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

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