Top 10 Machine LearningCertification & Training Programs (#9)

Colleagues, according to Glassdoor the average salary for a Machine Learning Engineer is $123,524. Number 9 in our Top 10 Countdown is the AWS Machine Learning Engineer program from Udacity. Master the skills necessary to become a successful ML engineer. The skill-based training modules - each with a hands-on project - include: 1) Introduction to Machine Learning - begin by using SageMaker Studio to perform exploratory data analysis. Know how and when to apply the basic concepts of machine learning to real world scenarios. Create machine learning workflows, starting with data cleaning and feature engineering, to evaluation and hyperparameter tuning. Finally, you'll build new ML workflows with highly sophisticated models such as XGBoost and AutoGluon (Project: Predict Bike Sharing Demand with AutoGluon); 2) Developing Your First ML Workflow - create general machine learning workflows on AWS. You’ll begin with an introduction to the general principles of machine learning engineering. From there, you’ll learn the fundamentals of SageMaker to train, deploy, and evaluate a model. Following that, you’ll learn how to create a machine learning workflow on AWS utilizing tools like Lambda and Step Functions. Finally, you’ll learn how to monitor machine learning workflows with services like Model Monitor and Feature Store. With all this, you’ll have all the information you need to create an end-to-end machine learning pipeline (Project: Build an ML Workflow on SageMaker); 3) Deep Learning Topics within Computer Vision and NLP - train, finetune, and deploy deep learning models using Amazon SageMaker. You’ll begin by learning what deep learning is, where it is used, and which tools are used by deep learning engineers. Next we will learn about artificial neurons and neural networks and how to train them. After that we will learn about advanced neural network architectures like Convolutional Neural Networks and BERT, as well as how to finetune them for specific tasks (Project: Image Classification using AWS SageMaker); 4) Operationalizing Machine Learning Projects on SageMaker - deploying professional machine learning projects on SageMaker. It also covers security applications. You will learn how to maximize output while decreasing costs and how to work with especially large datasets (Project: Operationalizing an AWS ML Project); and 5) Capstone Project: Inventory Monitoring at Distribution Centers - to build this project, students will have to use AWS Sagemaker and good machine learning engineering practices to fetch data from a database, preprocess it and then train a machine learning model. 

Enroll today (teams & execs welcome): https://tinyurl.com/43cfpu39 

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Data Science - Top 10 Countdown of Certification & Training Programs (#7)

Colleagues, after building your data wrangling and Python skills, our #7 recommendations on our Top 10 Countdown is Data Science Fundamentals Part 2 - Machine Learning and Statistical Analysis. This intermediate-level program will equip you in how to get up and running with a Python data science environment, the basics of the data science process and what each step entails, how (and why) to perform exploratory data analysis in Python with the pandas library, the theory of statistical estimation to make inferences from your data and test hypotheses, the fundamentals of probability and how to use scipy to work with distributions in Python, how to build and evaluate machine learning models with scikit-learn, the basics of data visualization and how to communicate your results effectively and the importance of creating reproducible analyses and how to share them effectively. Training modules include: 1) Exploring Data–Analysis and Visualization, 2) Making Inferences–Statistical Estimation and Evaluation, 3) Statistical Modeling and Machine Learning. The program concludes by discussing the differences between and nuances of statistics, modeling, and machine learning. I provide an overview of the various types of models and algorithms used for machine learning and introduce how to leverage scikit-learn–a robust machine learning library in Python–to make predictions.

Enroll today (teams & execs welcome): https://tinyurl.com/2rhu873h 

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Data Science - Top 10 Countdown of Certification & Training Programs (#8)

Colleagues #8 on our Top 10 Countdown of data science programs is Data Science Fundamentals Part 1 - Learning Basic Concepts, Data Wrangling, and Databases with Python. It focuses on the fundamentals of acquiring, parsing, validating, and wrangling data with Python and its associated ecosystem of libraries. After an introduction to Data Science as a field and a primer on the Python programming language, you walk through the data science process by building a simple recommendation system. After this introduction, you dive deeper into each of the specific steps involved in the first half of the data science process–mainly how to acquire, transform, and store data (often referred to as an ETL pipeline). You learn how to download data that is openly accessible on the Internet by working with APIs and websites, and how to parse this XML and JSON data. With this structured data, you learn how to build data models, store and query data, and work with relational databases. Along the way, you learn the fundamentals of programming with Python (including object-oriented programming and the standard library) as well as the best practices of building sustainable data science applications. Skill-based training modules address: 1: Introduction to Data Science with Python, 2: The Data Science Process–Building Your First Application, 3: Acquiring Data–Sources and Methods, 4: Adding Structure–Parsing Data and Data Models, 5: Storing Data–Persistence with Relational Databases, and 6: Validating Data–Provenance and Quality Control. 

