Machine Learning with Python - IBM (training)

Colleagues, the “Machine Learning with Python” training from IBM will equip you to utilizeScikit-learn to build, test, and evaluate models, apply data preparation techniques and manage bias-variance trade-offs to optimize model performance, implement core machine learning algorithms, including linear regression, decision trees, and SVM, for classification and regression tasks, and evaluate model performance using metrics, cross-validation, and hyperparameter tuning to ensure accuracy and reliability. Gain highly marketable skills involving Machine Learning, Clustering, regression, classification, SciPy and scikit-learn. You’ll explore supervised learning techniques with libraries such as TensorFlow and Pandas, as well as classification methods like decision trees, KNN, and SVM, covering key concepts like the bias-variance tradeoff. The course also covers unsupervised learning, including clustering and dimensionality reduction. With guidance on model evaluation, tuning techniques, and practical projects in Jupyter Notebooks, you’ll gain the Python skills that power your ML journey. Skill-based training modules include: 1) Introduction to Machine Learning, 2) Linear and Logistic Regression, 3) Building Supervised Learning Models, 4) Building Unsupervised Learning Models, 5) Evaluating and Validating Machine Learning Models, and 6) Final Project and Exam. 

Enroll today (teams & execs welcome): https://imp.i384100.net/VxZMZ6 

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

Success awaits you! Lawrence E. Wilson - AI Academy (share & subscribe)

Convolutional Neural Networks (training)

Colleagues, in the “Convolutional Neural Networks” program you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more. You will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data. Acquire high-demand skills involving Facial Recognition System, Tensorflow, Convolutional Neural Networks, Deep Learning, Object Detection and Segmentation, and Foundations of Convolutional Neural Networks. Skill-based lessons include: 1) Deep Convolutional Models: Case Studies, 2) Object Detection, and 3) Special Applications: Face recognition & Neural Style Transfer. 

Enroll today (teams & execs welcome): https://imp.i384100.net/YRRxkK

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

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

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

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) or (Kindle) 

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

Mathematics for Machine Learning Specialization

Colleagues, in the “Mathematics for Machine Learning Specialization” from the Imperial College London you will gain high-demand skills with Eigenvalues And Eigenvectors, Principal Component Analysis (PCA), Multivariable Calculus, and Linear Algebra. Get up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them. The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge. At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning. Skill-based training modules address: 1) Machine Learning: Linear Algebra, 2) Machine Learning: Multivariate Calculus, and 3) Machine Learning: PCA. 

Enroll today (teams & execs welcome): https://imp.i384100.net/APPX1N 

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

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

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

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) or (Kindle) 

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

Generative AI for Everyone (training)

Colleagues, the “Generative AI for Everyone” program taught by AI pioneer Andrew Ng you’ll get insights into what generative AI can do, its potential, and its limitations. You’ll delve into real-world applications and learn common use cases. You’ll get hands-on time with generative AI projects to put your knowledge into action and gain insight into its impact on both business and society. Learn: What generative AI is and how it works, its common use cases, and what this technology can and cannot do. How to think through the lifecycle of a generative AI project, from conception to launch, including how to build effective prompts. The potential opportunities and risks that generative AI technologies present to individuals, businesses, and society. Gain high-demand skills in Generative AI Tools, Large Language Models, AI strategy, Generative AI and AI Productivity. Skill-based training modules include: 1) Introduction to Generative AI - How Generative AI works, LLMs as a thought partner, AI is a general purpose technology, Writing, Reading, Chatting, What LLMs can and cannot do, Tips for prompting and Image generation; 2) Generative AI Projects - Using generative AI in software applications - Trying generative AI code yourself, Lifecycle of a generative AI project, Cost intuition, Retrieval Augmented Generation (RAG), Fine-tuning,Pretraining an LLM, Choosing a model, How LLMs follow instructions: Instruction tuning and RLHF, and Tool use and agents; and 3) Generative AI in Business and Society - Day-to-day usage of web UI LLMs, Task analysis of jobs, Additional job analysis examples, New workflows and new opportunities, Teams to build generative AI software, Automation potential across sectors, Concerns about AI, Artificial General Intelligence and Responsible AI.

Enroll today (teams & execs welcome): https://imp.i384100.net/9gKrZ4

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

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

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

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) or (Kindle) 

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

Advanced Learning Algorithms (training)

Colleagues, in the “Advanced Learning Algorithms” program you will build and train a neural network with TensorFlow to perform multi-class classification, apply best practices for machine learning development so that your models generalize to data and tasks in the real world, and use decision trees and tree ensemble methods, including random forests and boosted trees. Acquire high-demand skills including: Random Forest Algorithm, Statistical Machine Learning, Deep Learning, Machine Learning Algorithms, Predictive Modeling, Artificial Neural Networks, Advanced Analytics, Business Analytics, Predictive Analytics, Decision Tree Learning, Machine Learning Methods, Data Science, Tensorflow, Machine Learning, Statistical Modeling, Computer Science, Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, and Supervised Learning. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. Training lessons address: 1) Neural Networks, 2) Neural network training, 3) Advice for applying machine learning, and 4) Decision trees.

