TensorFlow 2.0 for Deep Learning (Training)

Colleagues, the TensorFlow 2.0 for Deep Learning program provides you with the core of deep learning using TensorFlow 2.0. You’ll learn to 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. By the end of the course, you’ll have a profound knowledge of how you can leverage TensorFlow 2.0 to build real-world applications without much effort. You will learn to Develop real-world deep learning applications, Classify IMDb Movie Reviews using Binary Classification Model, Build a model to classify news with multi-label, Train your deep learning model to predict house prices, Understand the whole package: prepare a dataset, build the deep learning model, and validate results, Assess the working of Recurrent Neural Networks and LSTM with hands-on examples, and Implement autoencoders and denoise autoencoders in a project to regenerate images. Skill-based training modules: 1) Deep Learning Basics, 2) TensorFlow 2.0 for Deep Learning, 3) Working with CNNs for Computer Vision and Deep Learning, 4) Working with LSTM for Text Data and Deep Learning, 5) Working with RNNs for Time Series Sequences and Deep Learning, 6) Autoencoders EAE and Denoising AE, and 7) Deep Learning Mini-Projects. This program uses a dedicated GitHub workspace.

Enroll today (individuals & teams welcome): https://tinyurl.com/4mu8fyfk 

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

Advanced Predictive Techniques With Scikit-Learn & TensorFlow

Colleagues, the Advanced Predictive Techniques With Scikit-Learn And TensorFlow program will equip you to use ensemble methods to improve accuracy in classification and regression problems. Ensemble methods improve prediction accuracy by combining in a clever way predictions from many individual predictors. Artificial Neural Networks are models loosely based on how neural networks work in a living being. This course introduces the use of Deep Learning models for Predictive Analytics using the powerful TensorFlow library. 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, grasp the foundations for working and building models using Neural Networks and understand different techniques to solve problems that arise when doing Predictive Analytics in the real world. 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 5) Predictive Analytics with TensorFlow and Deep Neural Networks.  

Enroll today (individuals & teams welcome): https://tinyurl.com/4tyd7vjc 

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

Implementing Deep Learning Algorithms with TensorFlow 2.0

Colleagues, the Implementing Deep Learning Algorithms with TensorFlow 2.0 provides you with a solid understanding of Deep Learning models and use Deep Learning techniques to solve business and other real-world problems. Learn various Deep Learning approaches such as CNN, RNN, and LSTM and implement them with TensorFlow 2.0. You will program a model to classify breast cancer, predict stock market prices, process text as part of Natural Language Processing (NLP),  Skill-based training modules include: 1)  Deep Learning Introduction and Environment Setup, 2) Building First Neural Network for Tabular Data with TensorFlow 2.0, 3) Convolutional Neural Networks with TensorFlow 2.0, 4) Recurrent Neural Network with TensorFlow 2.0, 5) Long Short-Term Memory Networks (LSTM), and 6) Transfer Learning with TensorFlow 2.0.  Basic knowledge of Python is assumed. You will understand what problems Deep Learning and TensorFlow 2.0 have solved and can solve, various Deep Learning model architectures and work with them, 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.

Enroll today (individuals & teams welcome): https://tinyurl.com/a9utjzxc 

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

Google Cloud Machine Learning with TensorFlow

Colleagues, the Google Cloud Machine Learning with TensorFlow equips you to use Google Cloud to train TensorFlow models and use them to predict results for multiple users. You will learn to efficiently train neural networks using large datasets and to serve your training models. You will use the power of Google's Cloud Platform to train deep neural networks faster. Learn to run predictions for your model using the cloud. You will also explore topics such as cloud infrastructures, distributed training, serverless technologies, model serving. Finally, you will learn to use GPUs organized in clusters to optimize your performance, train bigger models faster using the Google Cloud infrastructure, explore machine types and learn how to configure clusters to solve problems, train deep learning models using the Google Cloud AI Platform, run classical machine learning algorithms with TensorFlow, and execute your trained models to get predictions using the Artificial Intelligence Platform API. The program includes 40 lectures spanning the following training modules: 1) A Quick Start with Google Cloud Platform, 2) Machine Learning with TensorFlow Fundamentals, 3) Basic Model Training with TensorFlow 2.0, 4) Advanced Model Training with TensorFlow 2.0, 4) Serving Model Predictions with TensorFlow on GCP, and 6) Neural  Networks.

Enroll today (individuals & teams welcome): https://tinyurl.com/vj957fya 

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

Introduction to Deep Learning (and Neural Networks)

Colleagues, the Introduction to Deep Learning program will equip you in all the important concepts relating to deep learning models and how they give rise to the recent results in AI. We use guided examples and discuss a variety of practical applications, all accompanied by animations and visualizations. We also cover recent breakthroughs in deep learning research. This course will demystify the models that underpin the recent AI revolution and provide a solid foundation for further learning. Skill-based training modules include: 1) Fundamentals, 2) Perceptron: Weights, Biases, Activation Functions, 3) Multi-neuron Networks : XOR and nonlinearity, and 4)  Learning: Gradient Descent. After taking this course you will understand What deep learning is and how it  differs from other types of machine learning and artificial intelligence, How deep learning models use neural networks to make computations, What types of problems deep learning models can be used to solve, Types of data needed to train deep learning models, Variety of inputs deep  learning models receive and solutions they produce, Advantages that deep learning can offer over traditional machine learning, Why multi-neuron networks are able to solve complex problems, How neural networks use gradient descent and back-propagation to learn to make predictions.