Enroll today (teams & execs welcome): https://tinyurl.com/bc9k764e 

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Top 10 TensorFlow Certification & Training Programs (#10)

Colleagues, according to ZipRecruiter, the average salary for TensorFlow Developers is $132,215. Number 10 on our countdown is the Deep Learning with Tensorflow, Keras and PyTorch program. s an introduction to that brings the revolutionary machine-learning approach to life with interactive demos from the most popular deep learning library, TensorFlow, and its high-level API, Keras, as well as the hot new library PyTorch. 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. Learn how to: Build deep learning models in all the major libraries: TensorFlow, Keras, and PyTorch, Understand the language and theory of artificial neural networks, Excel in machine vision, natural language processing, and reinforcement learning, Create algorithms with state-of-the-art performance by fine-tuning model architectures, and Complete your own Deep Learning projects. Core skills you will gain include: 1) Deep Learning and Artificial Intelligence, 2) How Deep Learning Works, 3) High-Performance Deep Learning Networks, 4) Convolutional Neural Networks, and 5) Moving Forward with Your Own Deep Learning Projects (Capstone Project).

Enroll today (teams & execs welcome): https://tinyurl.com/3ph28ctz 

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Top 10 Python Developer Certification & Training Programs (#8)

Colleagues, according to ZipRecruiter the average salary of a  Full Stack Python Developer is $121,111. Number 8 on our countdown is the Python: Zero to Coder program from InformIT. While most introductory courses focus on the basics of the language, this course goes one step further to explain how Python is used in practice in the fields of data analysis and web development. Learn fundamental programming concepts, such as conditionals, loops, and functions. They are given hands-on, modular problems to solve so they can progress as they go. Finally, students tie it all together and experiment with some real programming in the form of text-based games. The goal of this course is to equip beginners to learn from scratch, navigate the world of software development, and then kick-start their programming journey with introductions to two of the more common uses of Python: data analysis and web development. Skill-based training modules include: Part I “Learn How to Program Today with Python”: 1 - Introduction to Programming and Python, 2 - Python and Programming Basics, 3 - Control Flow with Conditionals, 4 - Lists and Loops,  5 - Advanced Language Topics, 6 - Introduction to Data Analysis in Python, and 7 - Introduction to Web Development in Python. Part II: “Next Level Python”:  1 - Look at Python Basics, 2 - Work with Files, 3 - Manage Your Python Environments, 4 - Choose an IDE, 5 - Understand Python Modules and Namespaces, 6 - Debug and Test Your Code, 7 - Getting Data from the Web, 8 - Create a Web Scraping Application, and 9 - Put your Project on the Internet.

Enroll today (teams & execs welcome): https://tinyurl.com/4c9xns4m 

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Top 10 Python Developer Certification & Training Programs (#9)

Colleagues, CareerFoundry estimates average salary for Python Developers in the US at $110,906. We believe this figure will steadily increase as the Python language becomes more ubiquitous. Number 9 on our countdown is the How to Implement Search Algorithms with Python program from Codecademy.  Linear Search,  Time Complexity of Linear Search, Finding Elements in Lists) Best Case Performance, Worst Case Performance , Average Case Performance. Skill-based training modules include: 1) Linear Search - Imagine that you are a DJ at a party. The diagram on the right shows your playlist for the event. A party guest wants to know if “Uptown Funk” by Bruno Mars is a song on your playlist, 2) Finding Elements in Lists - Linear search can be used to search for a desired value in a list. It achieves this by examining each of the elements and comparing it with the search element starting with the first element to the, 3) Best Case Performance - Linear search is not considered the most efficient search algorithm, especially for lists of large magnitudes. However, linear search is a great choice if you expect to find the target value, 4) Worst Case Performance - There are two worst cases for linear search. Case 1: when the target value, 5) Average Case Performance - If this search was used 1000 times on 1000 different lists, some of them would be the best case, some the worst. For most searches, it would be somewhere in between, 6) Time Complexity of Linear Search - Linear search runs in linear time. Its efficiency can be expressed as a linear function, with the number of comparisons to find a target increasing linearly as the size of the list, N, increases. In the Linear & Binary Search Project - learn to modify a version of binary search to look for data in a sparse dataset.

