Top 3 Strategies to advance your TensorFlow Developer career

Colleagues, the average salary for a TensorFlow Developer is $132,215 according to ZipRecruiter.  First, Get Certified: A high quality cert from a reputable vendor or professional association may boost your income by 5%-10%. In the Deep Learning with TensorFlow program you will master popular algorithms like CNN, RCNN, RNN, LSTM, RBM using the latest TensorFlow 2.0 package in Python. Deep Learning with Tensorflow, Keras and PyTorch - build deep learning models in all the major libraries: TensorFlow, Keras, and PyTorch along with artificial neural networks. And Advanced Deployment Scenarios with TensorFlow - gain expertise in TensorFlow Extended, TF Hub, Transfer learning, Inference, Module storage, Text based model, Word embeddings, Experimenting with embeddings, Colab1m, Classify cats and dogs and Transfer learning), 3 - Tensorboard and Federated Learning. Second, Get Published. Write a 1-2 page article on Best Practices or Industry Trends for Medium, LinkedIn Articles or Technology.org. Third, Get Connected. Join the TensorFlow Forum. Sign-up for the TensorFlow Community on GitHub and subscribe to the TensorFlow User Group on LinkedIn.

Enroll in one or more programs today (teams & execs welcome): 

• Deep Learning with TensorFlow

• Deep Learning with Tensorflow, Keras and PyTorch

• Advanced Deployment Scenarios with TensorFlow

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Graphic source: Great Learning

Top 3 strategies for Machine Learning career success

Colleagues Fortune Business Insights projects total Machine Learning market growth through 2029 with a 38.8% CAGR .Glassdoor estimates the average salary for a Machine Learning Engineer at $122,963 USD. First, Get Certified: A high quality cert from a reputable vendor or professional association may boost your income by 5%-10%. Our top recommendation is Python Machine Learning Certification Training using Python that equips you with various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes, and Q-Learning. Next is AWS Certified Machine Learning - AWS Machine Learning-Specialty (ML-S) Certification exam, AWS Exploratory Data Analysis covers topics including data visualization, descriptive statistics, and dimension reduction and includes information on relevant AWS services, Machine Learning Modeling. Finally is the Machine Learning Engineer program that teaches you the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker, Deep Learning Topics within Computer Vision and NLP, Developing Your First ML Workflow, Operationalizing Machine Learning Projects, and a Capstone Project - Inventory Monitoring at Distribution Centers, Second, Get Published. Write a 1-2 page article on Best Practices or Tech Trends for Medium, LinkedIn Articles or Technology.org. Third, Get Connected. Subscribe to the Data School channel on YouTube (200k members). Join Reddit’s Machine Learning group (2.5m members. Then register for the Wolfram Machine Learning discussion forum.

Enroll in one or more programs today (teams & execs welcome): 

• Python Machine Learning Certification Training using Python

• AWS Certified Machine Learning

• Machine Learning Engineer 

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Graphic source: Forbes-Statista

Discover the Top 3 strategies for Python Developer career success

Colleagues, Python Developers according to DevSkiller earn $111,899 on average in the US. First, Get Trained. A high quality credential from a reputable vendor or professional association may boost your income by 5%-10%. The AI Programming with Python course is our first recommendation. Next is Essential Machine Learning and AI with Python and Jupyter Notebook program that shows you how AWS and Google Cloud Platform can be used to solve real-world business problems in Machine Learning and AI. Finally, the Supervised Learning - Linear Regression in Python equips you to apply Least Squares regression and its assumptions to real world data. Second, Get Published. Write a 1-2 page article on Best Practices or Python Tech Trends for Medium, LinkedIn Articles or Technology.org. And third, Get Connected. Become a Python Software Foundation member, join the Developers, Engineers & Techies group on LinkedIn (255k members) and subscribe to the Python community on Reddit (973k subscribers). 

Enroll in one or more programs today (teams & execs welcome): 

• AI Programming with Python

• Essential Machine Learning and AI with Python and Jupyter Notebook 

• Supervised Learning - Linear Regression in Python

Download your complimentary Python, TensorFlow & PyTorch - Career Transformation Guide.

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What are the Top 3 Strategies to advance your Data Science career?

Colleagues, Google Certified Professional Data Engineers earn on average $171,749 according to the Global Knowledge IT Skills and Salary Report (2021). Here are three strategies to achieve your career and income potential. First, Get Trained. A high quality credential from a reputable vendor or professional association may boost your income by 5%-10%. We recommend Python for Data Science Certification, Become a Data Architect and Spark, Ray and Python for Scalable Data Science. Second, Get Published. Write a 1-2 page article on Best Practices or Tech Trends on Medium, LinkedIn Articles or Technology.org. And third, Get Connected. Join the Kaggle forum, Data Science Central on Twitter (235k subscribers) and Data Science Meet-up (2.8m members). Together, these three strategies will enable you to become a leader in Data Science. 

