TensorFlow 2.0 - Three Best In-Breed Programs for Career Growth

Dev colleagues, TensorFlow 2.0 continues its rapid growth as the premier algorithm library for use with Python. To gain a competitive edge in your development reer here are three hand-picked TF 2.0 programs to sharpen and expand your TensorFlow skillset. First, the Deep Learning Algorithms With TensorFlow 2.0 program will train you to build Deep Learning neural networks with TensorFlow 2.0. Deep Learning has become the dominant method for speech recognition (Google Assistant), computer vision (search for "my pictures" on Google Photos), language translation, and even game-related Artificial Intelligence (AlphaGo and DeepMind). Gain a solid understanding of Deep Learning models and use Deep Learning techniques to solve business and other real-world problems to make predictions quickly and easily. Next, the Natural Language Processing with TensorFlow 2.0 Specialization from Andrew Ng teaches 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. And third, the TensorFlow 2.0 for Deep Learning program provides you with the core of deep learning using TensorFlow 2.0. Train your deep learning networks from scratch, pre-process and split your datasets, train deep learning models for real-world applications, and validate the accuracy of your models. You will learn to Develop real-world deep learning applications and Classify IMDb Movie Reviews using Binary Classification Model.

Sign-up today for one or all programs (individuals & teams welcome):

• Deep Learning Algorithms With TensorFlow 2.0

• Natural Language Processing with TensorFlow 2.0 Specialization

• TensorFlow 2.0 for Deep Learning

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, Understand the language and fundamentals of artificial neural networks and Build your own Deep Learning project. 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. He introduces the archetypal deep learning data sets, and then you build a deep neural network together to tackle a classic machine vision problem, 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/uvyfnwwf 

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

Deep Learning Algorithms With TensorFlow 2.0

Colleagues the Deep Learning Algorithms With TensorFlow 2.0 program will train you to build Deep Learning neural networks with TensorFlow 2.0. Deep Learning has become the dominant method for speech recognition (Google Assistant), computer vision (search for "my pictures" on Google Photos), language translation, and even game-related Artificial Intelligence (AlphaGo and DeepMind). Gain a solid understanding of Deep Learning models and use Deep Learning techniques to solve business and other real-world problems to make predictions quickly and easily. You’ll learn various Deep Learning approaches such as CNN, RNN, and LSTM and implement them with TensorFlow 2.0. You’ll program a model to classify breast cancer, predict stock market prices, and process text as part of Natural Language Processing (NLP). Grasp what Deep Learning and TensorFlow 2.0 are and what problems they have solved and can solve, Study the various Deep Learning model architectures, Apply neural network models, deep learning, NLP, and LSTM to several diverse data classification scenarios, including breast cancer classification; predicting stock market data for Google; classifying Reuters news topics; and classifying flower species, and Apply your newly-acquired skills to a wide array of real-world scenarios. Skill-based training modules include: 1) Deep Learning Introduction and Environment Setup, 2) Building Your First Neural Network for Tabular Data with TensorFlow 2.0, 3) Convolutional Neural Networks with TensorFlow 2.0, 4) Recurrent Neural Network with TensorFlow, 5) Long Short-Term Memory Networks (LSTM), and 6) Transfer Learning with TensorFlow.

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

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

Advanced Predictive Techniques with Scikit-Learn and TensorFlow

Colleagues, the Advanced Predictive Techniques With Scikit-Learn And TensorFlow program will train you in advanced predictive techniques with Scikit-Learn and TensorFlow. Better the performance of predictive models, build more complex models, and improve the quality of your predictive models.Ensemble methods offer a powerful way to improve prediction accuracy by combining in a clever way predictions from many individual predictors. In this course, you will learn how to use ensemble methods to improve accuracy in classification and regression problems. Learn to use ensemble algorithms to combine many individual predictors to produce better predictions, apply advanced techniques such as dimensionality reduction to combine features and build better models, evaluate models and choose the optimal hyper-parameters using cross-validation the foundations for working and building models using Neural Networks, and learn different techniques to solve problems that arise when doing Predictive Analytics in the real world. Skill-based training modules include: 1) Ensemble Methods for Regression and Classification, 2) Cross-Validation and Parameter Tuning, 3) Working with Features, 4) Introduction to Artificial Neural Networks and TensorFlow, and Predictive Analytics with TensorFlow and Deep Neural Network.

Enroll today (individuals & teams welcome): https://tinyurl.com/bd6r42dc 

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

Deep Learning Using OpenPose - Learn Pose Estimation Models and Build 5 AI Apps

Colleagues, the Deep Learning Using OpenPose program teaches you the Pose Estimation computer vision technique that can detect human figures in both images and videos. Imagine developing your own Pose Estimation applications without the specialized hardware, just using an ordinary webcam and the power of artificial intelligence (AI). You’ll get started with Pose Estimation, from learning the fundamentals of the technology through to implementing the OpenPose framework in real-time. You will also understand how to adapt this framework for 5 practical applications including: Fall detection, Counting people, Yoga pose identification, Plank pose correction, and Automatic body ratio calculation. Learn to Understand Pose Estimation, How to implement your own fall detection app, Use OpenPose to count people, Apply Pose Estimation for yoga pose identification, Ensure perfect planking and push-up posture with OpenPose and Calculate real-time body ratios. All code for this program is hosted on GitHub. 

