Top 3 Machine Learning Training Programs for 2023

Colleagues, GlassDoor estimate the average US salary for Machine Learning Engineers at $130,794. Keep your ML skillset up-to-date and earn top dollar for your high demand skills. Here are our top 3 training recommendations for a competitive advantage in 2023. First, is the Machine Learning Engineer program from Udacity. Learn the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker. The program offers an Introduction to Machine Learning, Developing Your First ML Workflow, Deep Learning Topics within Computer Vision and NLP, Operationalizing Machine Learning Projects on SageMaker and Capstone Project: Inventory Monitoring at Distribution Centers. Second is IBM’s Advanced Machine Learning and Signal Processing training. Access to valuable insights into Supervised and Unsupervised Machine Learning Models used by experts in many field relevant disciplines. We’ll learn about the fundamentals of Linear Algebra to understand how machine learning modes work. Then we introduce the most popular Machine Learning Frameworks for python: Scikit-Learn and SparkML. And third is the Machine Learning Engineering Career Track Program from Springboard. Deploy ML Algorithms and build your own portfolio. More than 50% of the Springboard curriculum is focused on production engineering skills. In this course, you'll design a machine learning/deep learning system, build a prototype and deploy a running application that can be accessed via API or web service.  

Enroll today (teams & execs welcome): 

• Machine Learning Engineer 

• Advanced Machine Learning and Signal Processing 

• Machine Learning Engineering Career Track Program

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2023: Top 3 R Programming Courses for Career Growth

Colleagues, ZipRecruiter estimates the average US salary for an R Developer is $123,850. Here are our top 3 training programs for developers seeking career and income growth. First up is R Programming. Learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Next is Programming for Data Science in R. Master the  fundamentals required for a career in data science. By the end of the program, you will be able to use R, SQL, Command Line, and Git. Training modules with hands-on labs include: 1) IIntroduction 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 R Programming - learn R programming fundamentals such as data structures, variables, loops, and functions. Learn to visualize data in the popular data visualization library ggplot2 (Project: Explore US Bikeshare Data), and 3) Introduction to Version Control - use version control and share your work with other people in the data science industry (Project: Post your work on Github). And third, Advanced R Programming. Gain skills in functional programming in R, robust error handling, object oriented programming, profiling and benchmarking, debugging, and proper design of functions. Upon completing this course you will be able to identify and abstract common data analysis tasks and to encapsulate them in user-facing functions. 

Enroll today (teams & exec are welcome):

• R Programming

• Programming for Data Science in R

• Advanced R Programming

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Data Structures, Algorithms and Machine Learning Optimization

Colleagues, the Data Structures, Algorithms, and Machine Learning Optimization equips you to to use "Big O" notation to characterize the time efficiency and space efficiency of a given algorithm, enabling you to select or devise the most sensible approach for tackling a particular machine learning problem with the hardware resources available to you, get acquainted with the entire range of the most widely-used Python data structures, including list-, dictionary-, tree-, and graph-based structures, develop a working understanding of all of the essential algorithms for working with data, including those for searching, sorting, hashing, and traversing, discover how the statistical and machine learning approaches to optimization differ, and why you would select one or the other for a given problem you're solving, understand exactly how the extremely versatile (stochastic) gradient descent optimization algorithm works and how to apply it and learn "fancy" optimizers that are available for advanced machine learning approaches (e.g., deep learning) and when you should consider using them. Training modules include: 1) Data Structures and Algorithms, 2) "Big O" Notation, 3) List-Based Data Structures, 4) Searching and Sorting, 5) Sets and Hashing, 6) Trees, 7) Graphs, 8) Machine Learning Optimization, and 9) Fancy Deep Learning Optimizers. 

Enroll today (teams & execs welcome): https://tinyurl.com/45xyvttt 

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Top 3 strategies for Machine Learning career success

Colleagues Fortune Business Insights projects total Machine Learning market growth through 2029 with a 38.8% CAGR .Glassdoor estimates the average salary for a Machine Learning Engineer at $122,963 USD. First, Get Certified: A high quality cert from a reputable vendor or professional association may boost your income by 5%-10%. Our top recommendation is Python Machine Learning Certification Training using Python that equips you with various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes, and Q-Learning. Next is 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. Finally is the Machine Learning Engineer program that teaches you 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, Get Published. Write a 1-2 page article on Best Practices or Tech Trends for Medium, LinkedIn Articles or Technology.org. Third, Get Connected. Subscribe to the Data School channel on YouTube (200k members). Join Reddit’s Machine Learning group (2.5m members. Then register for the Wolfram Machine Learning discussion forum.

