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

Advanced Data Science with IBM Specialization

Colleagues, the Advanced Data Science with IBM Specialization equips you with Massive parallel  data processing, data exploration and visualization, and advanced  machine learning and deep learning.will have a proven deep understanding on massive parallel  data processing, data exploration and visualization, and advanced  machine learning & deep learning. You'll understand the  mathematical foundations behind all machine learning & deep learning  algorithms. You can apply knowledge in practical use cases, justify  architectural decisions, understand the characteristics of  different algorithms, frameworks & technologies & how they  impact model performance & scalability. If you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge.To find out more about IBM digital badges follow the link ibm.biz/badging..Training modules include: 1) Fundamentals of Scalable Data Science - learn  the fundamentals of Apache Spark using Python and PySpark, 2) Advanced Machine Learning and Signal Processing -  invaluable insights into Supervised and Unsupervised Machine Learning Models used by experts in many field relevant disciplines. We’ll learn about the fundamentals of Linear Algebra to understand how machine learning modes work. Then we introduce the most popular Machine Learning Frameworks for python Scikit-Learn and SparkML, 3) Applied AI with DeepLearning - insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and 4) Advanced Data Science Capstone -  a deep understanding on massive parallel data processing, data exploration and visualization, advanced machine learning and deep learning and how to apply his knowledge in a real-world practical use case.

Register today (teams & execs welcome): https://tinyurl.com/mrxdyer2 

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

Machine Learning in Trading and Finance {New York Institute of Finance}

Colleagues, the Machine Learning in Trading and Finance program from the New York Institute of Finance and Google Cloud will train you in quantitative trading, pairs trading, and momentum trading. You will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it. You should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Training modules include: 1) Introduction to Quantitative Trading and TensorFlow - key components that are common to every trading strategy, no matter how complex, 2) Training neural networks with Tensorflow 2 and Keras, 3) Build a Momentum-based Trading System - traders buy or sell assets according to the strength of recent price trends. Price momentum is similar to momentum in physics, where mass multiplied by velocity determines the persistence with which an object will follow its current path (like a heavy train on a track). Momentum traders bet that an asset price that is moving strongly in a given direction will continue to move in that direction until the trend loses strength or reverses, and 4) Build a Pair Trading Strategy Prediction Model - discuss what pairs trading is, and how you can make money doing it. 

Sign-up today (teams & execs welcome): https://tinyurl.com/yfe4yncz 

Much career success, Lawrence E. Wilson - Financial Certification Academy (https://tinyurl.com/2p8f67vk)

Artificial Intelligence-Machine Learning-Deep Learning - Career Transformation Guide (2022)

Colleagues, this Artificial Intelligence-Machine Learning-Deep Learning - Career Transformation Guide includes valuable information that enables you to accelerate your career growth and income potential - Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Community and Networking resources. Glassdoor estimates the average salary for a Machine Learning Engineer at $131,001 USD. Indeed lists 2091  openings with an averMachine Learning Engineer age nationwide salary of $131,276 USD. The San Francisco Bay Area is the high-end of the salary range at $193,485 with Eden Prairie, Minnesota at $106,780. The three critical success factors in our career transformation model. First, Get Certified. Professionals with best in-class skill sets combined with industry-leading certifications advance more rapidly than your peers and typically earn 3%-5% above their colleagues. Second, Get Published. Relevant, succinct and insightful articles on best practices in your technical domain or functional discipline enhance your credibility and integrity. And third, Get Connected. Developing and maintaining a robust professional network - locally and globally - bolsters your career persona and positions you as the “go to” subject matter expert in your field.

Download your copy and register today. Share with your colleagues!: https://tinyurl.com/2p996t8d 

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

Deep Learning with TensorFlow (Application of Neural Networks)

