Victor Adeyanju’s Post

Skills Needed to Learn AI 💻 What Skills Do You Need to Learn AI? 🤔 To start your AI journey, focus on building these essential skills: ✨ A solid foundation in mathematics, especially linear algebra and statistics 📐. ✨ Programming knowledge—Python is a great place to start 🐍. ✨ Understanding of machine learning frameworks like TensorFlow or PyTorch 🤖. ✨ Critical thinking and problem-solving skills to approach challenges creatively. Learning AI is a process, but with the right mindset and resources, you can achieve it! 💪 🔗 Check out our website for more guidance. #SkillsForAI #LearnMachineLearning #AIProgramming #ArtificialIntelligence #FutureSkills

🚀 Deep Dive into Support Vector Machines (SVM) and Kernel Methods 🚀 I'm excited to share a comprehensive breakdown of the Support Vector Machine (SVM) and the Kernel Trick that drive some of the most powerful models in machine learning. 🧠💻 🔍 Key Concepts Covered: Introduction to SVM: Understanding the foundation of this powerful classification algorithm. Training Process in SVM: Deriving the mathematical formulation behind the training of SVMs. Soft Margin in SVM: Handling non-linearly separable data with flexibility. The Kernel Trick: Mathematical insights into transforming data to higher-dimensional spaces, enabling complex boundaries. Dual Optimization Problem: Deriving the dual form of the optimization problem for better efficiency and flexibility. Prediction with Kernelized Models: Leveraging the kernel method for making predictions beyond linear boundaries. Representer Theorem: A crucial concept to understand the link between kernel methods and function spaces. 🧮 This work is heavily inspired by Joe Suzuki's approach to SVM and Kernel Methods in his book "Kernel Methods in Machine Learning with Python", as well as the key ideas from "Statistical Learning with Python". SVMs and kernel methods are foundational to statistical learning, offering insights into how complex relationships in data can be modeled using simple principles. 🌟 #MachineLearning #SVM #SupportVectorMachine #KernelMethods #DataScience #Statistics #JoeSuzuki #StatisticalLearning #Python #MLAlgorithms #FunctionSpace #DeepLearning #AI #Mathematics #MathematicsResearch #RandD #SouthAsianUniversity Mentorness Recruitment Service

In the revolution of artificial intelligence, it is essential to learn about it. This semester, I am taking an Artificial Intelligence course as part of my academic studies. So, I have completed the Machine Learning Specialization from Stanford University and DeepLearning.AI to gain a solid understanding of artificial intelligence and machine learning. Throughout this course, I have learned various machine learning concepts and algorithms. The course was informative, covering both the theoretical and mathematical aspects of machine learning. https://lnkd.in/gY3b8Ugq #artificial #intelligence #machine #learning #algorithm #deep #learning #stanford #university #python

Weekend Thought 😬 Pure mathematics and physics are the true foundations of everything; machine learning and AI are merely tools, primarily used for searching through historical data. But what happens when that history is flawed? Many engage in these topics simply because it’s easy to run a Python library without fully grasping whether it’s even appropriate. Don’t get lost in chasing money or buzzwords in science! If you genuinely love your field as a Engineer, the rewards will naturally follow. #AI #Engineer #Science #Money

In rapidly growing AI in real life, mathematicians must equiped strong computer science skills. As, AI based on machine learnig and machine learning is the intersection of mathematics and computer scienc.  I believe for true innovation in ML mathematician must be equiped with computaional frameworks. I recomend expertise in python for machine learning. So, learn python and train machines.  #AI #MachineLearning #DataScience #Collaboration #Tech #Mathematics #Python

🌟 Excited to share a milestone in my professional development! 🌟 I've successfully completed the certificates for 18.6501x: Fundamentals of Statistics and 6.86x: Machine Learning with Python - From Linear Models to Deep Learning, both part of the Statistics and Data Science program by MIT on the edX platform. 📈 In Fundamentals of Statistics, I gained a solid foundation in inference, methods of estimation, parametric and non-parametric hypothesis testing, Bayesian statistics and linear regression. 🤖 In Machine Learning with Python, I delved into linear models and neural networks to tackle complex machine learning challenges. We explored everything from regression and classification algorithms to unsupervised and reinforcement learning. I'm excited to apply these skills to new challenges and continue advancing in the field of data science! #MITx #edX #Statistics #MachineLearning #DataScience #Python

Thrilled to announce that I've successfully completed the Supervised Machine Learning: Regression and Classification course by DeepLearning.AI and Stanford University with the final grade of 93.7%. During this course, I delved deep into Logistic Regression and applied Gradient Descent, exploring various mathematical implementations of the cost and loss functions. I also used different python libraries like NumPy,Pandas,Scikit-learn for applying the mathematical implementations of the models developed.Additionally, I gained a comprehensive understanding of classification techniques, including overfitting and underfitting models, and the importance of regularization. Excited to leverage these new skills in tackling real-world challenges! #MachineLearning #AI #LogisticRegression #GradientDescent #ContinuousLearning #Coursera

recreated Micrograd—a lightweight autograd engine in Python—from the ground up. It required immersing myself in computational graphs, backpropagation, and gradient descent. Micrograd streamlines differentiation, making it easier to build and assess diverse machine learning models. While I didn't develop it entirely from scratch, studying and remaking the code significantly enhanced my understanding of neural network training, particularly gradient computation and optimization. Here's the link to my colab notebook: https://lnkd.in/dqT3dKZm

🚀 Just completed a fun project using PyTorch to build a simple neural network for regression! 🔥 I developed a model to fit a noisy quadratic function, demonstrating how to train a neural network from scratch using three hidden layers and live visualization with Matplotlib. 📈✨ The project showcases: 🎯 Training a neural network with ReLU activations and MSE loss. 🧠 Real-time plotting of the training process to visualize how the model learns over time. 🔧 Experimenting with different hyperparameters like learning rate, weight decay, and architecture configuration. It was an exciting way to reinforce my understanding of deep learning concepts while getting hands-on experience with PyTorch and dynamic visualization! Feel free to check out the code on GitHub 🚀👩💻 https://lnkd.in/eMsW96wc There are easier ways to solve this problem than using a Neural Network; my approach is just a complex solution to a simple problem. 😄 #MachineLearning #DeepLearning #PyTorch #DataScience #ArtificialIntelligence #NeuralNetworks #Programming #Coding #Python #Tech