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

• Python Machine Learning Certification Training using Python

• AWS Certified Machine Learning

• Machine Learning Engineer 

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Data Science Roadmapping - Unlocking value in Data-Driven Organizations

CIO & COOs, ScienceDirect’s article “Data Science Roadmapping: An Architectural Framework for Facilitating Transformation Towards a Data-Driven Organization” (January 2022) states that “Leveraging data science can enable businesses to exploit data for competitive advantage by generating valuable insights. However, many industries cannot effectively incorporate data science into their business processes, as there is no comprehensive approach that allows strategic planning for organization-wide data science efforts and data assets. Accordingly, this study explores the Data Science Roadmapping (DSR) to guide organizations in aligning their business strategies with data-related, technological, and organizational resources … The results indicate that the framework facilitates DSR initiatives by creating a comprehensive roadmap capturing strategy, data, technology, and organizational perspectives. “

Accordingly, the new Data Science - Career Transformation Guide (2022 v2) includes valuable resources that enable CIOs and COOs you to upskill your teams and adopt a DSR strategy - Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Communities and Networking forums. This guide focuses on Data Science, Big Data, Analytics, Pandas and Jupyter Notebooks along with the tools and technologies to perform these functions including Python, R, Probability and Statistics, Google BigQuery, SQL, Hadoop and Tableau. The Global Knowledge 2021 IT Salary Survey ranks Google Certified Professional Data Engineer $171,749 USD #1 out of all certifications. Indeed.com lists some 599,779+ Data Scientist positions in the United States alone. Our three-fold career advancement strategy is to Get Certified, Get Published and Get Connected. 

Review and enroll today (teams & execs are welcome). https://tinyurl.com/4y2ujjje 

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Python, R, TensorFlow & PyTorch - Career Transformation Guide

Dev colleagues, the new Python, R, TensorFlow and PyTorch  - Career Transformation Guide includes valuable information that enables you to accelerate your career growth and income potential - Career opportunities, Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Forums and Communities. The Certification and Training programs are categorized by Python, R, TensorFlow and PyTorch. The average salary for Python Developers in the US is $111,225 according to CareerFoundry. Salary.com reports the media salary for Python Developers across the US is $95,120 USD. For Python Developers with formal training and expertise in TensorFlow and/or PyTorch mathematical libraries AIA projects an additional 3%-5%+ positive income delta is quite realistic. Therefore, the Artificial Intelligence Academy highly recommends that Python Developers also acquire TensorFlow and/or PyTorch training and certification. Analytics Insight project demand for Python developers to rank #1 among all programming languages in 2023.

Review and enroll today (teams & execs are welcome): https://tinyurl.com/ynenk99m 

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Artificial Intelligence-Machine Learning-Deep Learning - Career Transformation Guide [2022 v2]

Colleagues, our updated Artificial Intelligence-Machine Learning-Deep Learning - Career Transformation Guide includes valuable information that enables you to accelerate your career growth and income potential - Career opportunities, Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Forums and Communities. The Certification and Training programs are categorized by Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing and Computer Vision. Glassdoor estimates the average salary for a Machine Learning Engineer at $131,001 USD. Indeed lists 2091  openings with an averMachine Learning Engineer age 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. SimplyHired now has 868,669 Computer Vision career listings alone. Seize the opportunity. Success awaits you!

Review and enroll today (teams & execs are welcome): https://tinyurl.com/59utvx58

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Machine Learning with Python for Everyone

Dev colleagues, the Machine Learning with Python for Everyone program turns  introductory machine learning concepts into concrete code using Python, Scikit-learn, and friends. Our focus is on stories, graphics, and code that build your understanding of machine learning; we minimize pure mathematics. You learn how to load and explore simple datasets; build, train, and perform basic learning evaluation for a few models; compare the resource usage of different models in code snippets and scripts; and briefly explore some of the software and mathematics behind these techniques. Part I - Software, Mathematics, Classification, Regression, Part II - Evaluating Learning Performance, Classifiers, Regressors, and Part  III - Classification Methods, Regression Methods, Manual Feature Engineering, Hyperparameters and Pipelines. Build and apply simple classification and regression models, evaluate learning performance with train-test splits, assess learning performance with metrics tailored to classification and regression, and examine the resource usage of your learning models.

Enroll today (teams & execs welcome): https://tinyurl.com/wwajjs2j 

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Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (share with your team) 

Data Science - Career Transformation Guide [2022 v2]

Colleagues, the new Data Science - Career Transformation Guide includes valuable resources that enable you to accelerate your career growth and income potential - Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Communities and Networking forums. This guide focuses on Data Science, Big Data, Analytics, Pandas and Jupyter Notebooks along with the tools and technologies to perform these functions including Python, R, Probability and Statistics, Google BigQuery, SQL, Hadoop and Tableau. The Global Knowledge 2021 IT Salary Survey ranks Google Certified Professional Data Engineer $171,749 USD #1 out of all certifications. Indeed.com lists some 599,779+ Data Scientist positions in the United States alone. Our three-fold career advancement strategy is to Get Certified, Get Published and Get Connected. Success awaits you.

Review and enroll today (teams & execs are welcome): https://tinyurl.com/5bxa5t96 

Download your free Data Science - Career Transformation Guide. 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (share with your team) 

Machine Learning in Trading and Finance

Colleagues, the Machine Learning in Trading and Finance program from the New York Institute of Finance and Google Cloud will equip you in Quantitative trading, pairs trading, and momentum trading. You will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it. You should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas.By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading.  This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.

Enroll today (teams & execs are welcome):  https://tinyurl.com/2p993u5m 

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