you deep learning in action via a web application called the TensorFlow Playground, 3) Convolutional Networks - construct a deep network. This lesson builds upon those theoretical foundations to build more effective deep nets, gain an understanding of convolutional layers and how they can be stacked to solve increasingly complex problems with larger data sets. In order to make sense of the outputs from these sophisticated models, the TensorBoard result-visualization tool is used, 4) Introduction to TensorFlow - leading Deep Learning libraries are compared, and then you get down to business with TensorFlow, the open-source library doing the heavy neural network-lifting underneath Keras and build your own deep learning models in TensorFlow, and 5) Improving Deep Networks - tune model hyperparameters and create a deep learning project as well as outlining resources for further self-study.

Enroll today (individuals & teams welcome): https://tinyurl.com/4rzp4r5m 

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

Python, TensorFlow & PyTorch - Career Transformation Guide

Colleagues, the Python, TensorFlow and PyTorch  - Career Transformation Guide for 2022 includes valuable information that enables you to accelerate your career growth and income potential - Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Community and Networking resources.There are three critical success factors in our career transformation model. First, Get Trained and Certified. Professionals with best in-class skill sets combined with industry-leading certifications advance more rapidly than your peers and typically earn 3%-5% above their colleagues. Second, Get Published. Relevant, succinct and insightful articles on best practices in your technical domain or functional discipline enhance your credibility and integrity. And third, Get Connected. Developing and maintaining a robust professional network - locally and globally - bolsters your career persona and positions you as the “go to” subject matter expert in your field. Salary.com reports the media salary for Python Developers across the US is $95,120 USD. ZipRecruiter lists 214.206 Python development positions in the US alone. 

Download your copy and register today. Share with your colleagues!: https://tinyurl.com/bdhamd8e 

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

Supervised Learning - Linear Regression in Python

Colleagues, the Supervised Learning - Linear Regression in Python will equip you to apply Least Squares regression and its assumptions to real world data. Then we'll improve on that algorithm with Penalized Regression and non-parametric Kernel methods Learn to apply algorithms in the following areas: Least squares, Penalized least squares, Non-parametric methods, Model selection and fit on real world applications including Insurance and Healthcare. The curriculum addresses: 1) Introduction to Supervised Linear Regression, 2) Introduction to Machine Learning and Supervised Regression - Introduction to Machine Learning and Supervised Regression, Discuss the overall AI ecosystem and how Machine Learning (ML) is part of that ecosystem. - Understand the 3 different types of algorithms that make up ML - Provide some intuition for why functions and optimizations are important in ML, 3) Machine Learning - Understanding Assumptions and survey the statistical concepts important to understanding Linear Algorithms. - Design of experiments. - Conducting experiments. - Understand the difference between linear and non-linear functions, 4) Least Squares Regression - Ordinary Regression - Develop the simple linear regression algorithm. Understand the basic linear regression assumptions. Learn to identify when assumption violations occur. Understand how to evaluate model output, 5) Least Squares Regression - Multiple Regression - Extend the Least Squares algorithm to multiple dimensions Explore data to understand variable importance Prepare data for multiple regression Optimizing between Bias and Variance, 6) Penalized Regression - L1/L2 Optimization - understand motivation behind penalized regression Optimize L1 Regression (Lasso) parameters Optimize L2 Regression (Ridge) parameters Combine the L1/L2 penalties (Elastic Net) Understand the difference and trade offs between Subset Selection and Shrinkage Optimize hyper-parameters with Cross-Validation, 7) Kernel Methods - Support Vector Machines - Understand theory and motivation behind kernel methods. Derive a basic kernel and use the kernel trick. Build a support vector classifier. Extend to regression with a support vector machine. Optimize parameters with Cross validation and Grid Search, 8) Kernel Methods - Gaussian Process Regression - Understand multivariate distributions and non-parametric regression. Use Bayesian probability with joint probabilities. Develop theory behind Gaussian Process Regression. Optimize kernels and hyper-parameters, and 9) Summary and Real World Applications - Review Supervised Linear Regression topics. Perform Linear regression on real world data.

