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

Data Science at Scale Training – Your ticket to new career and income opportunities

Attention All Data Scientists, gain skills in scaleable data management, evaluate big data technologies, and design effective visualizations. This Specialization covers intermediate topics in data science. You will gain hands-on experience with scaleable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. You will also learn to visualize data and communicate results, and you’ll explore legal and ethical issues that arise in working with big data. In the final Capstone Project, developed in partnership with the digital internship platform Coursolve, you will apply your new skills to a real-world data science project. You will gain core skills in Python and R programming, Mapreduce and SQL. This program is organized into four courses: Data Manipulation at Scale - Systems and Algorithms, Practical Predictive Analytics - Models and Methods, Communicating Data Science Results, and the Data Science at Scale - Capstone Project.

Register today at https://fxo.co/83H0

Much career success, Lawrence Wilson - Artificial Intelligence Academy

Data Science Specialization - Johns Hopkins University

Colleagues, join over 270k professionals worldwide who are enrolled in this program. 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

Natural Language Processing with Python Certification Training – Accelerate your machine learning career

Software colleagues, Natural Language Processing with Python course will take you through the essentials of text processing all the way up to classifying texts using Machine Learning algorithms. You will learn various concepts such as Tokenization, Stemming, Lemmatization, POS tagging, Named Entity Recognition, Syntax Tree Parsing and so on using Python’s most famous NLTK package. Once you delve into NLP, you will learn to build your own text classifier using the Naïve Bayes algorithm. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language.  You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. You will gain high demand, marketable skills in: 1) Introduction to Text Mining and NLP, 2) Extracting, Cleaning and Pre-processing and Machine Learning, 3) Analyzing Sentence Structure, 3) Text Classification – I & II, and 4) In Class Project on Sentiment Classification.

Register today at: https://fxo.co/8m2j

Career success awaits you, Lawrence Wilson – Artificial Intelligence Academy

What are the Top 4 TensorFlow and PyTorch Training Programs to Accelerate your Python career in 2020?

Software colleagues, the dramatic growth in Python programming has boost the average annual salary for developers to $120k in the US according to DAXX (https://tinyurl.com/y6gb3dbd) while increasing the demand for compatible math libraries. TensorFlow was developed by Google Brain for internal use at Google use. It was then released under the Apache License 2.0 on November 9, 2015 A number of pieces of Deep Learning software are built on top of PyTorch, which reached a release launched in January 2020. Our top picks for TensorFlow training include: Deep Learning with TensorFlow: Application Training of Deep Neural Networks to Machine Learning (https://tinyurl.com/qoy6pvt) and Artificial Intelligence and Deep Learning with TensorFlow (https://fxo.co/8I4g). Our number 1 choice for PyTorch education is Machine Learning with PyTorch (https://tinyurl.com/szmb6cl). Finally, we highly recommend a hybrid training program entitled Deep Learning with TensorFlow, Keras, and PyTorch (https://tinyurl.com/yxxqzr9t). Now that PyTorch has reached a stable release we expect many more training programs to be launched this year.

Enroll today at the links above.

Much career success, Lawrence Wilson – Artificial Intelligence Academy