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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Graphic source: Course Report

Deep Learning with Tensorflow, Keras and PyTorch

AI colleagues, the Deep Learning with Tensorflow, Keras and PyTorch program is an introduction 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/yspuw6j3 

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Data Science Fundamentals

Colleagues, the Data Science Fundamentals Part 1 - Learning Basic Concepts, Data Wrangling, and Databases with Python program 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/bd6r76y7 

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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 Visualization with Python

Colleagues, the Data Visualization with Python program will equip you in several essential data visualization techniques, when to use them, and how to implement them with Python and Matplotlib. Gain thorough knowledge of data visualization. In this course, Introduction to Data Visualization with Python, you'll learn how to use several essential data visualization techniques to answer real-world questions. First, you'll explore techniques including scatter plots. Next, you'll discover line charts and time series. Finally, you'll learn what to do when your data is too big. When you're finished with this course, you'll have a foundational knowledge of data visualization that will help you as you move forward to analyze your own data. Training modules include: 1) Introduction to Jupyter, Pandas, and Matplotlib, 2) Finding Distribution of Data with Histograms, 3) Creating Time Series with Line Charts, 4) Examining Relationships in Data with Scatter Plots, 5) Comparing Data with Bar Graphs, 6) What to Do When Your Data Is Too Big, and 7) Solving Real-world Problems with Visualization. Skill-based training modules include: 1) Introduction to Jupyter, Pandas, and Matplotlib, 2) Finding Distribution of Data with Histograms, 3) Creating Time Series with Line Charts, 4) Examining Relationships in Data with Scatter Plots, 5) Comparing Data with Bar Graphs, 6) What to Do When Your Data Is Too Big, and 7) Solving Real-world Problems with Visualization.

Enroll today (teams & execs welcome): https://tinyurl.com/3rnen3u9 

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Learn Statistics with Python

Colleagues, join the 31,956 professionals who have taken the Learn Statistics with Python training from 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. It includes 15 hours of instruction, and a Certificate of Completion. This program consists of 11 lessons, 6 projects and 6 quizzes. Skill-based training modules cover: 1) Mean, Median and Mode, 2) Variance and Standard Deviation, 3) Histograms, 4) Describing Histograms, 5) Quartiles, Quantiles and Interquartile Range, and 6) Boxplots.

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

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AWS Machine Learning Engineer

Colleagues, according to Glassdoor the average salary for a Machine Learning Engineer is $123,524. The AWS Machine Learning Engineer program from Udacity enables you to 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/5y4ac8mj

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Natural Language Processing on Google Cloud

Colleagues, the Natural Language Processing on Google Cloud program includes an overview of sequence model architectures and how to handle inputs of variable length. 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. Training modules: 1) NLP on Google Cloud - NLP APIs such as the Dialogflow API, and the NLP solutions such as Contact Center AI and Document AI, 2) NLP with Vertex AI - explores AutoML and custom training, which are the two options to develop an NLP project with Vertex AI. Additionally, the module introduces an end-to-end NLP workflow and provides a hands-on lab to apply the workflow to solve a task of text classification with AutoML, 3) Text representation - prepare text data in NLP and introduces the major categories of text representation techniques, 4) NLP models - ANN, DNN, RNN, LSTM, and GRU, and 5) Advanced NLP models - encoder-decoder, attention mechanism, transformers, BERT, and large language models.

Enroll today (teams & execs welcome): https://tinyurl.com/5n8ksyd5 

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