Enroll today (individuals & teams welcome): https://tinyurl.com/uvjs63tw 

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

Natural Language Processing in TensorFlow

Colleagues, the Natural Language Processing in TensorFlow Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.. Learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network. You’ll also learn to apply RNNs, GRUs, and LSTMs in TensorFlow. Finally, you’ll get to train an  LSTM on existing text to create original poetry! 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. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization. Learn to build natural language processing systems using TensorFlow, process text, including tokenization and representing sentences as vectors, apply RNNs, GRUs, and LSTMs in TensorFlow and train LSTMs on existing text to create original poetry. Gain skills in Natural Language Processing, Tokenization, Machine Learning, Tensorflow and RNNs. Skill-based training modules include: 1) Sentiment in tText, 2) Word Embeddings, 3) Sequence Models, and 4) Sequence Models and Literature. 

Enroll today (eams & execs welcome): https://tinyurl.com/d3euff5u 

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

AWS Machine Learning Engineer

Colleagues, the AWS Machine Learning Engineer program enables you to meet the growing demand for machine learning engineers and master the job-ready skills that will take your career to new heights. master the skills necessary to become a successful ML engineer. Learn the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker. Training modules with hands-on projects cover: 1) Introduction to Machine Learning In this course, you'll start learning about machine learning through high level concepts through AWS SageMaker. Create machine learning workflows, starting with data cleaning and feature engineering, to evaluation and hyperparameter tuning. Finally, you'll build new ML workflows with highly sophisticated models such as XGBoost and AutoGluon (Project: Bike Sharing Demand with AutoGluon), 2) Developing Your First ML Workflow - machine learning workflows on AWS. Learn how to monitor machine learning workflows with services like Model Monitor and Feature Store. With all this, you’ll have all the information you need to create an end-to-end machine learning pipeline (Project: Build and ML Workflow on SageMaker), 3) Deep Learning Topics within Computer Vision and NLP - train, finetune, and deploy deep learning models using Amazon SageMaker. Learn about advanced neural network architectures like Convolutional Neural Networks and BERT, as well as how to finetune them for specific tasks. Finally, you will learn about Amazon SageMaker and you will take everything you learned and do them in SageMaker Studio (Project: Image Classification using AWS SageMaker), 4) .Operationalizing Machine Learning Projects on SageMaker - deploy professional machine learning projects on SageMaker. It also covers security applications. Learn how to deploy projects that can handle high traffic and how to work with especially large datasets (Project: Operationalizing an AWS ML Project), and 5) Capstone Project - .Inventory Monitoring at Distribution Centers. To build this project, students will have to use AWS Sagemaker and good machine learning engineering practices to fetch data from a database, preprocess it and then train a machine learning model. This project will serve as a demonstration of end-to-end machine learning engineering skills that will be an important piece of their job-ready portfolio.

Enroll today (eams & execs welcome): https://tinyurl.com/yckjbdnb 

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

Natural Language Processing in TensorFlow Specialization

Colleagues, the Natural Language Processing in TensorFlow Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.. Learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network. You’ll also learn to apply RNNs, GRUs, and LSTMs in TensorFlow. Finally, you’ll get to train an  LSTM on existing text to create original poetry! 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. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization. Learn to build natural language processing systems using TensorFlow, process text, including tokenization and representing sentences as vectors, apply RNNs, GRUs, and LSTMs in TensorFlow and train LSTMs on existing text to create original poetry. Gain skills in Natural Language Processing, Tokenization, Machine Learning, Tensorflow and RNNs. Skill-based training modules include: 1) Sentiment in tText, 2) Word Embeddings, 3) Sequence Models, and 4) Sequence Models and Literature. 

