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

Python Certification for Data Science

Colleagues, the Python Certification for Data Science program will enable you to learn these concepts from scratch and help you master important Python programming concepts such as data operations, file operations, object-oriented programming, and various Python libraries such as Pandas, Numpy, Matplotlib essential for Data Science. This Python for Data Science certification training will also make you understand the different types of Machine Learning, Recommendation Systems, and many more Data Science concepts to help you get started with your Data Science career. Build high-demand skills in the Fundamentals of Python programming, Sequences and File Operations, Deep Dive – Functions, OOPs, Modules, Errors and Exceptions, Introduction to NumPy, Pandas and Matplotlib, Data Manipulation, Introduction to Machine Learning with Python, Supervised Learning, Dimensionality Reduction, Unsupervised Learning, Association Rules Mining and Recommendation Systems, Reinforcement Learning, Time Series Analysis, and Model Selection and Boosting. The hands-on demonstration involves creating “Hello World” code, variables, conditional statements and loops.

Enroll today (teams & execs welcome): https://fxo.co/9QE9 

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

Python Scripting Certification

Colleagues, the Python Scripting Certification program equips to 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. The program focuses on the concepts of Python. It will help you to perform operations on variable types. You will learn the importance of Python in a real time environment and will be able to develop applications based on the Object-Oriented Programming concept. At the end of this course, you will be able to develop networking applications with suitable GUI.

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

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

Predictive Modeling and Machine Learning with MATLAB (MathWorks)

Colleagues, the Predictive Modeling and Machine Learning with MATLAB from MathWorks will equip you to harness the power of MATLAB. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background. You will use MATLAB to identify the best machine learning model for obtaining answers from your data. You will prepare your data, train a predictive model, evaluate and improve your model, and understand how to get the most out of your models, deepening your skills in Machine  Learning, Matlab and Predictive Modeling. Training modules include: 1) Creating Regression Models: You will be introduced to the Supervised Machine Learning Workflow, creating and evaluating regression machine learning models (11 videos (Total 73 min), 7 readings, 7 quizzes), 2) Creating Classification Models: Grasp the basics of classification models. Train several types of classification models and evaluate the results 6 videos (Total 45 min), 6 readings, 2 quizzes, 3) Applying the Supervised Machine Learning Workflow: Complete supervised machine learning workflow. You'll use validation data to inform model creation. You'll apply different feature selection techniques to reduce model complexity. Then you will create ensemble models and optimize hyperparameters. At the end of the module, you'll apply these concepts to a final project. 9 videos (Total 49 min), 5 readings, 3 quizzes.

Enroll today (eams & execs welcome): https://tinyurl.com/3mesc4a8 

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