How to Implement Search Algorithms with Python

Colleagues, the How to Implement Search Algorithms with Python program teaches you to optimize your approach for each search in your application and makes your overall system more efficient. Proficiency in this topic will help prepare you for your next coding interview and will help you understand how data retrieval methods work. Gain high-demand, marketable skills in Linear Search, 2) Finding Elements in Lists - Linear search can be used to search for a desired value in a list. It achieves this by examining each of the elements and comparing it with the search element starting with the first element, 3) Best Case Performance - Linear search is not considered the most efficient search algorithm, especially for lists of large magnitudes. However linear search is a great choice if you expect to find the target value, 4) Worst Case Performance - There are two worst cases for linear search. Case 1: when the target value at the end of the list, 5) Average Case Performance - If this search was used 1000 times on 1000 different lists, some of them would be the best case, some the worst. For most searches, it would be somewhere in between., 6) Time Complexity of Linear Search - Linear search runs in linear time. Its efficiency can be expressed as a linear function, with the number of comparisons to find a target increasing linearly as the size of the list, N, 7) Review - Congratulations! You have learned how linear search works and how to use it to search for values in lists. Let’s review what we learned: * Linear search is a search algorithm that is sequential, and 8) Linear & Binary Search Project - Learn to modify a version of binary search to look for data in a sparse dataset.

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

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

Custom Models, Layers, and Loss Functions with TensorFlow

Colleagues, the Custom Models, Layers, and Loss Functions with TensorFlow program provide learners with more control over their model architecture and tools that help them create and train advanced ML models. Gain high-demand skills in Functional APIs, Custom Layers, Custom and Exotic Models with Functional APIs and Custom Loss Functions. Compare Functional and Sequential APIs, discover new models you can build with the Functional API, and build a model that produces multiple outputs including a Siamese network, Build custom loss functions (including the contrastive loss function used in a Siamese network) in order to measure how well a model is doing and help your neural network learn from training data, Build off of existing standard layers to create custom layers for your models, customize a network layer with a lambda layer, understand the differences between them, learn what makes up a custom layer, and explore activation functions, and Build off of existing models to add custom functionality, learn how to define your own custom class instead of using the Functional or Sequential APIs, build models that can be inherited from the TensorFlow Model class, and build a residual network (ResNet) through defining a custom model class. Training modules include: 1) Functional APIs - Compare how the Functional API differs from the Sequential API, and see how the Functional API gives you additional flexibility in designing models. Practice using the functional API and build a Siamese network, 2) Custom Loss Functions - measure how well a model is doing, and are used to help a neural network learn from the training data. Learn how to build custom loss functions, including the contrastive loss function that is used in a Siamese network, 3) Custom Layers - flexibility to implement models that use non-standard layers. Practice building off of existing standard layers to create custom layers for your models, 4) Custom Models - build off of existing models to add custom functionality. This week, extend the TensorFlow Model Class to build a ResNet model, and 5) Bonus Content - customize what your model outputs or how it behaves during training and implement a custom callback to stop training once the callback detects overfitting.

Enroll today (individuals & teams welcome): https://tinyurl.com/98u4wn2c 

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

Advanced Computer Vision with TensorFlow

Colleagues, the Advanced Computer Vision with TensorFlow training program will enable you to explore image classification, image segmentation, object localization, and object detection. Apply transfer learning to object localization and detection, apply object detection models such as regional-CNN and ResNet-50, customize existing models, and build your own models to detect, localize, and label your own rubber duck images, implement image segmentation using variations of the fully convolutional network (FCN) including U-Net and d) Mask-RCNN to identify and detect numbers, pets, zombies, and more., and identify which parts of an image are being used by your model to make its predictions using class activation maps and saliency maps and apply these ML interpretation methods to inspect and improve the design of a famous network, AlexNet. Gain high-demand skills in Salience, Image Segmentation, Model Interpretability, Class Activation Maps and TensorFlow Object Detection API. Training modules that will help advance your career include: 1) Introduction to Computer Vision - overview of image classification, object localization, object detection, and image segmentation. Also be able to describe multi-label classification, and distinguish between semantic segmentation and instance segmentation. In the rest of this course, you will apply TensorFlow to build object detection and image segmentation models, 2) Object Detection - overview of some popular object detection models, such as regional-CNN and ResNet-50. You’ll use object detection models that you’ll retrieve from TensorFlow Hub, download your own models and configure them for training, and also build your own models for object detection. By using transfer learning, you will train a model to detect and localize rubber duckies using just five training examples, 3) Image Segmentation - using variations of the fully convolutional neural network, 4) Visualization and Interpretability - learn about the importance of model interpretability, which is the understanding of how your model arrives at its decisions and  implement class activation maps, saliency maps, and gradient-weighted class activation maps.

