Software Development - Career Transformation Guide

Developers, the new Software Development - Career Transformation Guide includes valuable information that enables you to accelerate your career growth and income potential - Career opportunities, Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Forums and Communities. The guide covers C, Containers, Git and GitHub, Java, Mobile (iOS & Android), SQL and Web Development (front-end). There are separate guides for: 1) Python, R, TensorFlow and PyTorch and 2) Linux and Open Source. Glassdoor reports the average Software Developer salary across the US at $97,763. According to ZipRecruiter there are currently 779,726 Software Developer positions open in the US alone. In addition to “language and toolset-specific” demand and corresponding salaries there is a widespread demand for “Fullstack” Developers (front-end/back-end) in the Web Development, Artificial Intelligence-Machine Learning and Blockchain-Ethereum arenas. This guide focuses on C, Containers (Docker & Kubernetes), Java, GitHub, SQL and front-end Full Stack (including JavaScript, Angular, React, Node.js, HTML and XML.) and Mobile (iOS & Android).

Review and enroll today (teams & execs are welcome): https://tinyurl.com/2zw9jbym 

Download your free Software Development - Career Transformation Guide. 

Much career success, Lawrence E. Wilson - Online Learning Central (share with your team) 

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

Top 3 Python for Data Science training programs

Dev colleagues, the average salary for a Python developer is $120,968 in the US according to Salary Expert. Here are 3 top-rated programs for career and income growth. First, Interpreting Data Using Statistical Models with Python. Gain the ability to go one step beyond visualizations and basic descriptive statistics, by harnessing the power of inferential statistics. First, you will learn how hypothesis testing, which is the foundation of inferential statistics, helps posit and test assumptions about data. Next, discover how the classic t-test can be used in a variety of common scenarios around estimating means. Also learn about related tests such as the Z-test, Pearson’s Chi-squared test, Levene’s test and Welch’s t-test for dealing with populations that have unequal variances. Second is the Statistics with Python training by Code Academy that focuses on mean, median, mode, standard deviation, and variance of different datasets. Not only will you learn how to calculate these statistics, but you will learn how to interpret them. By getting an understanding of what these statistics represent, you will be able to better describe your own datasets. And third, Applied Data Scientist with Python. Solve data science problems, software and data engineering for  data scientists, experiment design and recommendations, data science projects. Skill-based training modules with hands-on projects. Solving Data Science Problems, Software Engineering for Data Scientists, Data Engineering for Data Scientists, and Experiment Design.

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

Download your complimentary Python - Career Transformation Guide.

View the Developer’s Guide from the Python Software Foundation.

Career success awaits you, Lawrence Wilson - Artificial Intelligence Academy (share with your team)

Data Science Masters Program

Colleagues, the “Data Science Masters Program” makes you proficient in tools and systems used by Data Science Professionals. It includes training on Statistics, Data Science, Python, Apache Spark & Scala, Tensorflow and Tableau. The curriculum has been determined by extensive research on 5000+ job descriptions across the globe. Extensive Program with 13 Courses - 250+ Hours of Interactive Learning - 6+ Projects and 50+ Assignments. Training modules include: 1) Python Statistics for Data Science Course: Designed to provide learners with a comprehensive understanding of how to perform statistical analysis and make data-driven decisions. Through a series of interactive lessons and hands-on exercises, you will learn how to conduct hypothesis testing, perform regression analysis, and many more. This course is ideal for anyone looking to enhance their data science skills and gain a deeper understanding of statistics. This course will provide you with the knowledge you need to succeed in the rapidly growing field of data science, 2) Data Science with Python Certification Course: accredited by NASSCOM, aligns with industry standards, and is approved by the Government of India. This Data Science with Python course will teach you fundamental to advanced Data Science concepts such as data operations, file operations, object-oriented programming, Pandas, Numpy, and Matplotlib. Regression, clustering, decision trees, random forests, Naive Bayes, statistics, time series, supervised, unsupervised, and reinforcement learning methods will also be covered, 3) PySpark Certification Training Course: curated by top industry experts to help you master skills that are required to become a successful Spark developer using Python. This PySpark training will help you to master Apache Spark and the Spark ecosystem, which includes Spark RDDs, Spark SQL, Spark Streaming and Spark MLlib along with the integration of Spark with other tools such as Kafka and Flume. Our PySpark online course is live, instructor-led & helps you master key PySpark concepts with hands-on demonstrations. This PySpark training is fully immersive, where you can learn and interact with the instructor and your peers, 4) Artificial Intelligence Certification Course helps you master essentials of text processing and classifying texts along with important concepts such as Tokenization, Stemming, Lemmatization, POS tagging and many more. You will learn to perform image pre-processing, image classification, transfer learning, object detection, computer vision and also be able implement popular algorithms like CNN, RCNN, RNN, LSTM, RBM using the latest TensorFlow 2.0 package in Python. This course is curated by the industry experts after extensive research to meet the latest industry requirements and demands, and 5) Tableau Certification Training Course, a comprehensive program that aims to develop proficiency in Business Intelligence, Data Visualization, and reporting techniques. It covers Tableau Prep Builder, Tableau Desktop, Charts, LOD expressions, and Tableau Online. Real-life industry use cases in Retail, Entertainment, Transportation, and Life Sciences provide practical experience to create meaningful data visualizations. Plus multiple high quality elective courses.

