Data Science Certification

Colleagues, the Global Knowledge 2021 IT Salary Survey ranks Google Certified Professional Data Engineer #1 out of all certifications with an average salary of $171,749. The program prepares you to become a Certified Data Scientist. Learn data science from industry experts at Harvard, Columbia, Cisco, Apple and Google. Training modules include: 1) Probability and Statistics for Data Science with R: Harvard faculty teaches you how to apply statistical methods to explore, summarize, make inferences from complex data and develop quantitative models to assist business decision making - instructional component, R tutorial videos, and exercises to reinforce concepts and give you an opportunity to see statistics in action, Michael Parzen, faculty member at Harvard and teaches one of the most popular classes. Kaitlin Hagan is a post-doctoral fellow at Brigham and Women's Hospital and has won numerous teaching awards and citations for her work; 2) Data Wrangling in R: Real-world data preparation for further analysis using R - get your data into R efficiently and polish it up so that it is as good as it can be, the instructor is the founder of Analytics Incubation Center at Cisco and has 15 years of analytics development experience; 3) Econometric Analysis: Methods and Applications: Quantitative and econometric analysis focused on practical applications that are relevant in fields such as economics, finance, public policy, business, and marketing, Alan Yang, is a faculty member at the Department of International and Public Affairs at Columbia University where he teaches courses in Introductory Statistics, Econometrics, and Quantitative Analysis in Program Evaluation and Causal Inference; 4) Classification Models: Online self-paced course with capstone project  - nstructor is a lead data scientist at one of the largest software companies in the world, author of a best-seller and an adjunct professor at University of Toronto; and 5) Clustering and Association Rule Mining: Learn Clustering methods and Association Rule Mining Techniques - Cluster Analysis and study most popular set of Clustering algorithms with end-to-end examples in R, the instructor is a Machine Learning Scientist with 10+ years of hands-on experience in predictive analytics and data science research at leading consulting, captive and R&D organizations.

Enroll today (teams & execs welcome): https://tinyurl.com/4dv98nsa 

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Top 5 Data Science Certification & Training Programs

Colleagues, the Global Knowledge 2021 IT Salary Survey ranks Google Certified Professional Data Engineer $171,749 USD #1 out of all certifications. They analyze information to gain insight into business outcomes, build statistical models to support decision-making and create machine learning models to automate and simplify key business processes. Our first pick - no surprise here - is Preparing for the Google Cloud Certification: Cloud Data Engineer Professional Certificate program - Google Cloud Big Data and Machine Learning Fundamentals, Modernizing Data Lakes and Data Warehouses with Google Cloud,Building Batch Data Pipelines on GCP, Building Resilient Streaming Analytics Systems on Google Cloud, Smart Analytics, Machine Learning, and AI on GCP, and Preparing for the Google Cloud Professional Data Engineer Exam. Second is Advanced Data Science with IBM - massive parallel  data processing, data exploration and visualization, and advanced  machine learning and deep learning, massive parallel  data processing, data exploration and visualization, and advanced  machine learning & deep learning. You'll understand the  mathematical foundations behind all machine learning & deep learning  algorithms. Next, Become a Data Architect: Data Architecture Foundations - learn to design a data model, normalize data, and create a professional ERD. Finally, you will take everything you learned and create a physical database using PostGreSQL (Project: Design and HR Database); Designing Data Systems, Data Lake design patterns and how to enable transactional capabilities in a Data Lake (Project: Design an Enterprise Data Lake System); Data Governance - Data Management Architectures, as well as the golden record creation and master data governance processes (Project: Data Governance at Sneakerpeak). Fourth, Advanced Predictive Modeling in R Certification: Ordinary Least square regression, advanced regression, imputation, dimensionality reduction, correlation and linear regression analysis, Covariance & Correlation, Central Limit Theorem, Z Score, Normal Distributions and  Hypothesis Testing.. And finally.Big Data Hadoop Certification: Hadoop Ecosystem and Architecture, HDFS, Anatomy of File Read and Write. Ecosystem tools including HDFS, YARN, MapReduce, Hive and Pig.  Use HDFS, YARN, MapReduce, Hive, and Pig. Throughout this online instructor-led live Big Data Hadoop certification training, you will be working on real-life industry use cases in Retail, Social Media, Aviation, Tourism, and Finance domains using Edureka's Cloud Lab

Enroll in one or more programs today (teams & execs welcome). 

