Data Science with Python and R

Colleagues, the Global Knowledge 2021 IT Salary Survey ranks Google Certified Professional Data Engineer $171,749 USD #1 out of all certifications. This Data Science with Python and R training program is tailored to beginner Data Scientists seeking to use Python or R for data science. This course includes fundamentals of data preparation, data analysis, data visualization, machine learning, and interactive data science applications. Students will learn how to build predictive models and how to create interactive visual applications for their line of business using the Anaconda platform. This course will introduce data scientists to using Python and R for building on an ecosystem of hundreds of high performance open source tools. Training modules address: 1) Open Data Science for Everyone, 2) Background Concepts for Open Data Science, 3) Data Wrangling with Pandas, 4) Anaconda Platform Overview, 5) Creating Interactive Visualizations with Bokeh, 6) Conda Package Management, 7) Data Processing and Visualization in R, 8) Excel and Python with Anaconda Fusion, 9) Excel and Python with Anaconda Fusion, 10) Databases and Distributed Data with Mosaic, 11) Distributed and Parallel Computing with Dask. 

Enroll today (teams & execs welcome): https://tinyurl.com/2p83vcek 

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Data Structures, Algorithms and Machine Learning Optimization

Colleagues, the Data Structures, Algorithms, and Machine Learning Optimization program provides you with a functional, hands-on understanding of the essential computer science for machine learning applications. Learn "big O" notation to characterize the time efficiency and space efficiency of a given algorithm,  use Python data structures, including list-, dictionary-, tree-, and graph-based structures, understand the essential algorithms for working with data, including those for searching, sorting, hashing, and traversing, implement statistical and machine learning approaches to optimization differ, and why you would select one or the other for a given problem you're solving, grasp versatile (stochastic) gradient descent optimization algorithm works, and familiarize yourself with the "fancy" optimizers that are available for advanced machine learning approaches. Skill-based training modules cover: 1) Orientation to Data Structures and Algorithms - Machine Learning Foundations Series, A Brief History of Data and Algorithms, and their Applications to Machine Learning; 2) "Big O" Notation - Constant, Linear and Polynomial  Time, Common Runtimes, Best versus Worst Case scenarios; 3) List-Based Data Structures - Lists, Arrays, Linked Lists, Doubly-Linked Lists, Stacks, Queues, Deques; 4) Searching and Sorting - Binary Search, Bubble-Merge-Quick Sorts; 5) Sets and Hashing - Maps and Dictionaries, Sets, Hash Functions, Collisions, Load Factor, Hash Maps, String Keys, Hashing in ML; 6) Trees - Decision Trees, Random Forests, XGBoost: Gradient-Boosted Trees; 7) Graphs - Directed versus Undirected Graphs, DAGs: Directed Acyclic Graphs, Pandas DataFrames; 8) Machine Learning Optimization - Statistics versus Machine Learning - Objective Functions, Mean Absolute Error, Mean Squared Error, Minimizing Cost with Gradient Descent, Gradient Descent from Scratch with PyTorch, Critical Points, Stochastic Gradient Descent, Learning Rate Scheduling, Maximizing Reward with Gradient Ascent; and 9) Fancy Deep Learning Optimizers - Jacobian Matrices, Second-Order Optimization and Hessians, Momentum, and Adaptive Optimizers.

Enroll today (teams & execs welcome): https://tinyurl.com/yc2dfb8f 

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Deep Reinforcement Learning

Colleagues, the Deep Reinforcement Learning training program will equip you with skills that are powering amazing advances in AI. Then start applying these to applications like video games and robotics. Learn cutting-edge deep reinforcement learning algorithms—from Deep Q-Networks (DQN) to Deep Deterministic Policy Gradients (DDPG). Apply these concepts to train agents to walk, drive, or perform other complex tasks, and build a robust portfolio of deep reinforcement learning projects. Students should have experience with Python, probability, machine learning, and deep learning. Skill-based training modules include: 1) Foundations of Reinforcement Learning - master the fundamentals of reinforcement learning by writing your own implementations of many classical solution methods, 2) Value-Based Methods - apply deep learning architectures to reinforcement learning tasks. Train your own agent that navigates a virtual world from sensory data (Project: Navigation), 3) Policy-Based Methods - learn the theory behind evolutionary algorithms and policy-gradient methods. Design your own algorithm to train a simulated robotic arm to reach target locations (Project: Continuous Control), 4) Multi-Agent Reinforcement Learning - apply reinforcement learning methods to applications that involve multiple, interacting agents. These techniques are used in a variety of applications, such as the coordination of autonomous vehicles (Project: Collaboration and Competition).

Enroll today (teams & execs welcome): https://tinyurl.com/4d27236r 

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

Data Science with Python and R

Colleagues, the Global Knowledge 2021 IT Salary Survey ranks Google Certified Professional Data Engineer $171,749 USD #1 out of all certifications. This Data Science with Python and R training program is tailored to beginner Data Scientists seeking to use Python or R for data science. This course includes fundamentals of data preparation, data analysis, data visualization, machine learning, and interactive data science applications. Students will learn how to build predictive models and how to create interactive visual applications for their line of business using the Anaconda platform. This course will introduce data scientists to using Python and R for building on an ecosystem of hundreds of high performance open source tools. Training modules address: 1) Open Data Science for Everyone, 2) Background Concepts for Open Data Science, 3) Data Wrangling with Pandas, 4) Anaconda Platform Overview, 5) Creating Interactive Visualizations with Bokeh, 6) Conda Package Management, 7) Data Processing and Visualization in R, 8) Excel and Python with Anaconda Fusion, 9) Excel and Python with Anaconda Fusion, 10) Databases and Distributed Data with Mosaic, 11) Distributed and Parallel Computing with Dask. 

