Amazon Machine Learning (Training)

ML colleagues, this Amazon Machine Learning includes over 20 independent lessons totaling more than 3 hours of instruction with demos, interactive hands-on labs and detailed slide explanations. You will be equipped to solve for personalization, search, marketing, finance, productivity, and management efficiency using AML, configure a schema, and set up a data source using “small data” in S3, use data insights and visualization tools, leverage Features, Targets, Observations, Labeled Data, Unlabeled Data, and Ground Truth to prepare historical data for predictive analysis, and prepare data for use in a regression model and a multi-class model. Skill-based training modules address: 1) Machine Learning Basics, 2) Machine Learning Data Architecture, 3) Data and Schema Configuration, 4) Visualization and Modeling, and 5) Predictions with Amazon Machine Learning. Hands-on labs Usage scenarios are provided to inspire viewers to create their own value-added services on top of Amazon Machine Learning.

Enroll today at: https://tinyurl.com/y3krtokl

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

Python for Data Science Certification

Data Science colleagues, this Python Certification Training not only focuses on fundamentals of Python, Statistics and Machine Learning but also helps one gain expertise in applied Data Science at scale using Python. The training is a step by step guide to Python and Data Science with extensive hands on. The course is packed with several activity problems and assignments and scenarios that help you gain practical experience in addressing predictive modeling problem that would either require Machine Learning using Python. Starting from basics of Statistics such as mean, median and mode to exploring features such as Data Analysis, Regression, Classification, Clustering, Naive Bayes, Cross Validation, Label Encoding, Random Forests, Decision Trees and Support Vector Machines. Gain skills that will strengthen your career and equip you to pass the certification exam, including: 1) Sequences and File Operations, 2) Deep Dive – Functions, OOPs, Modules, Errors and Exceptions, 3) NumPy, Pandas and Matplotlib, 4) Data Manipulation, 5) Machine Learning with Python, 6) Supervised Learning, 7) Dimensionality Reduction, 8) Unsupervised Learning, 9) Association Rules Mining and Recommendation Systems, 10) Reinforcement Learning, 11) Time Series Analysis, plus 12) Model Selection and Boosting.

Register today at: https://fxo.co/9QE9

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

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Colleagues, join over 170k professionals who have enrolled in this new deeplearning.ai TensorFlow Specialization. Andrew Ng 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 valuable core skills in Computer Vision, TensorFlow and Machine Learning. Training modules address: 1) A New Programming Paradigm, 2) Introduction to Computer Vision, 3) Enhancing Vision with Convolutional Neural Networks, and 4) Using Real-world Images. Learn best practices for using TensorFlow, build a basic neural network in TensorFlow, understand how to use convolutions to improve your neural network, and train a neural network for a computer vision application.

Begin your journey today. Enroll at: https://tinyurl.com/yy7q4f4n

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

AI & Deep Learning in TensorFlow with Python (Deep Learning Engineer Certification)

Artificial Intelligence colleagues, join 1000s of professional at companies like VMware, Cisco, Dell and Honeywell who earned their Deep Learning Engineer Certification. You will learn about AI, neural networks, deep learning frameworks, and implementing machine learning algorithms using Deep Networks. We will also explore how different layers in neural networks does data abstraction and feature extraction using Deep Learning. Deep Learning in TensorFlow training is designed to make you a Data Scientist by providing you rich hands-on training on Deep Learning in TensorFlow with Python. This course is a stepping stone in your Data Science journey using which you will get the opportunity to work on various Deep Learning projects. Skill-based training modules include: 1) Introduction to Deep Learning, 2) Understanding Neural Networks with TensorFlow, 3) Deep dive into Neural Networks with TensorFlow, 4) Master Deep Networks, 4) Convolutional Neural Networks (CNN), 5) Recurrent Neural Networks (RNN), 6) Restricted Boltzmann Machine (RBM) and Autoencoders, 6) Keras API, and 7) TFLearn API ... along with an in-class Capstone Project.

Enroll today at: https://fxo.co/9PYj

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

AWS Certified Machine Learning-Specialty Training (ML-S)

ML colleagues, this program includes 7 hours of training that will equip you to pass the AWS Machine Learning-Specialty (ML-S) Certification exam. Data Engineering instruction covers the ingestion, cleaning, and maintenance of data on AWS. Exploratory Data Analysis (EDA) covers topics including data visualization, descriptive statistics, and dimension reduction and includes information on relevant AWS services. Machine Learning Modeling addresses feature engineering, performance metrics, overfitting, and algorithm selection. Operations covers deploying models, A/B testing, using AI services versus training your own model, and proper cost utilization. Skill-based modules focus on: 1) AWS Machine Learning-Specialty (ML-S) Certification, 2) Data Engineering for ML on AWS, 3) Exploratory Data Analysis on AWS, 4) Machine Learning Modeling on AWS, 5) Operationalize Machine Learning on AWS, 6) Create a Production Machine Learning Application, and 7) Case Studies on Sagemaker, DeepLense, Kinesis Features, AWS Flavored Python and Cloud9.