Down your complimentary Data Science - Career Transformation Guide.

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Top 3 Machine Learning training programs for career growth

Colleagues Fortune Business Insights projects total Machine Learning market growth through 2029 at CAGR 38.8%. .Glassdoor estimates the average salary for a Machine Learning Engineer at $131,001 USD. Indeed lists 2091  openings with an average Machine Learning Engineer nationwide salary of $131,276 USD. The San Francisco Bay Area is the high-end of the salary range at $193,485 with Eden Prairie, Minnesota at $106,780. ZipRecruiter calculates the average US Machine Learning Engineer salary at $130,530. Our first pick is the Machine Learning Engineer - learn the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker, Deep Learning Topics within Computer Vision and NLP, Developing Your First ML Workflow, Operationalizing Machine Learning Projects, and a Capstone Project - Inventory Monitoring at Distribution Centers, Second, the Machine Learning with PyTorch Open Source Torch Library - machine learning, and for deep learning specifically, are presented with an eye toward their comparison to PyTorch,  scikit-learn library, similarity between PyTorch tensors and the arrays in NumPy or other vectorized numeric libraries,clustering with PyTorch, image classifiers, And third, AWS Certified Machine Learning - AWS Machine Learning-Specialty (ML-S) Certification exam,  AWS Exploratory Data Analysis covers topics including data visualization, descriptive statistics, and dimension reduction and includes information on relevant AWS services, Machine Learning Modeling.  A final recommendation is the Machine Learning with Mahout Certification: - learn Machine Learning fundamentals, Apache Mahout Basics, History of Mahout, Supervised and Unsupervised Learning techniques, Mahout, Hadoop and Introduction to Clustering, Classification..

Enroll  in one or more programs today (teams & execs welcome). 

And download your complimentary AI-ML-DL - Career Transformation Guide.

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

Graphic source: Fortune Business Insights

Top 3 TensorFlow Training & Certification Recommendations

Colleagues, the demand for TensorFlow trained and certified developers soars as growth in Machine Learning, Data Science and NLP continues to accelerate. The average US salary for a TensorFlow trained developer is $148,508 according to ZipRecruiter. skill level, location and years of experience. Our first pick is Deep Learning with TensorFlow & PyTorch Deep Learning and Artificial Intelligence, TensorFlow Playground, weight initialization, unstable gradients, batch normalization, Convolutional Neural Networks, Keras, PyTorch. Next is Natural Language Processing with Python Certification NLP and Python Programming - Tokenization, Stemming, Lemmatization, POS tagging, Named Entity Recognition, Syntax Tree Parsing and so on using Python’s most famous NLTK package. And third, the AI TensorFlow Developer Professional Certificate equips developers to build scalable AI-powered applications with TensorFlow. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects.

Access the TensorFlow List of Datasets and visit TensorFlow on GitHub.

Download your complimentary Python, TensorFlow & PyTorch - Career Transformation Guide.

Enroll in one or more programs today (teams & execs welcome). 

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

Top 3 Python for Data Science training programs

Dev colleagues, the average salary for a Python developer is $111,225 in the US according to Salary Expert. Here are 3 top-rated programs for career and income growth. First, Applied Data Science with Python from the University of  Michigan program has over 328k students enrolled. Second is the Statistics with Python training by Code Academy that focuses on mean, median, mode, standard deviation, and variance of different datasets. Not only will you learn how to calculate these statistics, but you will learn how to interpret them. By getting an understanding of what these statistics represent, you will be able to better describe your own datasets. And third, Data Science: K-Means Clustering in Python explains the key concepts of data clustering, Demonstrate understanding of the key constructs and features of the Python language, Implement in Python the principle steps of the K-means algorithm while Designing and executing a whole data clustering workflow and interpret the outputs. Gain high-demand data science skills in K-Means Clustering, Machine Learning and Programming in Python from the University of London.

Enroll today in one or more programs (teams & execs welcome).