Enroll today (individuals & teams welcome): https://tinyurl.com/drm2pss 

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

Model Tuning for Machine Learning (Training)

Colleagues, the Model Tuning for Machine Learning training program will equip you to slingshot the predictive capabilities of your models, far out-pacing the limits of out-of-box ML. From a ground-up perspective, we'll understand how a model functions, the part of the model that is able to fit the data on its own, and how important additional tuning and fitting by a trained ML engineer is. This module includes real-world examples, coding assignments, and lots of in-depth exploration of how and why model tuning should be done. If you understand the material in this course, your models will improve, and the results you will be able to deliver will as well. The 32 training modules address: Introduction and expectation-setting, Hyperparameters, Intro to Bayesianism, Intro to Bayesian Model Averaging, Bayesian Model Averaging- Specification, Occam's Window, Computing the Integral, Bayesian Model Averaging-Worked Example, Intro to Bootstrap Aggregation, Intro to Bootstrap Aggregation- CART, Problem with Bagged Decision Trees, Random Forests- Start to Finish, Random Forests: Time-Accuracy Tradeoff, Boosted Trees- Differences from Random Forest, Boosted trees- Adaboost Procedure, XGBoost- Gradient Boosting, Boosted Trees- Final Decision, Introduction to Hyper-Parameters- Basics, Hyperparameters in Decision Trees, Hyperparamters in Decision Trees- Levels, Hyperparameters in decision trees- AUC, Finding optimal hyperparameters- Brute Force, Finding Optimal Hyperparameters- Sanity Check, Intro to Stacking, Intro to Stacking- Motivation, Stacking- Pedigree, Know Your Data, Time/Value Tradeoff, and Example Scenario - Network Transactions. 

Enroll today (individuals & teams welcome): https://tinyurl.com/kkesjbb4 

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

Data Science: K-Means Clustering in Python (Training)

Colleagues, the Data Science: K-Means Clustering in Python program will equip you in mathematics, statistics and programming skills that are necessary for typical data analysis tasks. You will consider these fundamental concepts on an example data clustering task, and you will use this example to learn basic programming skills that are necessary for mastering Data Science techniques. During the course, you will be asked to do a series of mathematical and programming exercises and a small data clustering project for a given dataset. Define and explain the key concepts of data clustering, Demonstrate understanding of the key constructs and features of the Python language, Implement in Python the principle steps of the K-means algorithm while Designing and executing a whole data clustering workflow and interpret the outputs. Gain high-demand data science skills in K-Means Clustering, Machine Learning and Programming in Python. Training modules include: 1)  Foundations of Data Science: K-Means Clustering in Python, 2) Means and Deviations in Mathematics and Python, 3) Moving from One to Two Dimensional Data, 4) Introducing Pandas and Using K-Means to Analyse Data, and 5) A Data Clustering In-Class Project. 

Enroll today (individuals & teams welcome): https://tinyurl.com/2uuhjjr9 

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

TensorFlow: Advanced Techniques Specialization

Colleagues, master your skill set with the TensorFlow: Advanced Techniques Specialization from DeepLearning.AI. Customize your machine learning models through four hands-on courses. Understand the underlying basis of the Functional API and build exotic non-sequential model types, custom loss functions, and layers, learn optimization and how to use GradientTape & Autograph, optimize training in different environments with multiple processors and chip types, practice object detection, image segmentation, and visual interpretation of convolutions, and explore generative deep learning, and how AIs can create new content, from Style Transfer through Auto Encoding and VAEs to GANs. Gain high demand skills in Model Interpretability, Custom Training Loops, Custom and Exotic Models, Generative Machine Learning, Object Detection, Functional API, Custom Layers, Custom and Exotic Models with Functional API, Custom Loss Functions, Distribution Strategies, Basic Tensor Functionality and GradientTape for Optimization. The four skill-based training modules include: 1) Custom Models, Layers, and Loss Functions with TensorFlow, 2) Custom and Distributed Training with TensorFlow, 3) Advanced Computer Vision with TensorFlow, and 4) Generative Deep Learning with TensorFlow. 

Sign-up today (individuals & teams welcome): https://tinyurl.com/k9pnhsra 

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

Python Programming - Three Best In-Class Training Courses

Colleagues, the dramatic growth in Python programming ensures that well-trained developers and programmers have optimal career and income opportunities. Here are three hand-picked Python programs from our archives to give you a competitive advantage. First is 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. Next is 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. And third, 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. 

Sign-up today (individuals & teams welcome):

• Practical Python Data Science Techniques

• Essential Machine Learning and AI with Python and Jupyter Notebook

• Supervised Learning - Linear Regression in Python

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

TensorFlow 2.0 - Three Best In-Breed Programs for Career Growth

Dev colleagues, TensorFlow 2.0 continues its rapid growth as the premier algorithm library for use with Python. To gain a competitive edge in your development reer here are three hand-picked TF 2.0 programs to sharpen and expand your TensorFlow skillset. First, the Deep Learning Algorithms With TensorFlow 2.0 program will train you to build Deep Learning neural networks with TensorFlow 2.0. Deep Learning has become the dominant method for speech recognition (Google Assistant), computer vision (search for "my pictures" on Google Photos), language translation, and even game-related Artificial Intelligence (AlphaGo and DeepMind). Gain a solid understanding of Deep Learning models and use Deep Learning techniques to solve business and other real-world problems to make predictions quickly and easily. Next, the Natural Language Processing with TensorFlow 2.0 Specialization from Andrew Ng teaches the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. Gain high-demand/highly marketable skills in Natural Language Processing, Tokenization, Machine Learning, Tensorflow and RNNs. And third, the TensorFlow 2.0 for Deep Learning program provides you with the core of deep learning using TensorFlow 2.0. 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. You will learn to Develop real-world deep learning applications and Classify IMDb Movie Reviews using Binary Classification Model.

Sign-up today for one or all programs (individuals & teams welcome):

• Deep Learning Algorithms With TensorFlow 2.0

• Natural Language Processing with TensorFlow 2.0 Specialization

• TensorFlow 2.0 for Deep Learning

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, Understand the language and fundamentals of artificial neural networks and Build your own Deep Learning project. 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. He introduces the archetypal deep learning data sets, and then you build a deep neural network together to tackle a classic machine vision problem, 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/uvyfnwwf 

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