Colleagues, the Deep Learning with TensorFlow - Applications of Deep Neural Networks to Machine Learning Tasks program brings machine-learning to life with interactive demos from the most popular Deep Learning library, TensorFlow, and its high-level API, Keras. Essential theory is whiteboarded to provide an intuitive understanding of Deep Learning’s underlying foundations, i.e., artificial neural networks plus provided Python-based Jupyter notebooks. This intermediate-level program includes over 6 hours of instruction. Learn to Build Deep Learning models in TensorFlow and Keras, Interpret the results of Deep Learning models, Troubleshoot and improve Deep Learning models. Skill-based training modules include: 1)  Introduction to Deep Learning - overview of deep learning, its roots in artificial neural networks, and the breadth of transformative applications it produces and builds an introductory neural network, 2) How Deep Learning Works - main families of deep neural networks and their applications, shows you deep learning in action via a web application called the TensorFlow Playground, 3) Convolutional Networks - construct a deep network. This lesson builds upon those theoretical foundations to build more effective deep nets, gain an understanding of convolutional layers and how they can be stacked to solve increasingly complex problems with larger data sets. In order to make sense of the outputs from these sophisticated models, the TensorBoard result-visualization tool is used, 4) Introduction to TensorFlow - leading Deep Learning libraries are compared, and then you get down to business with TensorFlow, the open-source library doing the heavy neural network-lifting underneath Keras and build your own deep learning models in TensorFlow, and 5) Improving Deep Networks - tune model hyperparameters and create a deep learning project as well as outlining resources for further self-study.

Enroll today (individuals & teams welcome): https://tinyurl.com/4rzp4r5m 

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

Python, TensorFlow & PyTorch - Career Transformation Guide

Colleagues, the Python, TensorFlow and PyTorch  - Career Transformation Guide for 2022 includes valuable information that enables you to accelerate your career growth and income potential - Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Community and Networking resources.There are three critical success factors in our career transformation model. First, Get Trained and Certified. Professionals with best in-class skill sets combined with industry-leading certifications advance more rapidly than your peers and typically earn 3%-5% above their colleagues. Second, Get Published. Relevant, succinct and insightful articles on best practices in your technical domain or functional discipline enhance your credibility and integrity. And third, Get Connected. Developing and maintaining a robust professional network - locally and globally - bolsters your career persona and positions you as the “go to” subject matter expert in your field. Salary.com reports the media salary for Python Developers across the US is $95,120 USD. ZipRecruiter lists 214.206 Python development positions in the US alone. 

Download your copy and register today. Share with your colleagues!: https://tinyurl.com/bdhamd8e 

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

Supervised Learning - Linear Regression in Python

Colleagues, the Supervised Learning - Linear Regression in Python will equip you to apply Least Squares regression and its assumptions to real world data. Then we'll improve on that algorithm with Penalized Regression and non-parametric Kernel methods Learn to apply algorithms in the following areas: Least squares, Penalized least squares, Non-parametric methods, Model selection and fit on real world applications including Insurance and Healthcare. The curriculum addresses: 1) Introduction to Supervised Linear Regression, 2) Introduction to Machine Learning and Supervised Regression - Introduction to Machine Learning and Supervised Regression, Discuss the overall AI ecosystem and how Machine Learning (ML) is part of that ecosystem. - Understand the 3 different types of algorithms that make up ML - Provide some intuition for why functions and optimizations are important in ML, 3) Machine Learning - Understanding Assumptions and survey the statistical concepts important to understanding Linear Algorithms. - Design of experiments. - Conducting experiments. - Understand the difference between linear and non-linear functions, 4) Least Squares Regression - Ordinary Regression - Develop the simple linear regression algorithm. Understand the basic linear regression assumptions. Learn to identify when assumption violations occur. Understand how to evaluate model output, 5) Least Squares Regression - Multiple Regression - Extend the Least Squares algorithm to multiple dimensions Explore data to understand variable importance Prepare data for multiple regression Optimizing between Bias and Variance, 6) Penalized Regression - L1/L2 Optimization - understand motivation behind penalized regression Optimize L1 Regression (Lasso) parameters Optimize L2 Regression (Ridge) parameters Combine the L1/L2 penalties (Elastic Net) Understand the difference and trade offs between Subset Selection and Shrinkage Optimize hyper-parameters with Cross-Validation, 7) Kernel Methods - Support Vector Machines - Understand theory and motivation behind kernel methods. Derive a basic kernel and use the kernel trick. Build a support vector classifier. Extend to regression with a support vector machine. Optimize parameters with Cross validation and Grid Search, 8) Kernel Methods - Gaussian Process Regression - Understand multivariate distributions and non-parametric regression. Use Bayesian probability with joint probabilities. Develop theory behind Gaussian Process Regression. Optimize kernels and hyper-parameters, and 9) Summary and Real World Applications - Review Supervised Linear Regression topics. Perform Linear regression on real world data.

Enroll today (individuals & teams welcome): https://tinyurl.com/uvjs63tw 

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