Advanced R Programming for Productivity & Machine Learning

Dev colleagues, this Advanced R Programming course begins with reading XML data and some common data manipulation operations using various base R functions and packages like plyr, comparing the speed of in memory calculations. He then demonstrates more advanced techniques for accomplishing the same task such as data.table, dplyr, Rcpp and parallel computation for increased speed. You will be equipped in Basic Aggregation, plyr, dplyr, data.table, Rcpp, Parallel processing, Web Graphics, Network Analysis, Text Mining and Advanced Document Creation. The seven core skill-based training modules include: 1) Reading XML Data, 2) Faster Group Operations, 4) Rcpp for Faster Code, 5) Advanced Machine Learning, 6) Network Analysis, 7) Web Graphics, and 8) Easier Presentations and Documents with RMarkdown. RStudio has made great advancements in creating documents and presentations, making the whole process easier than it was even just a few months ago. This lesson discusses the very easy steps to generate HTML, PDF and Word documents and HTML presentations.

Download your complimentary AI-ML Certification Guide (2021): https://tinyurl.com/3y5seaxe 

Enroll today (individuals & teams welcome): https://tinyurl.com/e8f32s5c 

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

Deep Learning with TensorFlow: Applications of Deep Neural Networks to Machine Learning Tasks

AI-ML colleagues this Deep Learning with TensorFlow program equips you to understand the Deep Learning machine-learning approach 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. Paired with tips for overcoming common pitfalls and hands-on code run-throughs provided in Python-based Jupyter notebooks, this foundational knowledge empowers individuals with no previous understanding of neural networks to build powerful state-of-the-art Deep Learning models. The five skill-based training modules include: 1) Introduction to Deep Learning, 2) How Deep Learning Works, 3) Convolutional Networks, 4) TensorFlow Introduction, and 5) Improving Deep Networks.

Download your complimentary AI-ML Certification Guide (2021):

Enroll today (individuals & teams welcome): https://tinyurl.com/5dcwvz9x 

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

Feedforward Neural Networks (Training)

AI-ML colleagues, the Feedforward Neural Networks program will take you from the most basic concepts in neural networks to building and optimizing a complete neural network and using different tools to solve problems using Deep Neural Networks. Gain key skills in: Deep neural network, Activation Function and Types of Nonlinearities, Sigmoid Neuron Implementation, Forward Propagation Implementation, Parameters and Hyperparameters, and Neural Network using Keras. The ten skill-based training modules address: 1) Introduction and Overview, 2) Motivation behind Deep Learning, 3) A Simple Network, 4) Feed Forward Neural Network, 5) Backpropagation, 6) TensorFlow, 7) Improving the Neural Network, 8) Optimization, 9) Applications, and 10)  Summary and Conclusion. 

Download your complimentary AI-ML Certification Guide for 2021 here: https://tinyurl.com/rcdjsjnx 

Enroll today (individuals & teams welcome):https://tinyurl.com/5bnd8dj4 

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

Natural Language Processing with TensorFlow 2.0 Specialization

AI colleagues, the Machine Learning course and Deep Learning Specialization from Andrew Ng teach 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. Training modules include: 1) Sentiment in Text,, 2) Word Embeddings, 3) Sequence Models, and 4) Sequence models and Literature:  Taking everything that you've learned in training a neural network based on NLP, we thought it might be a bit of fun to turn the tables away from classification and use your knowledge for prediction. You will build a poetry generator trained with the lyrics from traditional Irish songs, and can be used to produce beautiful-sounding verse of its own!

Enroll today (individuals & teams): https://tinyurl.com/ylpafc6g 

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

Probabilistic Deep Learning with TensorFlow

DL colleagues, learn to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library.. Gain high-demand skills in Probabilistic Neural Networks, Deep Learning, Generative Models, Tensorflow and Probabilistic Programming Language (PRPL). The five training modules equip you in: 1) TensorFlow Distributions: Probabilistic modelling is a powerful and principled approach that provides a framework in which to take account of uncertainty in the data, 2) Probabilistic layers and Bayesian Neural Networks: Accounting for sources of uncertainty is an important aspect of the modelling process, especially for safety-critical applications such as medical diagnoses, 3) Bijectors and Normalising Flows: Normalising flows are a powerful class of generative models, that aim to model the underlying data distribution by transforming a simple base distribution through a series of bijective transformations., 4) Variational Autoencoders: Variational autoencoders are one of the most popular types of likelihood-based generative deep learning models. Two networks are jointly learned: an encoder or inference network, as well as a decoder or generative network, and 5) Capstone Project: Develop probabilistic deep learning models using tools and concepts from the TensorFlow Probability library such as Distribution objects, probabilistic layers, bijectors, and KL divergence optimisation. 