Get started today (teams & execs welcome): 

• Python for Data Science Certification

• Become a Data Architect

• Spark, Ray and Python for Scalable Data Science

Download your complimentary Data Science - Career Transformation Guide.

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (share & subscribe)

Graphic source: HealthCatalyst

Become a Computer Vision Expert

Colleague, Become a Computer Vision Expert and advance your income and career growth. Master the computer vision skills behind advances in robotics and automation. Write programs to analyze images, implement feature extraction, and recognize objects using deep learning models. Learn cutting-edge computer vision and deep learning techniques—from basic image processing, to building and customizing convolutional neural networks. Apply these concepts to vision tasks such as automatic image captioning and object tracking, and build a robust portfolio of computer vision projects. Training modules - with hands-on labs include: 1) Introduction to Computer Vision - master computer vision and image processing essentials. Learn to extract important features from image data, and apply deep learning techniques to classification tasks (Project: Facial Keypoint Detection); 2) Advanced Computer Vision and Deep Learning - apply deep learning architectures to computer vision tasks. Discover how to combine CNN and RNN networks to build an automatic image captioning application (Project: Automatic Image Capturing); and 3) Object Tracking and Localization - locate objects and track them over time. These techniques are used in a variety of moving systems, such as self-driving car navigation and drone flight (Project: Landmark Detection and Tracking). 

Enroll today (teams & execs welcome): https://tinyurl.com/snuuf774 

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Data Science Certification with R

Colleagues, Glassdoor estimate the average salary of US-based Data Scientists at $118,088. This Data Science with R certification training lets you gain expertise in Machine Learning Algorithms such as K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. This Data Science with R  Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout the R Programming Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR. The curriculum addresses: 1) Introduction to Data Science with R - see how Data Science helps to analyze large and unstructured data with different tools, 2) Statistical Inference - learn statistical techniques and terminologies used in data analysis, 3) Statistical Inference - statistical techniques and terminologies used in data analysis, 4) Data Extraction, Wrangling and Exploration - sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format, 4) Introduction to Machine Learning - categories of Machine Learning and implement Supervised Learning Algorithms, 5)  Classification Techniques - Supervised Learning Techniques and the implementation of various techniques, such as Decision Trees, Random Forest Classifier, 6) Unsupervised Learning - various types of clustering that can be used to analyze the data, 6) Recommender Engines - association rules and different types of Recommender Engines, 7) Text Mining - Unsupervised Machine Learning Techniques and the implementation of different algorithms, for example, TF-IDF and Cosine Similarity, 8) Time Series - data, different component of Time Series data, Time Series modeling - Exponential Smoothing models and ARIMA model for Time Series Forecasting, and 9)  Deep Learning - learn the oncepts of Reinforcement learning and Deep learning in this module. These concepts are explained with the help of Use cases. You will get to discuss Artificial Neural Network, the building blocks for Artificial Neural Networks, and Artificial Neural Network terminologies. 

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

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Deep Learning with TensorFlow

Colleagues, Deep Learning professionals earn on average $137,989 per year according to ZipRecruiter. The Deep Learning with TensorFlow training program will equip you 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 projects. Skill-based training modules address: 1) Introduction to Deep Learning - artificial neural networks, and the breadth of transformative applications it produces. Jon also goes over how to run the code examples provided throughout the LiveLessons, and then he builds an introductory neural network with you, 2) How Deep Learning Works - main families of deep neural networks and their applications. The heart of the lesson is a high-level overview of the essential theory that underlies deep learning. To bring this theory to life, Jon shows you deep learning in action via a web application called the TensorFlow Playground, archetypal deep learning datasets, and then you build a deep neural network together to tackle a classic machine vision problem, 3) Convolutional Networks - convolutional layers and how they have 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 added to your arsenal at the end of the lesson, 4) Introduction to TensFlow - high-level deep learning API Keras to build your models. In this lesson, the 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, in Jon’s opinion, clearly the best choice from the options available. Given this, the second half of the lesson is dedicated to building your own deep learning models in TensorFlow, and 5) Improving Deep Networks - performance of your deep learning models, including by tuning model hyperparameters. The lesson concludes by discussing how to build your own deep learning project as well as outlining resources for further self-study.