Register today at: https://tinyurl.com/ya6ko8sg

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

Building Artificial Intelligence Apps on Google Cloud Platform

AI and GCP colleagues, this innovative training program is designed for developers who want to expand their Data Science skills and build AutoML applications in the Cloud. You will learn how to use AutoML, Big Query, Python, and Google App Engine to create sophisticated AI applications. Cloud AutoML is a suite of machine learning products that allows developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google’s state-of-the-art transfer learning and neural architecture search technology. Developers use Cloud AutoML’s graphical user interface to train, evaluate, improve, and deploy models based on their data. The seven (7) training modules will equip you to: 1) Create an Application Skeleton on GCP using Google App Engine, 2) Build ETL (Extract Transform Load) Pipelines, 3) Use ML Prediction on BigQuery, 4) Use AutoML, 5) Use AI Platform, 6) Build an Analytics Application, and 7) Implement Build Systems and Containers.

Enroll today at: https://tinyurl.com/y8zwv3lf

Much career success, Lawrence E. Wilson - Online Learning Central (OLC) and Artificial Intelligence Academy (AIA)

Data Science Specialization Training – Johns Hopkins University

Colleagues, this Data Science Specialization from one of America’s top universities will equip you to use R to clean, analyze, and visualize data, Use GitHub to manage data science projects, Navigate the entire data science pipeline from data acquisition to publication, and Perform regression analysis, least squares and inference using regression models. This Specialization includes ten courses: Data Scientist’s Toolbox, R Programming, Cleaning Data, Exploratory Data Analysis, Reproducible Data, Statistical Inference, Regression Models, Practical Machine Learning, Developing Data Products and a Data Science Capstone Project.

Register today at: https://fxo.co/7ux5

Much career success, Lawrence Wilson –  Artificial Intelligence Academy

Machine Learning Specialization (University of Washington)

Software Developers & Engineers, are you ready to Build Intelligent Applications? Master machine learning fundamentals in four hands-on courses. This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data. There are 4 Courses in this Specialization which include: 1) Machine Learning Foundations: A Case Study Approach, 2) Machine Learning: Regression, 3) Machine Learning: Classification, and 4) Machine Learning: Clustering & Retrieval. You will gain high demand, marketable skills in Data Clustering Algorithms, Machine Learning, Classification Algorithms, Decision Tree.

Register today at https://fxo.co/7pic

Much career success, Lawrence Wilson – Artificial Intelligence Academy

Programming Foundations of Classification and Regression (Machine Learning with Python series)

Colleagues, this video-based training program includes over four hours of Code-along sessions move you from introductory machine learning concepts to concrete code. Machine learning is moving from futuristic AI projects to data analysis on your desk. Code-along sessions move you from introductory machine learning concepts to concrete code. Machine learning is moving from futuristic AI projects to data analysis on your desk. You need to go beyond nodding along in discussion to coding machine learning tasks. These videos show you how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends.You will learn how to Build and apply simple classification and regression models, Evaluate learning performance with train-test splits, Evaluate learning performance with metrics tailored to classification and regression, and Evaluate the resource usage of your learning models. Core skills-based training modules will equip you with: 1) Software Background (Numpy, Matplotlib, scikit-learn, seaborn, and pandas–high-level packages), 2) Mathematical Background, 3) Beginning Classification (Part I): Two models: k-nearest neighbors and naive Bayes, 4) Beginning Classification (Part II): Evaluate learning performance with accuracy and how to evaluate resource utilization for memory and time within Jupyter notebooks and also in standalone Python scripts, and 5) Beginning Regression (Parts I and II).

Register today at: https://tinyurl.com/y77nvs7m

Career success awaits you, Lawrence Wilson – Artificial Intelligence Academy

Machine Vision, GANs, Deep Reinforcement Learning (Training)

Colleagues, Machine Vision, GANs, Deep Reinforcement Learning is an introduction to three of the most exciting topics in Deep Learning today. Modern machine vision involves automated systems outperforming humans on image recognition, object detection, and image segmentation tasks. Generative Adversarial Networks cast two Deep Learning networks against each other in a “forger-detective” relationship, enabling the fabrication of stunning, photorealistic images with flexible, user-specifiable elements. Throughout these lessons, essential theory is brought to life with intuitive explanations and interactive, hands-on Jupyter notebook demos. Examples feature Python and straightforward Keras layers in TensorFlow 2, the most popular Deep Learning library. Understand the high-level theory and key language around machine vision, deep reinforcement learning, and generative adversarial networks, Create state-of-the art models for image recognition, object detection, and image segmentation, Architect GANs that create convincing images in the style of human-drawn illustrations, Build deep RL agents that become adept at performing in a wide variety of environments, such as those provided by OpenAI Gym, Run automated experiments for optimizing deep reinforcement learning agent hyperparameters. Training modules include: 1) Orientation, 2) Convolutional Neural Networks for Machine Vision (create ConvNets in TensorFlow), 3) Generative Adversarial Networks for Creativity (generative adversarial networks (GANs), 4) Deep Reinforcement Learning, and 5) Deep Q-Learning and Beyond.

Register today at: https://tinyurl.com/y9mnw3v5

Career success awaits you, Lawrence Wilson – Artificial Intelligence Academy