Machine Learning and AI with Python and Jupyter Notebook

Colleagues, the  Essential Machine Learning and AI with Python and Jupyter Notebook program shows you how AWS and Google Cloud Platform can be used to solve real-world business problems in Machine Learning and AI. It covers how to get started with Python via Jupyter Notebook, and then proceeds to dive into nuts and bolts of Data Science libraries in Python, including Pandas, Seaborn, scikit-learn, and TensorFlow. EDA, or exploratory data analysis, is at the heart of Machine Learning;. Software engineering fundamentals tie the series together, with key instruction on linting, testing, command-line tools, data engineering APIs. Learn Data Science concepts and Python fundamentals for Machine Learning, how to develop a Data Engineering API with Flask and Pandas,EDA, Python and AWS, and understand both Python and Google Cloud Platform. Training modules address: 1) Data Science Coding with Python Fundamentals, 2) Applying Functions, 3) Python Control Structures, 4) Deploying Libraries in Python, 5) Python Classes, 6) IO Operations in Python and Pandas, 7) Software Carpentry, 8) Data Engineering API with Flask and Pandas, 9) Social Power NBA EDA and ML Project, 10) Intermediate Machine Learning, 11)  Python based AWS Cloud ML and AI Pipelines, 12) Python based Google Compute Platform ML and AI Pipelines,  13) Command-line Machine Learning Tools, and 14) Datascience: Case Study Social Power in the NBA. 

Enroll today (individuals & teams welcome): https://tinyurl.com/2s246wzm 

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

Deep Learning for Natural Language Processing

Colleagues, the Deep Learning for Natural Language Processing - Second Edition program  is an introduction to building 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. This program is for intermediate skill level professionals. Learn How To 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 API for TensorFlow 2, and Improve Deep Learning model performance by selecting appropriate model architectures and tuning model hyperparameters. Training modules that equip with high-demand skills address: 1)The Power and Elegance of Deep Learning for NLP, 2) Word Vectors, 3) Modeling Natural Language Data, 4) Recurrent Neural Networks, and 5) Advanced Models. 

Enroll today (individuals & teams welcome): https://tinyurl.com/matehbru 

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

Practical Python Data Science Techniques

Colleagues, the Practical Python Data Science Techniques program equips you to calculate the word frequencies using Data Science techniques of Python, work with Scikit-learn to solve problems in Machine Learning, and perform Cluster Analysis using Python Data Science. You will learn to perform Exploratory data analysis on your Data, Clean and process your Data to have the right shape, Tokenize your Document to words with Python, Calculate the word frequencies using Data Science Techniques of Python, Work with scikit-learn to solve every problem in Machine Learning, Perform Cluster Analysis using Python Data Science Techniques, and Build a Time Series Analysis with Panda. Work time dimension data and learn to build a recommendation system as well as about supervised learning problems (regression and classification) and unsupervised learning problems (clustering). Learn to perform text preprocessing steps that are necessary for every text analysis application. Specifically, the course will cover tokenization, stop-word removal, stemming and other preprocessing techniques.Training modules address: 1) Exploring Your Data, 2) Dealing with Text, 3) Machine Learning Problems, and 4) Time Series and Recommender Systems.

Enroll today (individuals & teams welcome): https://tinyurl.com/crtzhxts 

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

PyTorch For Deep Learning and Computer Vision

Colleagues, PyTorch is becoming a very transformative framework in the field of Deep Learning. Its flexibility has made building a Deep Learning model easier. This PyTorch For Deep Learning And Computer Vision teaches you Deep Learning with PyTorch. PyTorch has completely changed the landscape of the deep learning domain with its flexibility and has made building deep learning models easier. The development world offers some of the highest paying jobs in deep learning. Instructor Rayan Slim will help you learn and master deep learning with PyTorch. Having taught over 44,000 students, Rayan is a highly rated and experienced instructor who has followed a learning-by-doing style to create this course. By the end of this course, you will have built state-of-the-art deep learning and Computer Vision applications with PyTorch. You will learn the tensor data structure, machine and deep learning applications with PyTorch, neural networks from scratch, applied themes of advanced imagery and Computer Vision, complex problem solvings in Computer Vision by harnessing highly sophisticated pre-trained models and style transfer to build sophisticated AI applications. Skill-based training modules address: 1) Intro to Tensors, 2) Linear Regression, 3) Perceptrons, 4) Deep Neural Networks, 5) Image Recognition, 6) Convolutional Neural Networks, 7) CIFAR 10 Classification, 8) Transfer Learning, 9) Style Transfer – PyTorch plus two Appendices: Crash courses in Python and NumPy.

Enroll today (individuals & teams welcome): https://tinyurl.com/25bc4549 

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

Python Developer (Training)

Colleagues, this in-depth Python Developer training program will teach you the ins and outs of Python programming. You will learn to work with iPhone Notebook, the Collections Module, regular expressions, databases, CSV files, JSON, and XML. You will also learn advanced sorting, how to write object-oriented code in Python, and how to test and debug your Python code. Finally, you'll get a rapid introduction to NumPy, pandas, and matplotlib, which are Python libraries. Skill-based training modules include: 1) Introduction to Python-Python Basics, Functions and Modules, Math, Python Strings, Iterables: Sequences, Dictionaries, Sets, Virtual Environments, Packages, and pip, Flow Control, Exception Handling, Python Dates and Times, File Processing, PEP8 and Pylint, 2) Advanced Python-Advanced Python Concepts, Regular Expressions, Working with Data, Testing and Debugging, Classes and Objects, and 3) Python Data Analysis with JupyterLab-JupyterLab, NumPy, Pandas,.  

Enroll today (individuals & teams welcome): https://tinyurl.com/s8a54s2b 

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

Machine Learning Engineering Career Track (with a Mentor)

Colleagues, the Machine Learning Engineering Career Track Program will equip you 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.

Enroll today (individuals & teams welcome): https://fxo.co/BWBp  

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

TensorFlow 2.0 for Deep Learning (Training)

Colleagues, the TensorFlow 2.0 for Deep Learning program provides you with the core of deep learning using TensorFlow 2.0. You’ll learn to train your deep learning networks from scratch, pre-process and split your datasets, train deep learning models for real-world applications, and validate the accuracy of your models. By the end of the course, you’ll have a profound knowledge of how you can leverage TensorFlow 2.0 to build real-world applications without much effort. You will learn to Develop real-world deep learning applications, Classify IMDb Movie Reviews using Binary Classification Model, Build a model to classify news with multi-label, Train your deep learning model to predict house prices, Understand the whole package: prepare a dataset, build the deep learning model, and validate results, Assess the working of Recurrent Neural Networks and LSTM with hands-on examples, and Implement autoencoders and denoise autoencoders in a project to regenerate images. Skill-based training modules: 1) Deep Learning Basics, 2) TensorFlow 2.0 for Deep Learning, 3) Working with CNNs for Computer Vision and Deep Learning, 4) Working with LSTM for Text Data and Deep Learning, 5) Working with RNNs for Time Series Sequences and Deep Learning, 6) Autoencoders EAE and Denoising AE, and 7) Deep Learning Mini-Projects. This program uses a dedicated GitHub workspace.

Enroll today (individuals & teams welcome): https://tinyurl.com/4mu8fyfk 

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