Enroll today (teams & execs welcome): https://imp.i384100.net/POKMKj

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

Success awaits you! Lawrence E. Wilson - AI Academy (share & subscribe)

Supervised Machine Learning: Regression and Classification

Colleagues, the “Supervised Machine Learning: Regression and Classification” is part of Machine Learning Specialization from DeepLearning.AI. You will learn to build machine learning models in Python using popular machine learning libraries NumPy & scikit-learn. Also learn to build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression. Acquire high-demand skills involving Linear Regression, Regularization to Avoid Overfitting, Logistic Regression for Classification, Gradient Descent, and Supervised Learning. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.). The curriculum includes: Week 1 - Introduction to Machine Learning: Applications of machine learning, What is machine learning?, Supervised learning, Unsupervised learning, Jupyter Notebooks, Linear regression model part, Cost function formula, Visualizing the cost function and examples, Gradient descent, Implementing gradient descent, Gradient descent intuition, Learning rates, Gradient descent for linear regression, and Running gradient descent; Week 2 - Regression with multiple input variables: Multiple features, Vectorization part, Gradient descent for multiple linear regression, Feature scaling part, Feature scaling part, Checking gradient descent for convergence, Choosing the learning rate, Feature engineering, and Polynomial regression; and Week 3 - Classification: Motivations, Logistic regression, Decision boundary, Cost function for logistic regression, Simplified Cost Function for Logistic Regression, Gradient Descent Implementation, The problem of overfitting, Addressing overfitting, Cost function with regularization, Regularized linear regression, and Human-Centered AI. 

Enroll today (teams & execs welcome): https://imp.i384100.net/555bxN 

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

Success awaits you! Lawrence E. Wilson - AI Academy (share & subscribe)

ChatGPT Prompt Engineering for Developers (training)

Colleagues, take your AI skills to the next level with the “ChatGPT Prompt Engineering for Developers” program. Use API access to leverage LLMs into your own applications, and learn to build a custom chatbot. In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful applications. Using the OpenAI API, you will be able to quickly build capabilities that learn to innovate and create value in ways that were cost-prohibitive, highly technical, or simply impossible before now. This short course taught by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI) will describe how LLMs work, provide best practices for prompt engineering, and show how LLM APIs can be used in applications for a variety of tasks, including: Summarizing (e.g., summarizing user reviews for brevity) - Inferring (e.g., sentiment classification, topic extraction) - Transforming text (e.g., translation, spelling & grammar correction) - Expanding (e.g., automatically writing emails) In addition, you’ll learn two key principles for writing effective prompts, how to systematically engineer good prompts, and also learn to build a custom chatbot. All concepts are illustrated with numerous examples, which you can play with directly in our Jupyter notebook environment to get hands-on experience with prompt engineering

Enroll today (teams & executives are welcome): https://tinyurl.com/yhjxjh6v 

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

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) https://tinyurl.com/mae9ku3b or (Kindle) https://tinyurl.com/27jux34w 

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” audiobook on Audible (https://tinyurl.com/bdfrtyj2) or ebook on Kindle (https://tinyurl.com/jfntsyj2) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Kindle) https://tinyurl.com/4bmmad9k  (Audible - coming soon!)

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

Structuring Machine Learning Projects (training)

Colleagues, the “Structuring Machine Learning Projects” program is part of the Deep Learning Specialization from DeppLearning.AI. Learn to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience. Gain highly marketable skills in Decision-Making, Machine Learning, Deep Learning, Inductive Transfer, and Multi-Task Learning. Training modules include: 1) ML Strategy - Orthogonalization, Single Number Evaluation Metric, Satisficing and Optimizing Metric, Train/Dev/Test Distributions, Size of the Dev and Test Sets, When to Change Dev/Test Sets and Metric?, Human-level Performance?, Avoidable Bias, Understanding Human-level Performance, Surpassing Human-level Performance, Improving your Model Performance, and Andrej Karpathy Interview; and 2) ML Strategy - Part II - Carrying Out Error Analysis, Cleaning Up Incorrectly Labeled Data, Build your First System Quickly, then Iterate, Training and Testing on Different Distributions, Bias and Variance with Mismatched Data Distributions, Addressing Data Mismatch, Transfer Learning, Multi-task Learning, What is End-to-end Deep Learning?, Whether to use End-to-end Deep Learning, and Ruslan Salakhutdinov Interview. 

Enroll today (teams & execs welcome): https://imp.i384100.net/RGPGQa 

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

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

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

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) or (Kindle) 

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