Generative AI: Enhance your Data Analytics Career (training)

Colleagues, in the “Generative AI: Enhance your Data Analytics Career” program you will learn to describe how you can use Generative AI tools and techniques in the context of data analytics across industries, implement various data analytic processes such as data preparation, analysis, visualization and storytelling using Generative AI tools, evaluate real-world case studies showcasing the successful application of Generative AI in deriving meaningful insights, and analyze the ethical considerations and challenges associated with using Generative AI in data analytics. Gain high-demand skills in Data Analysis, Querying Databases, Data Generation, Generative AI, and Data Augmenting. This course unravels the potential of generative AI in data analytics and provides in-depth knowledge of the fundamental concepts, models, tools, and generative AI applications regarding the data analytics landscape. You will examine real-world applications and use generative AI to gain data insights using techniques such as prompts, visualization, storytelling, querying and so on. In addition, you will understand the ethical implications, considerations, and challenges of using generative AI in data analytics across different industries. You will acquire practical experience through hands-on labs where you will leverage generative AI models and tools such as ChatGPT, ChatCSV, Mostly.AI, and SQL through AI. Finally, you will apply the concepts learned throughout the course to a data analytics project. Also, you will have an opportunity to test your knowledge with practice and graded quizzes and earn a certificate. Skill-based training modules include: 1) Data Analytics and Generative AI, 2) Use of Generative AI for Data Analytics, and 3) Final Project and Exam. 

Enroll today (teams & execs welcome): http://imp.i384100.net/QjJM66 

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)

Sequence Models (training)

Colleagues, the “Sequence Models” program is part of the Deep Learning Specialization from DeepLearning.AI. You will be equipped with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more. you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering. Gain expertise in Gated Recurrent Units (GRU), Recurrent Neural Networks, Natural Language Processing, Long Short Term Memory (LSTM), and Attention Models. Skill-based lessons include: 1) Recurrent Neural Networks, 2) Natural Language Processing & Word Embeddings, 3) Sequence Models and Attention Mechanism, and 4) Transformer Networks.

Enroll today (teams & execs welcome): https://imp.i384100.net/Wyy3eM

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)

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)