Enroll today (individuals & teams welcome): https://tinyurl.com/3bvyhmea 

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

Machine Learning and AI with Python and Jupyter Notebook

Colleagues, the  Essential Machine Learning and AI with Python and Jupyter Notebook program shows you how AWS and Google Cloud Platform can be used to solve real-world business problems in Machine Learning and AI. It covers how to get started with Python via Jupyter Notebook, and then proceeds to dive into nuts and bolts of Data Science libraries in Python, including Pandas, Seaborn, scikit-learn, and TensorFlow. EDA, or exploratory data analysis, is at the heart of Machine Learning;. Software engineering fundamentals tie the series together, with key instruction on linting, testing, command-line tools, data engineering APIs. Learn Data Science concepts and Python fundamentals for Machine Learning, how to develop a Data Engineering API with Flask and Pandas,EDA, Python and AWS, and understand both Python and Google Cloud Platform. Training modules address: 1) Data Science Coding with Python Fundamentals, 2) Applying Functions, 3) Python Control Structures, 4) Deploying Libraries in Python, 5) Python Classes, 6) IO Operations in Python and Pandas, 7) Software Carpentry, 8) Data Engineering API with Flask and Pandas, 9) Social Power NBA EDA and ML Project, 10) Intermediate Machine Learning, 11)  Python based AWS Cloud ML and AI Pipelines, 12) Python based Google Compute Platform ML and AI Pipelines,  13) Command-line Machine Learning Tools, and 14) Datascience: Case Study Social Power in the NBA. 

Enroll today (individuals & teams welcome): https://tinyurl.com/2s246wzm 

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

Deep Learning for Natural Language Processing

Colleagues, the Deep Learning for Natural Language Processing - Second Edition program  is an introduction to building natural language models with deep learning. These lessons bring intuitive explanations of essential theory to life with interactive, hands-on Jupyter notebook demos. Examples feature Python and Keras, the high-level API for TensorFlow 2, the most popular Deep Learning library. In early lessons, specifics of working with natural language data are covered, including how to convert natural language into numerical representations that can be readily processed by machine learning approaches. In later lessons, state-of-the art Deep Learning architectures are leveraged to make predictions with natural language data. This program is for intermediate skill level professionals. Learn How To Preprocess natural language data for use in machine learning applications, Transform natural language into numerical representations with word2vec, Make predictions with Deep Learning models trained on natural language, Apply state-of-the-art NLP approaches with Keras, the high-level API for TensorFlow 2, and Improve Deep Learning model performance by selecting appropriate model architectures and tuning model hyperparameters. Training modules that equip with high-demand skills address: 1)The Power and Elegance of Deep Learning for NLP, 2) Word Vectors, 3) Modeling Natural Language Data, 4) Recurrent Neural Networks, and 5) Advanced Models. 

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

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

Practical Python Data Science Techniques

Colleagues, the Practical Python Data Science Techniques program equips you to calculate the word frequencies using Data Science techniques of Python, work with Scikit-learn to solve problems in Machine Learning, and perform Cluster Analysis using Python Data Science. You will learn to perform Exploratory data analysis on your Data, Clean and process your Data to have the right shape, Tokenize your Document to words with Python, Calculate the word frequencies using Data Science Techniques of Python, Work with scikit-learn to solve every problem in Machine Learning, Perform Cluster Analysis using Python Data Science Techniques, and Build a Time Series Analysis with Panda. Work time dimension data and learn to build a recommendation system as well as about supervised learning problems (regression and classification) and unsupervised learning problems (clustering). Learn to perform text preprocessing steps that are necessary for every text analysis application. Specifically, the course will cover tokenization, stop-word removal, stemming and other preprocessing techniques.Training modules address: 1) Exploring Your Data, 2) Dealing with Text, 3) Machine Learning Problems, and 4) Time Series and Recommender Systems.

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

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

PyTorch For Deep Learning and Computer Vision

Colleagues, PyTorch is becoming a very transformative framework in the field of Deep Learning. Its flexibility has made building a Deep Learning model easier. This PyTorch For Deep Learning And Computer Vision teaches you Deep Learning with PyTorch. PyTorch has completely changed the landscape of the deep learning domain with its flexibility and has made building deep learning models easier. The development world offers some of the highest paying jobs in deep learning. Instructor Rayan Slim will help you learn and master deep learning with PyTorch. Having taught over 44,000 students, Rayan is a highly rated and experienced instructor who has followed a learning-by-doing style to create this course. By the end of this course, you will have built state-of-the-art deep learning and Computer Vision applications with PyTorch. You will learn the tensor data structure, machine and deep learning applications with PyTorch, neural networks from scratch, applied themes of advanced imagery and Computer Vision, complex problem solvings in Computer Vision by harnessing highly sophisticated pre-trained models and style transfer to build sophisticated AI applications. Skill-based training modules address: 1) Intro to Tensors, 2) Linear Regression, 3) Perceptrons, 4) Deep Neural Networks, 5) Image Recognition, 6) Convolutional Neural Networks, 7) CIFAR 10 Classification, 8) Transfer Learning, 9) Style Transfer – PyTorch plus two Appendices: Crash courses in Python and NumPy.

Enroll today (individuals & teams welcome): https://tinyurl.com/25bc4549 

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