Enroll today (teams & executives are welcome): https://tinyurl.com/36vxr3nj 

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Here is the “Data-Driven Organizations” book series:  

1 - Data-Driven Decision-Making  (Audible) (Kindle)

2 - Implementing Data Science Methodology: From Data Wrangling to Data Viz (Audible) (Kindle)

Much career success, Lawrence E. Wilson - AI Academy (share with your team)

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 

Recommended reading: Data-Driven Organizations book series:

1 - Data-Driven Decision-Making (Kindle) (Audible)

2 - Implementing Data Science Methodology: From Data Wrangling to Data Viz (Kindle) (Audible)

And download your complimentary AI-ML-DL - Career Transformation Guide.

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

Artificial Intelligence Through Algorithmic Information Theory

Colleagues, with the “Artificial Intelligence Through Algorithmic Information Theory” you will be able to see machine learning, reasoning, mathematics, and even human intelligence as abstract computations aiming at compressing information. This new power of yours will not only help you understand what AI does (or can’t do!) but also serve as a guide to design AI systems. Associated skills: Computer Science, Probability Theories, Aesthetics, Turing Machine, Artificial Neural Networks, Artificial Intelligence, Information Theory, Basic Math, Cognitive Science, Innovation, Planning, Probability, Machine Learning. Learn to measure information through compression, compare algorithmic information with Shannon’s information, detect languages through joint compression, use the Web to compute meaning similarity, how probability and randomness can be defined in purely algorithmic terms and how algorithmic information sets limits to the power of AI (Gödel’s theorem), a criterion to make optimal hypotheses in learning tasks, a method to solve analogies and detect anomalies, a new understanding of machine learning as a way to achieve compression, Why unexpected means abnormally simple, Why coincidences are unexpected, and Why subjective information and interest are due to complexity drop and why relevance, aesthetics, emotional intensity and humor rely on coding. Skill-based training modules include: 1) Describing data - Complexity as code length, Conditional Complexity, 2) Measuring Information - Complexity and frequency, Meaning distance, 3) Algorithmic information & mathematics - Algorithmic probability, Randomness, Gödel’s theorem, 4) Machine Learning and Algorithmic Information - Universal induction: MDL, Analogy & Machine Learning as complexity minimization, and 5) Subjective information - Simplicity & coincidences, Subjective probability and Relevance.

Enroll today (teams & executives are welcome): https://fxo.co/HFFb 

Download your free AI-ML-DL - Career Transformation Guide.

“Transformative Innovation” book series for your listening-reading pleasure: 

 

1 - ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity (Audible) (Kindle)

2 - ChatGPT - The Era of Generative Conversational AI Has Begun (Audible) (Kindle) 

3 - The Race for Quantum Computing  (Audible) (Kindle) 

Much career success, Lawrence E. Wilson - AI Academy (share with your team)

Machine Learning with Python: From Linear Models to Deep Learning (training)

Colleagues, this new training program “Machine Learning with Python: From Linear Models to Deep Learning” focuses on the principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning, implementing and analyzing models such as linear models, kernel machines, neural networks, and graphical models, choosing suitable models for different applications, and implementing and organizing machine learning projects, from training, validation, parameter tuning, to feature engineering. Lectures include: 1) Linear classifiers, separability, perceptron algorithm, 2) Maximum margin hyperplane, loss, regularization, 3) Stochastic gradient descent, over-fitting, generalization, 5) Linear regression, 6) Recommender problems, collaborative filtering, 7) Non-linear classification, kernels, 8) Learning features and  Neural networks, 9) Deep learning, back propagation, 10) Recurrent neural networks, 11) Generalization, complexity, VC-dimension, 12) Unsupervised learning and clustering, 13) Generative models, mixtures, 14) Mixtures and the EM algorithm, 15) Learning to control: Reinforcement learning, 16) Reinforcement learning continued, and 17) Natural Language Processing applications. Projects cover: Automatic Review Analyzer, Digit Recognition with Neural Networks, and Reinforcement Learning. 

Enroll today (teams & executives are welcome): https://fxo.co/HO1v 

Download your free AI-ML-DL - Career Transformation Guide.

“Transformative Innovation” book series for your listening-reading pleasure: 

 

1 - ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity (Audible) (Kindle)

2 - ChatGPT - The Era of Generative Conversational AI Has Begun (Audible) (Kindle) 

3 - The Race for Quantum Computing  (Audible) (Kindle) 

Much career success, Lawrence E. Wilson - AI Academy (share with your team)