Enroll today (teams & execs welcome): https://tinyurl.com/2p83vcek 

Down your complimentary Data Science - Career Transformation Guide.

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

Spark, Ray, and Python for Scalable Data Science

Colleagues, according to Salary.com the average Data Scientist salary in the United States is $136,309. The Spark, Ray, and Python for Scalable Data Science program equips you to scale machine learning and artificial intelligence projects using Python, Spark, and Ray. Learn to integrate Python and distributed computing, scale data processing with Spark, conduct exploratory data analysis with PySpark, utilize parallel computing with Ray and scale machine learning and artificial intelligence applications with Ray. Skill-based training modules include: 1) Introduction to Distributed Computing in Python - you get some experience with one of Spark's primary data structures, the resilient distributed dataset (RDD). Next is key-value pairs and how Spark does operations on them similar to MapReduce. The lesson finishes up with a bit of Spark internals and the overall Spark application lifecycle, 2) Exploratory Data Analysis with PySpark - large data science workflow centered around natural language processing (NLP). He starts off with a general introduction to exploratory data analysis (EDA), followed by a quick tour of Jupyter notebooks. Next he discusses how to do EDA with Spark at scale, and then he shows you how to create statistics and data visualizations to summarize data sets. Finally, he tackles the NLP example, showing you how to transform a large corpus of text into numerical representation suitable for machine learning, 3) Parallel Computing with Ray - Ray programming API, with Jonathan comparing the similarities and differences between the Ray and Spark APIs. You learn how you can distribute functions with Ray, and 4) Scaling AI Applications with Ray - scale up machine learning and artificial intelligence applications with Python. The lesson starts with the general model training and evaluation process in Python. Then it turns to how Ray enables you to scale both the evaluation and tuning of our models. You see how Ray makes possible very efficient hyperparameter tuning. You also see how, once you have a trained model, Ray can serve predictions from your machine learning model.

Enroll today (teams & execs welcome): https://tinyurl.com/4pydnt23 

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Data Science for Business Leaders

Colleagues, the Data Science for Business Leaders program equips you to master the strategic decision-making skills for the people, platforms, and processes required to leverage the power of Data Science in your business. This course provides business leaders and managers with strategies and guidelines for how best to solve the human capital, technological, and management challenges of building data science into the business. Students will gain skills in identifying opportunities for data science across many functional areas of the business, as well as learn the tools to prioritize and execute on those opportunities as part of a data science initiative. Enrollees should have exposure to statistics and probability, and business decision-making in an IT or technical environment. Training modules include: 1) Introduction to Data Science - learn exactly what Data Science is, who Data Scientists are, and what's possible through Data Science, 2) Business Case for Data Science - create a data science strategy isn’t a standalone activity; it must be driven by a business's overarching operations and strategy. This course will cover how to articulate a business’s strategic objectives and identify opportunities for data science-based transformation, a critical starting point for any data strategy, 3) Human Capital of Data Science - the human capital component of Data Science is critical to delivering on a data science strategy. Learn how to recruit, hire, and train for a Data Science organization, and how to structure that organization in order to deliver value to the business. Asses ways to leverage data and data science to foster a data-driven culture throughout the business, 4) Data and Machine Learning Infrastructure Strategy - depend on the types of data to be leveraged for Data Science, the form and magnitude of that data, the types of data science models that a business plans to create, and the overall scale of operations represented by those data science models. This lesson investigates the parameters that must be considered both in creating a Data Architecture Strategy and in building a Machine Learning Architecture to support Data Science initiatives. The Capstone Project is “Building a 100-Day Data Plan” - create a Data Science strategy that drives transformation in the business during your first 100 days.

Enroll today (execs & teams welcome): https://fxo.co/E6Cx 

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Data Science with Python

Colleagues, Data Science professionals earn an average $136,309 per year according to Salary.com. This Data Science with Python program prepares you for a data science career by learning the fundamental data programming tools: Python, SQL, command line, and Git. Training modules - each with a hands-on project - include: 1) Introduction to SQL - learn SQL fundamentals such as JOINs, Aggregations, and Subqueries. Learn how to use SQL to answer complex business problems (Project: Investigate a Database), 2) Introduction to Python Programming - learn data structures, variables, loops, and functions. Learn to work with data using libraries like NumPy and Pandas (Project: Explore US Bikeshare Data), and 3) Introduction to Version Control - use version control and share your work with other people in the data. This program also includes real-world projects from industry experts - immersive content built in partnership with top tier companies, you’ll master the tech skills companies want, technical mentor support - mentors guide your learning and are focused on answering your questions, motivating you and keeping you on track, and 3) career services - access Github portfolio review and LinkedIn profile optimization to help you advance your career and land a high-paying role.

Enroll today (teams & execs welcome): https://tinyurl.com/2p8v72jr 

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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). 

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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