Data Structures and Algorithms

Colleagues, in the Data Structures and Algorithms program you will learn data structures and algorithms by solving 80+ practice problems. You will begin each course by learning to solve defined problems related to a particular data structure and algorithm. By the end of each course, you would be able to evaluate and assess different data structures and algorithms for any open-ended problem and implement a solution based on your design choices. Knowledge of Python and Basic Algebra is recommended. Skill-based training modules include: 1) Introduction - meet your instructors, and refresh your Python skills. Learn the framework to deconstruct any open-ended problem and then understand the concepts of time and space complexity (Project: Unscramble ComputerScience Problems), 2) Data Structures - can be used to store data. Implement different methods used to manipulate these data structures and examine the efficiency. Understand the advantages and applications of different data structures. Learn how to approach open ended problems (either in interview or real-world) and select appropriate data structures based on requirements (Project: Show Me the Data Structures, 3) Basic Algorithms - such as searching and sorting on different data structures and examine the efficiency of these algorithms. Use recursion to implement these algorithms and then learn how some of these algorithms can be implemented without recursion. Practice selecting and modifying these algorithms for a variety of interview problems (Project: Problems vs. Algorithms), and 4) Advanced Algorithms - such as brute-force greedy algorithms, graph algorithms, and dynamic programming which optimizes recursion by storing results to sub problems. (Project: Route Planner).

Enroll today (teams & execs welcome): https://tinyurl.com/3ua6b24c 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Deep Learning (Training)

Colleagues, the Deep Learning program will equip you to drive advances in artificial intelligence that are changing our world. Enroll now to build and apply your own deep neural networks to challenges like image classification and generation, time-series prediction, and model deployment. Become an expert in neural networks, and learn to implement them using the deep learning framework PyTorch. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models accessible from a website. Working knowledge of Python, NumPy, pandas and familiarity with calculus and linear algebra is recommended. Training modules with hands-on projects include: 1) Introduction - apply style transfer to your own images, and gain experience using development tools such as Anaconda and Jupyter notebooks, 2) Neural Networks - build your first network with Python and NumPy (Project: Predicting Bike-Sharing Patterns), 3) Convolutional Neural Networks - build convolutional networks and use them to classify images (faces, melanomas, etc.) based on patterns and objects that appear in them (Project: Landmark Classification & Tagging for Social Media), 4) Recurrent Neural Networks - build your own recurrent networks and long short-term memory networks with PyTorch (Project: Generate TV Scripts), 5) Generative Adversarial Networks - implement a Deep Convolutional GAN (generative adversarial network) to generate realistic images, with Ian Goodfellow, the inventor of GANs, and Jun-Yan Zhu, the creator of CycleGANs (Project: Generate Faces), and 6) Deploy a Sentiment Analysis Model - deploy a PyTorch sentiment analysis model (Project: Deploying a Sentiment Analysis Model).

Enroll today (teams & execs welcome): https://tinyurl.com/2p8n3na6 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Natural Language Processing in TensorFlow

Colleagues join over 100k professionals enrolled in the Natural Language Processing in TensorFlow training program. teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work and learn to apply RNNs, GRUs, and LSTMs in TensorFlow. Finally, you’ll get to train an  LSTM on existing text to create original poetry. Build natural language processing systems using TensorFlow, Process text, including tokenization and representing sentences as vectors, Apply RNNs, GRUs, and LSTMs in TensorFlow, and Train LSTM. Gain skills in Natural Language Processing, Tokenization, Machine Learning, Tensorflow and RNNs. Training modules include: 1) Sentiment in text - the tokenization of that text. This is the process of converting the text into numeric values, with a number representing a word or a character. This week you'll learn about the Tokenizer and pad_sequences APIs in TensorFlow and how they can be used to prepare and encode text and sentences to get them ready for training neural networks, 2) Word Embeddings - where tokens are mapped as vectors in a high dimension space. With Embeddings and labeled examples, these vectors can then be tuned so that words with similar meaning will have a similar direction in the vector space, 3) Sequence models - learn a variety of model formats that are used in training models to understand context in sequence, and 4) Sequence models and literature - use your knowledge for prediction. Given a body of words, you could predict the word most likely to follow a given word or phrase, and once you've done that, to do it again, and again. With that in mind, this week you'll build a poetry generator. It's trained with the lyrics from traditional Irish songs.

