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. 

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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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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 

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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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Python 3 Programming Specialization (University of Michigan)

Colleagues, join the 182,662 developers enrolled in the Python 3 Programming Specialization where you will learn to write programs that query Internet APIs for data and extract useful information from them.  And you’ll be able to learn to use new modules and APIs on your own by reading the documentation. That will give you a great launch toward being an independent Python programmer.  Training modules include: 1) Python Basics - conditional execution and iteration as control structures, and strings and lists as data structures. You'll program an on-screen Turtle to draw pretty pictures. You'll also learn to draw reference diagrams as a way to reason about program executions, which will help to build up your debugging skills, 2) Python Functions, Files, and Dictionaries - dictionary data structure and user-defined functions. You’ll learn about local and global variables, optional and keyword parameter-passing, named functions and lambda expressions. You’ll also learn about Python’s sorted function and how to control the order in which it sorts by passing in another function as an input. For your final project, you’ll read in simulated social media data from a file, compute sentiment scores, and write out .csv files, 3) Data Collection and Processing with Python - fetch and process data from services on the Internet. It covers Python list comprehensions and provides opportunities to practice extracting from and processing deeply nested data. You'll also learn how to use the Python requests module to interact with REST APIs and what to look for in documentation of those APIs. For the final project, you will construct a “tag recommender” for the flickr photo sharing site, 4) Python Classes and Inheritance - classes, instances, and inheritance. You will learn how to use classes to represent data in concise and natural ways. You'll also learn how to override built-in methods and how to create "inherited" classes that reuse functionality. You'll also learn about how to design classes. Finally, you will be introduced to the good programming habit of writing automated tests for their own code. 

Enroll today (teams & execs welcome): https://tinyurl.com/28cruk7u 

 

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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, TensorFlow: Advanced Techniques is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. Access the TensorFlow List of Datasets and visit TensorFlow on GitHub.

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

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Spark, Ray and Python for Scalable Data Science

Colleagues, the global data science platform market was valued at $31B in 2020, and it is expected to reach $230B by 2026, registering a CAGR of 39.7 % during the forecast period according to Mordor Intelligence. The #10 recommendation on our Top 10 countdown is Spark, Ray and Python for Scalable Data Science from InformIT. Learn to integrate Python and distributed computing, scale data processing with Spark, conduct exploratory data analysis with PySpark, utilize parallel computing with Ray and implement machine learning and artificial intelligence applications with Ray. Skill-based training modules include: 1 - Introduction to Distributed Computing in Python, 2 - Scaling Data Processing with Spark, 3 - Exploratory Data Analysis with PySpark, 4 - Parallel Computing with Ray, and 5 - Scaling AI Applications with Ray. 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. Finally, the lesson finishes with an introduction to how Ray can enable you to both deploy machine learning models to production and monitoring.

Enroll today (teams & execs welcome): https://tinyurl.com/muhzvb8j 

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