Enroll today (individuals & teams welcome): https://tinyurl.com/phdve8xj 

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

Unsupervised Machine Learning Projects with R

Colleagues, the Unsupervised Machine Learning Projects with R will help you build your knowledge and skills by guiding you in building machine learning projects with a practical approach and using the latest technologies provided by the R language such as Rmarkdown, R-shiny, and more. Also focus on two machine learning paradigms—K-Means Clustering and Principal Component Analysis—to grasp how they work and apply them to business Customer Segmentation (Market Segmentation Analysis). You will be equipped to deploy Machine Learning algorithms in R, explore K-means clustering techniques, prepare data for imputation and model diagnostics, train, evaluate, and improve your models, visualize the Principal Component Analysis model in 2D, perform pattern mining for transactional data, understand mocking  and how to use mocking frameworks and select design patterns. Skill-based training modules include:  1) Machine Learning Model in R, 2)  Exploring K-Means Clustering, 3) Principal Component Analysis (PCA), and 4) Pattern Mining.

Enroll today (individuals & teams welcome): https://tinyurl.com/2hxf3vzx 

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

Predictive Modeling and Machine Learning with MATLAB

ML colleagues, the Predictive Modeling and Machine Learning with MATLAB program will increase your ability to harness the power of MATLAB. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background. You will use MATLAB to identify the best machine learning model for obtaining answers from your data. You will prepare your data, train a predictive model, evaluate and improve your model, and understand how to get the most out of your models deepening your skills in Machine  Learning, Matlab and Predictive Modeling. Training modules include: 1) Creating Regression Models: You will be introduced to the Supervised Machine Learning Workflow, creating and evaluating regression machine learning models (11 videos (Total 73 min), 7 readings, 7 quizzes), 2) Creating Classification Models: Grasp the basics of classification models. Train several types of classification models and evaluate the results 6 videos (Total 45 min), 6 readings, 2 quizzes, 3) Applying the Supervised Machine Learning Workflow: Complete supervised machine learning workflow. You'll use validation data to inform model creation. You'll apply different feature selection techniques to reduce model complexity. Then you will create ensemble models and optimize hyperparameters. At the end of the module, you'll apply these concepts to a final project. 9 videos (Total 49 min), 5 readings, 3 quizzes.

Enroll today (individuals & teams welcome): https://tinyurl.com/28ru79mx 

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

Advanced Computer Vision with TensorFlow

Colleagues, the Advanced Computer Vision with TensorFlow program will equip you to explore image classification, image segmentation, object localization, object detection, apply transfer learning to object localization and detection. Apply object detection models such as regional-CNN and ResNet-50, customize existing models, and build your own models to detect, localize, and label your own rubber duck images. Implement image segmentation using variations of the fully convolutional network (FCN) including U-Net and mask-RCNN to identify and detect numbers, pets and zombies. Identify which parts of an image are being used by your model to make its predictions using class activation maps and saliency maps and apply machine learning interpretation methods to inspect and improve the design of AlexNet. Training modules include: 1) Introduction to Computer Vision: Describe multi-label classification, and distinguish between semantic segmentation and instance segmentation, 2) Object Detection: Understand object detection models, such as regional-CNN and ResNet-50. You’ll use object detection models that you’ll retrieve from TensorFlow Hub, download your own models and configure them for training, 3) Image Segmentation: Assign class labels to each pixel, and perform detailed identification of objects compared to bounding boxes. You will build the convolutional neural network, U-Net, and Mask R-CNN to identify, and 4) Visualization and Interpretability: Understand how your model arrives at its decisions and visualize a model’s intermediate layer activations.

Enroll today (individuals & teams welcome): https://tinyurl.com/h4uspery 

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

Mastering Keras (with Certificate of Completion)

Colleagues, TensorFlow (and it's easy-to-learn deep learning wrapper Keras) have become game-changers in permitting simple implementations of the most complex of deep learning techniques.You will be equipped to use Keras’ full power, and unleash the amazing potential of advanced deep learning on your data science problems. You will learn to design and train deep learning models for synthetic data generation, object detection, one-shot learning, and much more. You will be able to implement many advanced deep learning modelling algorithms and adapt them to your own purposes. Please note that familiarity with machine learning and deep learning approaches, together with practical experience with Keras and Python programming, are assumed for taking this course. The program include 31 lectures and seven training modules: 1) Preparing Yourself for Mastering Keras Journey, 2) Working with the Keras Functional API, 3) Developing and Implementing Deep Generative Models, 4) Advanced CNNs, 5) Object Detection, 6) Deep Reinforcement Learning, and 7) One-Shot and Deep Semi-Supervised Learning.

Enroll today (individuals & teams welcome): https://tinyurl.com/r65mrswh 

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

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