Mattia Ferrini’s Post

More on mathematics of machine learning. From Linear Algebra and its Python applications, we're shifting gears to explore Probability! 📊🔍 let's take a step toward understanding the role of probability in shaping decision-making in algorithms. Today, we cover -  ✅ Basic Probability Concepts ✅ Types of Events (Independent, Dependent, and Mutually Exclusive) ✅ Theoretical vs. Empirical Probability https://lnkd.in/d93UFK7z #Probability #DataScience #AI #ML #Research #LearningJourney #MathForML #Raven-R

I am thrilled to share that I have successfully completed a 4-month course in Artificial Intelligence / Machine Learning with Pregrad .This intensive program ran from April 5, 2024, to August 5, 2024, and has been an incredible journey of learning and growth. #python #ml #ai #npm #datacleaning

With all the LLM hype around it usually happens to me, being philosophically a convinced Bayesian, that people is not interested anymore in this approach. However, I very well know that trends come and go, and people will soon discover the plateau of LLMs... Instead of knowing each new Generative AI model, I challenge you to discover something new, something that will hopefully change your mindset, your thinking. You will learn to think probabilistically, you will learn lots of useful concepts and practical tools that can be used in a wide array of scenarios. Try something different and discover, very easily and at the same time with lots of practice and code chunks, how to make Bayesian analysis with Python in this book. Honestly I wish that I had this book before my PhD on Bayesian optimization. It would have made things easier. You will learn here the fundamental concepts on Bayesian statistics, programming probabilistically, comparing models and even Gaussian processes!!! Which are one the main tools of Bayesian optimization! A very recommended book.

LLM is great innovation in NLP world. But, still now it's in very initial stage. Due the unnecessary hype we are missing many significant parts of Analytics world. Great sharing! #bayesian #statistics #datascience

Mathematics is the backbone of machine learning. When we studied math during our undergraduate years, we might not have realized its real-time use of applications, but machine learning models heavily rely on maths. In particular, calculus plays a key role in cost-effectiveness and error loss functionalities. Python offers mutiple libraries for implementing calculus functions. #1: Sympy #2: NumPy #3: JAX. Among these, I found JAX to be easy to use and efficient in performance but Sympy also good option for Symbolic computations. #ai #generativeai #openai

As part of my recent exploration of graph theory and algorithm design, I developed a Python-based program to dynamically generate and solve complex mazes. 🔍 Maze Generation: Implemented a maze generation algorithm using randomized depth-first search (DFS), creating intricate mazes that are visualized in real-time using Pygame. 🛤️ Pathfinding: Solved the generated mazes using Dijkstra’s algorithm, demonstrating proficiency in advanced graph data structures and algorithms. This project involved optimizing the pathfinding process by: Efficiently building and navigating graph representations of mazes. Diagnosing and resolving algorithmic challenges such as infinite loops. Enhancing the robustness of the solution by refining end conditions and node processing. This project not only expanded my knowledge of Python and Pygame but also reinforced key concepts in AI-driven problem-solving and optimization. This project was part of my Game Artificial Intelligence course at the University of Essex, taught by Dr. Michael Fairbank #Python #AI #AlgorithmDesign #GraphTheory #GameDevelopment #Pathfinding #Dijkstra #MazeGeneration #ProblemSolving #Pygame

Successfully completed the "Master Machine Learning using Python" 30 Hours long course offered by Great Learning Academy. "Topics Covered in this Course are:" Supervised Learning Mathematical/Feature Space Linear Regression and Pearson’s Coefficient Descriptive Analysis on the Dataset Missing Value Imputation Bivariate Analysis Error Analysis and Adjusted R2 Fluke Correlation Classification Models – Logistic and Naïve Bayes Classification Algorithm – Logistic Regression Bayes’ Theorem Ensemble Techniques Decision Trees Introduction Unsupervised Learning Clustering – K-Means Clustering Principal Component Analysis (PCA) Featurization, Model Selection, and Tuning Feature Engineering Model Tuning and Performance #python #Artificialintelligence #AI #Datascience #ML Great Learning #GreatLearningAcademy #greatlearning #glacertificate

Machine Learning Mathematics Roadmap! This well-organized reference covers a wide range of topics, including probability, statistics, and linear algebra, and is specifically designed to help readers understand machine learning algorithms. Highlights include possible interview questions for machine learning engineers and real-world uses of mathematical formulas in Python. Furthermore, a plethora of free resources from reliable websites like Stanford and Khan Academy are included. This roadmap offers insights into how mathematics forms the backbone of machine learning. It's a blend of theory and practical application that's rare to find. What's your take on the importance of mathematics machine learning? I hope you find it fascinating, and please remember to give me thanks if you do. If you find it helpful, kindly like, share, and follow me for more posts like this in the future. 🔍Follow Sunil Jangra for more content. Happy Learning!! #statistics #linearalgebra #calculus #datascience #machinelearning

I am glad to share that I have completed the ‘HarvardX: Machine Learning and AI with Python’ course on edX, offered by Harvard University, renowned for its excellence in teaching, learning, and research. Key Learnings: ▪ Explored advanced data science challenges using sample data sets, decision trees, random forests, and machine learning models. ▪ Evaluated variable importance for enhanced model interpretation. ▪ Bagging (Bootstrap Aggregating): Leveraging out-of-bag estimation for reliable performance evaluation. ▪ Examined machine learning results to recognize data bias and avoid underfitting or overfitting. ▪ Boosting (Gradient Boosting, AdaBoost) ▪ Enhanced my Python skills. #MachineLearning #AI #Python #DataScience #HarvardUniversity #edX #HarvardX 

I'm excited to share that I've successfully completed the Artificial Intelligence course offered by Adverk technologies .This course has been a fantastic journey, deepening my understanding of AI, machine learning, and data science! I've gained hands-on experience in: Python programming language Machine learning Data science A big thank you to Adverk technologies for their guidance and support throughout the course. #ArtificialIntelligence #MachineLearning #DataScience #AI #LearningJourney #ContinuousLearning