Download your complimentary Python - Career Transformation Guide.

View the Developer’s Guide from the Python Software Foundation.

Career success awaits you, Lawrence Wilson -  Artificial Intelligence Academy (subscribe)

Deep Learning for Natural Language Processing - Applications of Deep Neural Networks to Machine Learning Tasks

Colleagues, the Deep Learning for Natural Language Processing  - Applications of Deep Neural Networks to Machine Learning program bring intuitive explanations of essential theory to life with interactive, hands-on Jupyter notebook demos. Examples feature Python and Keras, the high-level API for TensorFlow. Training modules include:  1) The Power and Elegance of Deep Learning for NLP -  linguistics section that introduces the elements of natural language and breaks down how these elements are represented both by Deep Learning and by traditional machine learning approaches. This is followed up with a tantalizing overview of the broad natural language applications in which Deep Learning has emerged as state-of-the-art. The lesson then reviews how to run the code in these LiveLessons on your own machine, as well as the foundational Deep Learning theory that is essential for building an NLP specialization upon. The lesson wraps up by taking a sneak peek at the capabilities you’ll develop over the course of all five lessons, 2) Word Vectors - word vectors are as well as how the beautiful word2vec algorithm creates them. Subsequently, the lesson arms you with a rich set of natural language data sets that you can train powerful Deep Learning models, and then swiftly moves along to leveraging those data to generate word vectors of your own, 3) Modeling Natural Language Data - calculate a concise and broadly useful summary metric called the Area Under the Curve of the Receiver Operator Characteristic. Calculate that summary metric in practice by building and evaluating a dense neural network for classifying documents, and add convolutional layers into your deep neural network as well, 4) Recurrent Neural Networks - essential RNN theory, a Deep Learning family that’s ideally suited to handling data that occur in a sequence like languages, apply this theory by incorporating an RNN into your document classification model, and high-level theoretical overview of especially powerful RNN variants—the Long Short-Term Memory Unit and the Gated Recurrent Unit,before incorporating these into your Deep Learning models as well, and 5) Advanced Models - LSTM, namely the Bi-Directional and Stacked varieties. - non-sequential network architectures—where instead of only stacking neural layers on top of each other as we’ve always done and run layers side-by-side in parallel.

Enroll today (eams & execs welcome):  https://tinyurl.com/yks4wu2h 

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

Deep Learning (Training)

Colleagues, the Deep Learning training program will equip you to build and apply your own deep neural networks to challenges like image classification and generation, time-series prediction, and model deployment. Become an expert in neural networks, and learn to implement them using the deep learning framework PyTorch. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models accessible from a website. Get your first taste of deep learning by applying style transfer to your own images, and gain experience using development tools such as Anaconda and Jupyter notebooks. Skill-based training modules include: 1) Neural Networks - build your first network with Python and NumPy. Use the modern deep learning framework PyTorch to build multi-layer neural networks, and analyze real data., 2) Convolutional Neural Networks - build and use them to classify images (faces, melanomas, etc.) based on patterns and objects that appear in them. Use these networks to learn data compression and image denoising., 3) Recurrent Neural Networks - build RNNs and long short-term memory networks with PyTorch; perform sentiment analysis and use recurrent networks to generate new text from TV scripts, 4) Generative Adversarial Network - use GANs to generate realistic images, with Ian Goodfellow, the inventor of GANs, and Jun-Yan Zhu, the creator of CycleGANs, and 5) Deploying a Sentiment Analysis Model - train and deploy your own PyTorch sentiment analysis model. Deployment gives you the ability to use a trained model to analyze new user input. Build a model, deploy it, and create a gateway for accessing it from a website (Project: Deploying a Sentiment Analysis Model). Knowledge of Python, NumPy, pandas, calculus and linear algebra is recommended.

