Completed supervised machine learning course by DeepLearning.AI

I am thrilled to announce that I have completed Course 1 of the Machine Learning Specialization offered by DeepLearning.AI in collaboration with Stanford Online, taught by the renowned Andrew Ng. In this course, I: ✅ Built machine learning models in Python using libraries like NumPy and scikit-learn. ✅ Gained hands-on experience with supervised learning techniques such as linear and logistic regression. ✅ Explored real-world AI applications, preparing myself for deeper machine learning and AI challenges. This program has been an incredible learning journey, equipping me with foundational skills and practical knowledge to solve challenging real-world problems with machine learning. I’m excited to continue learning and applying these concepts in my projects and career! #MachineLearning #ArtificialIntelligence #DeepLearningAI #AndrewNg #CareerDevelopment #LifelongLearning

Thrilled to announce that I’ve completed the Computer Vision course at the Information Technology Institute (ITI) It was an incredible journey diving deep into topics like Python for Machine Learning NumPy Linear Algebra Probability and Statistics for ML Data Visualization and Exploration Classic Machine Learning Introduction to Deep Learning and Introduction to Computer Vision. This course was a game-changer, enhancing my skills and knowledge in AI and Computer Vision. #AI #ComputerVision #DeepLearning #MachineLearning #ITI

🎓 Proud to Announce Completion of Course: Supervised Machine Learning: Regression and Classification 🚀 Under the expert guidance of Dr. Andrew Ng, I delved into: 👉 Building machine learning models in Python using popular machine learning libraries NumPy & scikit-learn 👉 Building & training supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression Grateful for the opportunity from DeepLearning.AI and Stanford University online. Excited to continue pushing boundaries in AI . #MachineLearning #DataScience #AndrewNg #AI

Red HOT - 29Oct Publication; Deep Learning with JAX, Google's numerical computing library. quote "The JAX numerical computing library tackles the core performance challenges at the heart of deep learning and other scientific computing tasks. By combining Google’s Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations." end-quote. Publisher: Manning, https://lnkd.in/ewEApm_R #investments, #ai, #machinelearning, #python, #trading, #investments, #cfa, #statistics, #python, #hedgefund, #quantitativefinance, #deeplearning, #quant, #money, #forex, #alternativeinvestments, #gold

I'm thrilled to share that I have successfully completed the "Supervised Machine Learning: Regression and Classification" course offered by DeepLearning.AI and Stanford University. This course provided me with hands-on experience in building machine learning models using Python, NumPy, and scikit-learn. I learned how to create and train models for prediction and binary classification tasks, including linear regression and logistic regression. A big thank you to the amazing instructors and the entire DeepLearning.AI team for creating such an impactful learning experience. Looking forward to applying these skills to solve real-world problems and contribute to the field of AI and machine learning. #MachineLearning #AI #DataScience #DeepLearning #Coursera

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

𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗝𝗲𝗻𝘀𝗲𝗻'𝘀 𝗶𝗻𝗲𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗽𝗹𝗮𝘆 𝗮 𝗿𝗼𝗹𝗲 𝗶𝗻 𝘃𝗮𝗿𝗶𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗿𝗲𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘁𝗿𝗶𝗰𝗸? 𝗙𝗶𝗻𝗱 𝗼𝘂𝘁 𝗶𝗻 𝗺𝘆 𝗹𝗮𝘁𝗲𝘀𝘁 𝘁𝘂𝘁𝗼𝗿𝗶𝗮𝗹! Delve into the world of Variational Inference and the Evidence Lower Bound (ELBO) with a comprehensive guide that covers: - 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝗗𝗲𝗿𝗶𝘃𝗮𝘁𝗶𝗼𝗻𝘀: Understand the core concepts and detailed mathematics behind VI and ELBO. - 𝗜𝗻𝘁𝘂𝗶𝘁𝗶𝘃𝗲 𝗘𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻𝘀: Grasp the intuition with simple explanations of complex ideas. - 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗲𝗱 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝗱𝗲: Implement a Variational Autoencoder (VAE) using PyTorch, showcasing key concepts like the reparameterization trick. Enhance your knowledge in Bayesian machine learning and explore how to approximate intractable posterior distributions effectively. 📌 Read the full tutorial here: https://lnkd.in/g8RB2pQf PS if the content brought you value please share it with someone who can benefit from it. #AI #MachineLearning #BayesianInference #VariationalInference #DeepLearning #DataScience #Python #VAE

🎉 Thrilled to share that I have completed the **Artificial Intelligence with Python** course led by Mukesh Rao, Director of Education Data Science at Great Learning! 🌟 Key Learnings: Explored neural network history, biological vs. artificial neurons, perceptron mechanism, and ANN architecture. Gained insights into activation functions, softmax for classification, and hands-on experience with TensorFlow 2.0, Keras, and practical demos like the MNIST dataset project. Learned forward and back propagation intricacies, loss functions, and gradient descent. Ready to apply AI skills in real-world projects! 🧠💻 #ArtificialIntelligence #Python #NeuralNetworks #MachineLearning #AI #DeepLearning #Keras #TensorFlow #DataScience #GreatLearning

Completed the course : Complete AI and Machine Learning , DataScience Bootcamp. An amazing journey of learning python, using Jupyter-notebooks, Pandas , Numpy , matplotlib , scikit-learn and google-colab with tensor flow. A journey through the world of machine learning with the lots of kaggle projects to practice as we learn the concepts of supervised learning : Heart disease prediction , House price recognition, bulldozer price prediction to image recognition using neural network and deep learning (dog breed identification) Brilliant course completed .