(offered by deeplearning.ai)

Course 1: Supervised Learning: Regression and Classification

• Topics Covered: Classification & Regression machine learning problems, over & under fitting, gradient descent, MSE, MAE and logistic loss functions, Regularization, Polynomial Regression & Feature Maps
• Projects & Code: https://github.com/Tahir001/Artificial-Intelligence/tree/main/Machine_Learning_Specialization
• Course Notes: https://drive.google.com/file/d/19nIMqQWXCekq6_nKAfzayyoS_8XXv3o3/view?usp=sharing

Course 2: Advanced Learning Algorithms

• Topics Covered: Neural Networks (ANNs), forward and backward propagation (Training ANNs), Activation functions, and Decision Trees, Multi-Class Classification, Xgboost
• Projects & Code: https://github.com/Tahir001/Artificial-Intelligence/tree/main/Machine_Learning_Specialization
• Course Notes: https://drive.google.com/file/d/19NQocllfY20nIuqsRN7E2lrsafHZSVjO/view?usp=sharing

Course 3: Unsupervised Learning, Recommendation Systems, Reinforcement Learning

• Topics Covered: Unsupervised Learning Algorithms, Dimensionality Reduction with PCA, K-Means Clustering, Anomaly Detection, Building Recommendation Systems, Deep Reinforcement Learning Models
• Projects & Code: https://github.com/Tahir001/Artificial-Intelligence/tree/main/Machine_Learning_Specialization
• Course Notes: https://drive.google.com/file/d/1E53zmusXCXmpfbLOeByCOp7qTXV6ld7l/view?usp=sharing