Enroll today (teams & execs welcome): https://tinyurl.com/yj8zehht 

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

Machine Learning Engineer for Microsoft Azure ($131k average salary)

Colleagues, the Machine Learning Engineer for Microsoft Azure training program will strengthen your machine learning skills and build practical experience by training, validating, and evaluating models using Azure Machine Learning. Learn by building and deploying sophisticated machine learning solutions using popular open source tools and frameworks, and gaining practical experience running complex machine learning tasks using the built-in Azure labs accessible inside the Udacity classroom. According to Indeed.com average base salaries for Machine  Learning Engineers is $131,099. Prior experience with Python, Machine Learning, and Statistics is recommended. Skill-based training modules - each with hands-on labs - include: 1) Using Azure Machine Learning - learn to configure machine learning pipelines in Azure, identify use cases for Automated Machine Learning, and use the Azure ML SDK to design, create, and manage machine learning pipelines in Azure (Project: Optimizing and ML Pipeline in Azure); 2) Machine Learning Operations - key concepts of operationalizing machine learning, from selecting the appropriate targets for deploying models, to enabling Application Insights, identifying problems in logs, and harnessing the power of Azure’s Pipelines. All these concepts are part of core DevOps pillars that will allow you to demonstrate solid skills for shipping machine learning models into production (Project: Operationalizing Machine Learning); and 3) Capstone Project - use Azure’s Automated ML and HyperDrive to solve a task. Finally, you will have to deploy the model as a web service and test the model endpoint (Project: Training and Building a Machine Learning model in Microsoft Azure).

Enroll today (eams & execs welcome): https://tinyurl.com/2p98sk7a 

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

Sequence Models for Natural Language Processing (Google Cloud)

Colleagues, the Sequence Models for Natural Language Processing program from Google Cloud is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length. Acquire high-demand skills to Predict future values of a time-series, Classify free form text, Address time-series and text problems with recurrent neural networks, Choose between RNNs/LSTMs and simpler models, and Train and reuse word embeddings in text problems. You will get hands-on practice building and optimizing your own text classification and sequence models on a variety of public datasets in the labs we’ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow. Training modules address: 1) Working with Sequences - what a sequence is, see how you can prepare sequence data for modeling, and be introduced to some classical approaches to sequence modeling and practice applying them, 2) Recurrent Neural Networks - how they address the variable-length sequence problem, explain how our traditional optimization procedure applies to RNNs, and review the limits of what RNNs can and cannot represent, 3) Dealing with Longer Sequences - learn about LSTMs, Deep RNNs, working with real world data, 4) Text Classification - examine different ways of working with text and how to create your own text classification models, 5) Reusable Embeddings - labeled data for our classification models is expensive and precious. Here we will address how we can reuse pre-trained embeddings to make our models with TensorFlow Hub, 6) Encoder-Decoder Models - sequence-to-sequence model called the encoder-decoder network to solve tasks, such as Machine Translation, Text Summarization and Question Answering, and 6) Summary - review what you have learned so far about sequence modeling for time-series and natural language data.

Enroll today (eams & execs welcome): https://tinyurl.com/2p9yyx3v 

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

Convolutional Neural Networks in TensorFlow

Colleagues, join the 109k developers enrolled in the Convolutional Neural Networks in TensorFlow training program.  If you want to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. 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. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization. Gain high demand skills in Inductive Transfer, Augmentation, Dropouts, Machine Learning and Tensorflow. Training modules include: 1) Exploring a Large Dataset - basic image classification, and you learned a little bit about Convolutional Neural Networks (ConvNets). In this course you'll go deeper into using ConvNets will real-world data, and learn about techniques that you can use to improve your ConvNet performance, particularly when doing image classification!In Week 1, this week, you'll get started by looking at a much larger dataset than you've been using thus far: The Cats and Dogs dataset which had been a Kaggle Challenge in image classification, 2) Augmentation: A technique to avoid overfitting - namely that your model is very good at classifying what it is trained for, but not so good at classifying things that it hasn't seen. In order to generalize your model more effectively, you will of course need a greater breadth of samples to train it on. That's not always possible, but a nice potential shortcut to this is Image Augmentation, where you tweak the training set to potentially increase the diversity of subjects it covers, 3) Transfer Learning - where people with models trained on large datasets train them, so that you can either use them directly, or, you can use the features that they have learned and apply them to your scenario, 4) Multiclass Classifications - when moving beyond binary into Categorical classification there are some coding considerations you need to take into account.

Enroll today (teams & execs welcome): https://tinyurl.com/yckpmbjh 

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