Download your complimentary AI Certification Guide for 2021 here: https://tinyurl.com/1l2soeh0 

Enroll today (individuals & teams): https://tinyurl.com/hjd51uc4 

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

Introduction to Machine Learning with PyTorch

ML colleagues, Zip Recruiter estimates Machine Learning Engineer salaries in the US average $130k.  Learn foundational machine learning techniques -- from data manipulation to unsupervised and supervised algorithms. Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Then, move on to exploring deep and unsupervised learning. At each step, get practical experience by applying your skills to code exercises and projects. First, Supervised Learning - a common class of methods for model construction (Project: Find Donor for CharityML). Second, Deep Learning - learn the foundations of neural network design and training in PyTorch (Project: Build an Image Classifier). And third, Unsupervised Learning - implement unsupervised learning methods for different kinds of problem domains (Project: Create Customer Segments). Take your ML career to new heights.

Enroll today (individuals & teams): https://fxo.co/BFWR 

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

Jupyter Notebooks for Data Science Analysis in Python

AI-ML colleagues, Jupyter Notebooks are a popular tool for learning and performing data science in Python (and other languages used in data science). This program will teach you about Project Jupyter and the Jupyter ecosystem and gets you up and running in the Jupyter Notebook environment. Together, we’ll build a data project in Python, and you’ll learn how to share this analysis in multiple formats, including presentation slides, web documents, and hosted platforms (great for colleagues who do not have Jupyter installed on their machines). In addition to learning and doing Python in Jupyter, you will also learn how to install and use other programming languages, such as R and Julia, in your Jupyter Notebook analysis. Skill-based training modules address: 1) Project Jupyter and the Jupyter Ecosystem with NUMFOCUS, 2)  Creating Data Science Analyses in the Jupyter Notebook using EDA and the R kernel, 3) Sharing Jupyter Notebooks with RISE, 4)  Exploring New Jupyter Projects In-Depth with Widgets, Binder and  BinderHub.

Enroll today (individuals & teams welcome): tinyurl.com/f6zsm5ni

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

Certified Deep Learning Engineer - Deep Learning with TensorFlow 2.0 Certification Training

AI colleagues, Indeed.com estimates average US salaries for Certified Deep Learning Engineer  at $166k+. This Deep Learning with TensorFlow 2.0 Certification Training is curated with the help of experienced industry professionals as per the latest requirements & demands. This course will help you master popular algorithms like CNN, RCNN, RNN, LSTM, RBM using the latest TensorFlow 2.0 package in Python. You will be working on various real-time projects like Emotion and Gender Detection, Auto Image Captioning using CNN and LSTM. Skill-based training modules include: : 1) Getting Started with TensorFlow 2.0, 2) Convolution Neural Network, 3) Regional CNN, 4) Boltzmann Machine & Autoencoder, 5) Generative Adversarial Network(GANEmotion and Gender Detection, 7) Introduction RNN and GRU, 8) LSTM,and  9) Auto Image Captioning Using CNN LSTM.

Enroll today (individuals & teams are welcome): https://fxo.co/AA8u 

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

Computer Science for Artificial Intelligence (Certificate Program)

AI colleagues, this professional certificate series from Harvard University extension  combines CS50’s legendary Introduction to Computer Science course with a new program that takes a deep dive into the concepts and algorithms at the foundation of modern artificial intelligence. Two skill-based modules comprise this program: 1) CS50's Introduction to Computer Science - 6–18 hours per week, for 12 weeks - An introduction to the intellectual enterprises of computer science and the art of programming, and 2) CS50's Introduction to Artificial Intelligence with Python - 10–30 hours per week, for 7 weeks - use machine learning in Python in this introductory course on artificial intelligence. Topics addressed encompass computer science and programming, graph search algorithms, reinforcement learning, machine learning, artificial intelligence principles, designing intelligent systems, and using AI in Python programs.

Enrol today (individual & teams are welcome): https://fxo.co/AiVo 

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

Statistics and Data Science - MicroMasters® Program

Data Science colleagues, Indeed estimates average base salaries for Data Scientists at  $122k+. This MicroMasters program will equip you in the foundations of data science, statistics, and machine learning along with big data analysis and make data-driven predictions through probabilistic modeling and statistical inference; identify and deploy appropriate modeling and methodologies in order to extract meaningful information for decision making. Finishing this MicroMasters program will prepare you for job titles such as: Data Scientist, Data Analyst, Business Intelligence Analyst, Systems Analyst, Data Engineer Core courses inc;lude: 1) Probability - The Science of Uncertainty and Data, 2) Fundamentals of Statistics, 3) Machine Learning with Python: from Linear Models to Deep Learning, and 4) Capstone Exam in Statistics and Data Science. Electives cover: 1) Data Analysis in Social Science—Assessing Your Knowledge, and 2) Data Analysis: Statistical Modeling and Computation in Applications. Learners must complete and successfully earn a verified certificate in four required courses and pass the virtually-proctored capstone exam.

Enroll today (individuals & teams are welcome): https://fxo.co/AiVW 

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