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.

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)

Divide and Conquer, Sorting and Searching, and Randomized Algorithms (training)

Colleagues, the “Divide and Conquer, Sorting and Searching, and Randomized Algorithms” program is part of the Algorithms Specialization from Stanford University with over 244k students enrolled online. The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts). Skill-based training lessons include: Week 1 - Why Study Algorithms?, Integer Multiplication, Karatsuba Multiplication, Merge Sort: Motivation and Example, Merge Sort: Pseudocode and Analysis, Guiding Principles for Analysis of Algorithms, The Gist, Big-Oh Notation, Big Omega and Theta; Week 2 - O(n log n) Algorithm for Counting Inversions, Strassen's Subcubic Matrix Multiplication Algorithm, O(n log n) Algorithm for Closest Pair, Motivation, Formal Statement, Proof I and II, and Interpretation of the 3 Cases; Week 3 - Partitioning Around a Pivot, Correctness of Quicksort, Choosing a Good Pivot, Analysis (A Decomposition Principle, The Key Insight and Final Calculations), and Probability Review; and Week 4 - Randomized Selection (Algorithm and Analysis), Deterministic Selection - Algorithm, Omega(n log n) Lower Bound for Comparison-Based Sorting, Graphs and Minimum Cuts, Graph Representations, Random Contraction Algorithm, Analysis of Contraction Algorithm and Counting Minimum Cuts. 

Enroll today (teams & execs welcome): https://imp.i384100.net/qz4b9O 

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)

CS50 Intro to Artificial Intelligence - Python (HarvardX - training)

Colleagues, in the “CS50's Introduction to Artificial Intelligence with Python” course from HarvardX will learn to use machine learning in Python in this introductory course on artificial intelligence. This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, machine learning, large language models, and other topics in artificial intelligence as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own. You will learn graph search algorithms, adversarial search, knowledge representation, logical inference, probability theory, Bayesian networks, Markov models, constraint satisfaction, machine learning, reinforcement learning, neural networks, and natural language processing.

Enroll today (teams & execs welcome): http://edx.sjv.io/qz4bKq 

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)