Enroll today (eams & execs welcome): https://tinyurl.com/d3euff5u 

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

Artificial Intelligence for Trading (Training)

Colleagues, the Artificial Intelligence for Trading program involves real-world projects designed by industry experts, covering topics from asset management to trading signal generation. Master AI algorithms for trading, and build your career-ready portfolio. Learn the basics of quantitative analysis, including data processing, trading signal generation, and portfolio management. Use Python to work with historical stock data, develop trading strategies, and construct a multi-factor model with optimization. Training modules - each with a hands-on project - include: 1) Basic Quantitative Trading - market mechanics and how to generate signals with stock data. Work on developing a momentum-trading strategy in your first project. (Project: Trading with Momentum); 2) Advanced Quantitative Trading - quant workflow for signal generation, and apply advanced quantitative methods commonly used in trading (Project: Breakout Strategy); 3) Stocks, Indices,and ETFs - portfolio optimization, and financial securities formed by stocks, including market indices, vanilla ETFs, and Smart Beta ETFs (Project: Smart Beta and Portfolio Optimization); 4) Factor Investing and Alpha Research - alpha and risk factors, and construct a portfolio with advanced optimization techniques (Project: Alpha Research and Factor Modeling); 5) Sentiment Analysis with Natural Language Processing - fundamentals of text processing, and analyze corporate filings to generate sentiment-based trading signals (Project: Sentiment Analysis Using NLP); 6) Advanced Natural Language Processing with Deep Learning - apply deep learning in quantitative analysis and use recurrent neural networks and long short-term memory to generate trading signals (Project: Deep Neural Network with News Data); 7) Combining Multiple Signals - advanced techniques to select and combine the factors you’ve generated from both traditional and alternative data (Project: Combine Signals for Enhanced Alpha); and 8) Simulating Trades with Historical Data - refine trading signals by running rigorous back tests. Track your P&L while your algorithm buys and sells (Project: Backtesting).

Enroll today (eams & execs welcome): https://tinyurl.com/hw9tnjjf 

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

Deep Neural Networks with PyTorch (IBM)

AI colleagues, the Deep Neural Networks with PyTorch from IBM equips you  to develop deep learning models using  Pytorch. The course will start with Pytorch's  tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by  Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered. After completing this program you will be able to explain and apply their knowledge of Deep Neural Networks and related machine learning methods, know how to use Python libraries such as PyTorch  for Deep Learning applications and build Deep Neural Networks using PyTorch. Skill-based training modules cover: 1) Tensor and Datasets, 2) Differentiation in PyTorch, 3) Simple Datasets, 4) Linear Regression, 2) Gradient Descent, 3) Prediction in One Dimension, 4) PyTorch Linear Regression Training Slope and Bia, 5) Training Parameters in PyTorch, 6) Multiple Input Output Linear Regression, 7) Multiple Output Linear Regression, 8) Linear Classifier, 9) Logistic Regression: Prediction, 10) Bernoulli Distribution and Maximum Likelihood Estimation, 11) Softmax Regression, 12) Deep Neural Networks, 13) Convolutional Neural Network, and 14) TorchVision Models.

Enroll today (eams & execs welcome): https://tinyurl.com/4uj75b2z 

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

Machine Learning Engineer for Microsoft Azure

Colleagues, the Machine Learning Engineer for Microsoft Azure program will strengthen your machine learning skills and build practical experience by training, validating, and evaluating models using Azure Machine Learning. Students will enhance their skills by building and deploying sophisticated machine learning solutions using popular open source tools and frameworks, and gain practical experience running complex machine learning tasks using the built-in Azure labs accessible inside the Udacity classroom. The three skill-based training modules with a hands-on project include: 1) Using Azure Machine Learning - Machine learning is a critical business operation for many organizations. 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. All these concepts are part of core DevOps pillars that will allow you to demonstrate solid skills for shipping machine learning models into production (Project - Operationalizing Machine Learning), and 3) Capstone Project - 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. Finally, you will have to deploy the model as a web service and test the model endpoint. Prior experience with Python, Machine Learning, and Statistics is recommended.

Sign-up today (teams & execs welcome): https://tinyurl.com/2pe8hrvj 

Much career success, Lawrence E. Wilson - Online Learning Central