Enroll today (eams & execs welcome): https://tinyurl.com/4wryjdbm 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Deep Learning for Natural Language Processing

Colleagues, the Deep Learning for Natural Language Processing program equips you to build natural language models with deep learning. These lessons bring intuitive explanations of essential theory to life with interactive, hands-on Jupyter notebook demos. Examples feature Python and Keras, the high-level API for TensorFlow 2, the most popular Deep Learning library. In early lessons, specifics of working with natural language data are covered, including how to convert natural language into numerical representations that can be readily processed by machine learning approaches. In later lessons, state-of-the art Deep Learning architectures are leveraged to make predictions with natural language data. Skill-based lessons include: 1) The Power and Elegance of Deep Learning for NLP - how it has been revolutionized in recent years by Deep Learning approaches, run the code, and theory that is essential for building an NLP specialization upon; 2) Word Vectors - linguistics section that introduces computational representations of natural language elements and an illustrating what word vectors are as well as how the beautiful word2vec algorithm creates them; 3) Modeling Natural Language Data - vector-space embeddings and creating word vectors with word2vec. Whiteboard how to calculate a concise and broadly useful summary metric called the Area Under the Curve of the Receiver Operator Characteristic then calculate that summary metric in practice by building and evaluating a dense neural network for classifying documents. The lesson then goes a step further by showing you how to add convolutional layers into your deep neural network as well; 4) Recurrent Neural Networks - theory, apply this theory by incorporating an RNN into your document classification model. Jon then provides a high-level theoretical overview of especially powerful RNN variants--the Long Short-Term Memory Unit and the Gated Recurrent Unit--then incorporate these variants into your deep learning models; and 5) Advanced Models - LSTM  special cases, namely the Bi-Directional and Stacked varieties plus data sets that you can use to train powerful Deep Learning models and other advanced approaches, including sequence generation, seq2seq models, attention, transfer learning, non-sequential network architectures, and financial time series applications.

Enroll today (eams & execs welcome): https://tinyurl.com/4dp57tck 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Become a Data Engineer

Colleagues, this Data Engineer program will equip you to design data models, build data warehouses and data lakes, automate data pipelines, and work with massive datasets. ZipRecruiter estimates average US Data Engineers’ salaries at $121,938. To be successful in this program, you should have intermediate Python and SQL Learn to design data models, build data warehouses and data lakes, automate data pipelines, and work with massive datasets. At the end of the program, you’ll combine your new skills by completing a capstone project. Training modules - each with a hands-on project - include: 1) Data Modeling - create relational and NoSQL data models to fit the diverse needs of data consumers. Use ETL to build databases in PostgreSQL and Apache Cassandra), 2) Cloud Data Warehouses - acquire data warehousing skills and deepen your understanding of data infrastructure. Create cloud-based data warehouses on Amazon Web Services - AWS (Project: Build a Cloud Data Warehouse), 3) Spark and Data Lakes - understand the big data ecosystem and how to use Spark to work with massive datasets. Store big data in a data lake and query it with Spark (Project: Bulk a Data Lake), 4) Data Pipelines with Airflow - schedule, automate, and monitor data pipelines using Apache Airflow. Run data quality checks, track data lineage, and work with data pipelines in production (Project: Data Pipelines with Airflow), and 5) Capstone Project - combine what you've learned throughout the program to build your own data engineering portfolio project.

Enroll today (eams & execs welcome): https://tinyurl.com/mtfdt7yv 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Hypothesis Testing with Python

Colleagues, the Hypothesis Testing with Python from Code Academy will enable you to Plan, implement, and interpret a hypothesis test in Python. Hypothesis testing is used to address questions about a population based on a subset from that population. For example, A/B testing is a framework for learning about consumer behavior based on a small sample of consumers. This course assumes some preexisting knowledge of Python, including the NumPy and pandas libraries. Learn hypothesis testing for a sample statistic, hypothesis testing associations and sample size determination. Acquire high-demand, marketable skills in 1) Introduction to Hypothesis Testing - Find out what you’ll learn in this course and why it’s important, 2) Hypothesis testing: Testing a Sample Statistic - Learn about hypothesis testing and implement binomial and one-sample t-tests in Python, 3) Hypothesis Testing: Testing an Association - Learn about hypothesis tests that can be used to evaluate whether there is an association between two variables, 4) Experimental Design - Learn to design an experiment to make a decision using a hypothesis test, and 5) Hypothesis Testing Projects - Practice your hypothesis testing skills with some additional projects.