Enroll today (teams & execs welcome): https://tinyurl.com/y6vek5nz 

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Data Science with Python and R

Colleagues, the Global Knowledge 2021 IT Salary Survey ranks Google Certified Professional Data Engineer $171,749 USD #1 out of all certifications. This Data Science with Python and R training program is tailored to beginner Data Scientists seeking to use Python or R for data science. This course includes fundamentals of data preparation, data analysis, data visualization, machine learning, and interactive data science applications. Students will learn how to build predictive models and how to create interactive visual applications for their line of business using the Anaconda platform. This course will introduce data scientists to using Python and R for building on an ecosystem of hundreds of high performance open source tools. Training modules address: 1) Open Data Science for Everyone, 2) Background Concepts for Open Data Science, 3) Data Wrangling with Pandas, 4) Anaconda Platform Overview, 5) Creating Interactive Visualizations with Bokeh, 6) Conda Package Management, 7) Data Processing and Visualization in R, 8) Excel and Python with Anaconda Fusion, 9) Excel and Python with Anaconda Fusion, 10) Databases and Distributed Data with Mosaic, 11) Distributed and Parallel Computing with Dask. 

Enroll today (teams & execs welcome): https://tinyurl.com/2p83vcek 

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Data Structures, Algorithms and Machine Learning Optimization

Colleagues, the Data Structures, Algorithms, and Machine Learning Optimization program provides you with a functional, hands-on understanding of the essential computer science for machine learning applications. Learn "big O" notation to characterize the time efficiency and space efficiency of a given algorithm,  use Python data structures, including list-, dictionary-, tree-, and graph-based structures, understand the essential algorithms for working with data, including those for searching, sorting, hashing, and traversing, implement statistical and machine learning approaches to optimization differ, and why you would select one or the other for a given problem you're solving, grasp versatile (stochastic) gradient descent optimization algorithm works, and familiarize yourself with the "fancy" optimizers that are available for advanced machine learning approaches. Skill-based training modules cover: 1) Orientation to Data Structures and Algorithms - Machine Learning Foundations Series, A Brief History of Data and Algorithms, and their Applications to Machine Learning; 2) "Big O" Notation - Constant, Linear and Polynomial  Time, Common Runtimes, Best versus Worst Case scenarios; 3) List-Based Data Structures - Lists, Arrays, Linked Lists, Doubly-Linked Lists, Stacks, Queues, Deques; 4) Searching and Sorting - Binary Search, Bubble-Merge-Quick Sorts; 5) Sets and Hashing - Maps and Dictionaries, Sets, Hash Functions, Collisions, Load Factor, Hash Maps, String Keys, Hashing in ML; 6) Trees - Decision Trees, Random Forests, XGBoost: Gradient-Boosted Trees; 7) Graphs - Directed versus Undirected Graphs, DAGs: Directed Acyclic Graphs, Pandas DataFrames; 8) Machine Learning Optimization - Statistics versus Machine Learning - Objective Functions, Mean Absolute Error, Mean Squared Error, Minimizing Cost with Gradient Descent, Gradient Descent from Scratch with PyTorch, Critical Points, Stochastic Gradient Descent, Learning Rate Scheduling, Maximizing Reward with Gradient Ascent; and 9) Fancy Deep Learning Optimizers - Jacobian Matrices, Second-Order Optimization and Hessians, Momentum, and Adaptive Optimizers.

Enroll today (teams & execs welcome): https://tinyurl.com/yc2dfb8f 

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Deep Reinforcement Learning

Colleagues, the Deep Reinforcement Learning training program will equip you with skills that are powering amazing advances in AI. Then start applying these to applications like video games and robotics. Learn cutting-edge deep reinforcement learning algorithms—from Deep Q-Networks (DQN) to Deep Deterministic Policy Gradients (DDPG). Apply these concepts to train agents to walk, drive, or perform other complex tasks, and build a robust portfolio of deep reinforcement learning projects. Students should have experience with Python, probability, machine learning, and deep learning. Skill-based training modules include: 1) Foundations of Reinforcement Learning - master the fundamentals of reinforcement learning by writing your own implementations of many classical solution methods, 2) Value-Based Methods - apply deep learning architectures to reinforcement learning tasks. Train your own agent that navigates a virtual world from sensory data (Project: Navigation), 3) Policy-Based Methods - learn the theory behind evolutionary algorithms and policy-gradient methods. Design your own algorithm to train a simulated robotic arm to reach target locations (Project: Continuous Control), 4) Multi-Agent Reinforcement Learning - apply reinforcement learning methods to applications that involve multiple, interacting agents. These techniques are used in a variety of applications, such as the coordination of autonomous vehicles (Project: Collaboration and Competition).

Enroll today (teams & execs welcome): https://tinyurl.com/4d27236r 

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