Sudharsan D S’ Post

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

🚀 Completed Lecture 2: Uncertainty from CS50's Introduction to Artificial Intelligence with Python, taught by Brian Yu, Senior Preceptor in Computer Science at Harvard University! In this lecture, we dived deep into the world of probability. We started with foundational concepts like Unconditional and Conditional probability with examples, then explored formulas and the significance of random variables and independence. A key highlight was understanding Bayes' rule, along with principles like negation, inclusion-exclusion, marginalization, and conditioning. We also covered: - Inference(both by enumeration and approximate) - Sampling and likelihood weighting - Various models including Bayesian Networks, transition models, sensor models, and the Hidden Markov Model (HMM). We also explored the Markov assumption and Markov chains, built using transition and sensor models, leading us to tasks like: 1. Filtering 2. Prediction 3. Smoothing 4. Most likely explanation The lecture was packed with code and visual examples, making complex concepts much easier to grasp. Super excited to keep learning and apply these in real-world AI applications! 💻✨ #CS50 #ArtificialIntelligence #AI #MachineLearning #Probability #BayesianNetworks #MarkovModel #Python #HarvardUniversity#harvard

ID: FE/23/50126797 Name: Muhammad Sunusi Abubakar Track: AI/ML Week 20 weekly Reflection MY WEEKLY REFLECTION Achievement: Over the past 11 weeks, my proudest accomplishment has been mastering the fundamentals of machine learning algorithms, Python, and data analysis. This achievement has given me a solid foundation in technical skills and a deeper understanding of AI concepts, which I initially found challenging. Challenge: My biggest challenge was adapting to a fast-paced learning environment and understanding complex concepts. To tackle this, I adopted a more organized study routine, breaking down complex topics into smaller, manageable sections and using additional resources, such as online tutorials and peer discussions, to deepen my understanding. Next Steps: Moving forward, I plan to keep improving my skills in deep learning, model evaluation, and generative AI. By exploring these skills, I hope to enhance my contributions to future projects and build confidence in my technical capabilities. #My3MTT #3MTTWeeklyReflection @3mttNigeria @IHSTowers @NITDANigeria

Excited to announce my completion of the Supervised Machine Learning: Regression and Classification certificate from Stanford University via Coursera, under the expert guidance of Andrew Ng .Delved into Linear Regression, Regularization techniques, Logistic Regression for Classification, Gradient Descent, and more. Proficient in Python programming with NumPy and scikit-learn for data analysis and visualization. Ready to apply these skills to real-world challenges in AI and ML. #MachineLearning #AI #Stanford #Coursera

🌟 Essential Skills to Kickstart Your AI Journey 🌟 If you’re ready to learn AI, here are the key skills you’ll need: Programming Knowledge: Python is essential, but familiarity with other languages like R or Java can be helpful. Mathematics: A good grasp of algebra, calculus, and statistics will help you understand machine learning algorithms. Data Handling: Knowing how to work with large datasets and data manipulation is crucial. Problem-Solving: AI is all about finding solutions, so strong analytical skills are a must. With these skills, you’ll be well-equipped to dive into the world of AI! #AI #TechSkills #LearningAI #ArtificialIntelligence #DataScience

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

Excited to share that I have successfully completed the CS50’s Introduction to Artificial Intelligence with Python course offered by Harvard Online. This comprehensive introduction to AI has truly ignited my passion and drive for this fascinating field. A special shoutout to Brian Yu for their incredible teaching and inspiration throughout the course. Your insights and enthusiasm have made this learning journey unforgettable! From mastering game-playing engines and handwriting recognition to delving into machine translation, graph search algorithms, and reinforcement learning, this course has provided me with a robust foundation in AI and machine learning. I’m eager to apply these skills in real-world projects and continue exploring the limitless possibilities of artificial intelligence. #AI #ArtificialIntelligence #MachineLearning #Python #HarvardOnline #CS50 #Innovation #Tech

I’m thrilled to share that I’ve obtained a new certification: Supervised Machine Learning: Regression and Classification from DeepLearning.AI and Stanford University🎓 This course provided deep insights into linear, logistic, and polynomial regression, feature scaling, feature engineering, regularization techniques, and various loss/cost functions such as sigmoid and MSE. The hands-on experience with programming and mathematical constructs was truly enriching! Feeling more equipped to take on the challenges of machine learning and excited for what’s next. 💡🚀 #MachineLearning #DeepLearning #Python #AI

Dear network, I'm delighted to share that I've successfully completed the Machine Learning Specialization by DeepLearning.AI on Coursera! This comprehensive program has provided me with a strong foundation in machine learning. It covers topics such as Supervised Machine Learning: Regression and Classification, Advanced Learning Algorithms, Unsupervised Learning, Recommenders, and Reinforcement Learning. I'm thrilled to apply my newfound knowledge to upcoming projects and continue learning in the dynamic field of machine learning. This experience has not only broadened my understanding but also reinforced my passion for leveraging technology to solve real-world problems. #dataanalytics #python #SupervisedMachineLearning #regression #classification

🚀 Exploring Naive Bayes in Machine Learning 🚀 Naive Bayes is a powerful yet simple probabilistic classifier based on Bayes' theorem. Despite its simplicity, it's incredibly effective in real-world applications like spam filtering, sentiment analysis, and recommendation systems. 💡 Key Concept: It assumes that the features are independent, given the class label—hence "naive." But don't let the name fool you; this algorithm is a workhorse in the ML toolkit. 🔍 Why It Works: Even when the independence assumption is violated, Naive Bayes often performs surprisingly well in practice, making it a go-to method for text classification and more! #MachineLearning #DataScience #NaiveBayes #AI #Tech Python Coding