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

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Data Science - Top 10 Countdown of Certification & Training Programs (#9)

Colleagues, the Global Knowledge 2021 IT Salary Survey ranks Google Certified Professional Data Engineer with an average salary of $171,749 USD #1 out of all IT certifications. ZipRecruiter lists some 773,863 Data Scientist positions in the United States alone. Our #9 recommendation on our Top 10 countdown is the Become a Data Architect program. Plan, design and implement enterprise data infrastructure solutions and create the blueprints for an organization’s data management system. You’ll create a relational database with PostGreSQL, design an Online Analytical Processing (OLAP) data model to build a cloud based data warehouse, and design scalable data lake architecture that meets the needs of Big Data. Finally, you will learn how to apply the principles of data governance to an organization’s data management system.With a focus on Data Architecture Foundations you will 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).

Enroll today (teams & execs welcome): https://tinyurl.com/4mck9jnx 

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Top 10 Machine LearningCertification & Training Programs (#10)

Colleagues, the average salary for a Machine Learning Engineer at $123,524 USD according to Glassdoor. Number 10 in our Top 10 Countdown is the Machine Learning Engineering Career Track Program from Springboard. Learn to 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.  The 500+ hour curriculum features a combination of videos, articles, hands-on projects, and career-related coursework.  Skill-based training modules include: 1) Battle-Tested Machine Learning Models, 2) Deep Learning, 3) Computer Vision and Image Processing, 4) The Machine Learning Engineering Stack, 5) ML Models At Scale and In Production, 6) Deploying ML Systems to Production, and 7) Working With Data. You will build a realistic, complete, ML application that’s available to use via an API, a web service or, optionally, a website. One-on-one Mentorship provides you with  weekly guided calls with your personal mentor, an industry expert. Our career coaching calls will help your mentor. Create a successful job search strategy, Build your Machine Learning Engineering network, Find the right job titles and companies, Craft a Machine Learning Engineer resume and LinkedIn profile, Ace the job interview and Negotiate your salary.

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Top 10 Python Developer Certification & Training Programs (#10)

Colleagues, the average salary of a  Full Stack Python Developer is $121,111 according to ZipRecruiter. Number 10 on our countdown is the Python Scripting Certification. This cert program will equip you in NumPy, PandasVariables, Operands,  Expressions, Conditional Statements, Loops, Command Line Arguments, Writing to the Screen; Sequences, File Operations - Python files I/O Functions, Lists and related operations, Tuples and related operations, Strings and related operations, Sets and related operations, Dictionaries,; Functions, OOPs, Modules, Errors and Exceptions, Parameters, Global variables, Variable scope and Returning Values, Lambda Functions, Object Oriented Concepts, Standard Libraries, Modules Used in Python (OS, Sys, Date and Time etc.), Import Statements. Learn different types of sequence structures, related operations, and their usage. Also learn diverse ways of opening, reading, and writing to files. Then create generic python scripts, how to address errors/exceptions in code, and finally how to extract/filter content using regex. Next, cover basics of statistics, different types of measures and probability distributions, and the supporting libraries in Python that assist in these operations. Finally, learn in detail about data visualization - matplotlib library, grids, axes, plots, markers, colors, fonts and styling, types of plots - bar graphs, pie charts, histograms and contour plots. 

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

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Data Science - Top 10 Certification & Training Programs (#10)

Colleagues, the global data science platform market was valued at $31B in 2020, and it is expected to reach $2301 by 2026, registering a CAGR of 39.7 % during the forecast period, 2021-2026 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.

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