Enroll today (eams & execs welcome): https://fxo.co/CZIg 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

AI TensorFlow Developer Professional Certificate (Google TensorFlow Certificate)

Colleagues, in the AI TensorFlow Developer Professional Certificate program from DeepLearning.AI you will learn the necessary tools to build scalable AI-powered applications with TensorFlow. After finishing this program, you will apply your new TensorFlow skills to a wide range of problems and projects. This program can help you prepare for the Google TensorFlow Certificate exam and bring you one step closer to achieving the Google TensorFlow Certificate. Ready to deploy your models to the world? Learn how to go live with your models with the TensorFlow: Data and Deployment Specialization. Learn Computer Vision, Convolutional Neural Network, Machine Learning, Natural Language Processing, Tensorflow, Inductive Transfer, Augmentation, Dropouts, Tokenization, RNNs, Forecasting and Time Series. Acquire high-demand and marketable skills, including: 1) Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning, 2) Convolutional Neural Networks in TensorFlow, 3) Natural Language Processing in TensorFlow, and 4) Sequences, Time Series and Prediction.

Enroll today (eams & execs welcome): https://tinyurl.com/vuavxus4  

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Convolutional Neural Networks in TensorFlow

Colleagues join over 138k professionals enrolled in the Convolutional Neural Networks in TensorFlow program from DeepLearning.AI.  TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models. Learn the necessary tools to build scalable AI-powered applications with TensorFlow. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. This program can help you prepare for the Google TensorFlow Certificate exam and bring you one step closer to achieving the Google TensorFlow Certificate. Acquire high-demand skills, including Computer Vision, Convolutional Neural Network, Machine Learning, Natural Language Processing, Tensorflow, Inductive Transfer, Augmentation, Dropouts, Tokenization, RNNs,Forecasting and Time Series. Training modules address: 1) Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning, 2) Convolutional Neural Networks in TensorFlow, 3) Natural Language Processing in TensorFlow, and 4) Sequences, Time Series and Prediction.

Enroll today (eams & execs welcome): https://tinyurl.com/mvbhm8hm 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Applied Data Science with Python

Colleagues, Applied Data Science with Python from the University of  Michigan over 263k students enrolled equips you for exceptional career and income growth. Glassdoor’s salary range for Data Scientists is $65k-$165k..Training modules include: 1)  Introduction to Data Science in Python - take tabular data, clean it, manipulate it, and run basic inferential statistical analyses, 2) Applied Plotting, Charting & Data Representation in Python - information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations, visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework, 3) Applied Machine Learning in Python - dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models, apply the Scikit learn predictive modeling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting), 4) Applied Text Mining in Python - text mining and manipulation basics. The course begins with an understanding of how text is handled by Python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text, common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes, natural language processing methods to text, and demonstrate how text classification is accomplished, and  advanced methods for detecting the topics in documents and grouping them by similarity (topic modeling), and 5) Applied Social Network Analysis in Python - network analysis through tutorials using the NetworkX library, connectivity and network robustness, centrality of a node in a network, and models of network generation and the link prediction problem. 

Register today (teams & execs welcome): https://tinyurl.com/y2k3pq6u 

Much career success, Lawrence E. Wilson -  Artificial Intelligence Academy

Deep Learning for Natural Language Processing LiveLessons: Applications of Deep Neural Networks to Machine Learning Tasks

Colleagues, the Deep Learning for Natural Language Processing LiveLessons: Applications of Deep Neural Networks to Machine Learning Tasks program brings intuitive explanations of essential theory to life with interactive, hands-on Jupyter notebook demos. Examples include Python and Keras, the high-level API for TensorFlow, the most popular Deep Learning library. In the early lessons, specifics of working with natural language data are covered, including how to convert natural language into numerical representations that can be readily processed by machine learning approaches. In the later lessons, state-of-the art Deep Learning architectures are leveraged to make predictions with natural language data. Preprocess natural language data for use in machine learning applications. Transform natural language into numerical representations with word2vec, Make predictions with Deep Learning models trained on natural language, Apply state-of-the-art NLP approaches with Keras, the high-level TensorFlow API, Improve Deep Learning model performance by tuning hyperparameters Lessons include: 1) The Power and Elegance of Deep Learning for Natural Language Processing, 2) Word Vectors, 3) Modeling Natural Language Data, 4) Recurrent Neural Networks, and 5) Advanced Models. (InformIT) 

Enroll today (eams & execs welcome): https://tinyurl.com/2p84v5dt 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy