, This course is focus on the theoretical aspects of the recent deep learning methods.Section 1: Introduction to Machine learning & Deep learning  Lecture 1: Introduction to Deep learning  Brief history of Deep learning  Motivation  Lecture 2: What is Machine Learning?  Machine leaning Definition  Traditional Programming vs Machine learning  AI vs Machine learning vs Deep learning  Lecture 3: Types of Machine Learning  Supervised, unsupervised, and reinforcement learning  Classification vs Regression  Clustering and dimensionality reduction  Lecture 4: Machine Learning & Deep learning Applications  Lecture 5: Steps to Build a Machine Learning System  Data collection, feature extraction, modelling, estimation, and validation.  for example, how to develop an image categorization system.  Lecture 6: K-Nearest Neighbors (KNN) ModelSection 2: Linear Regression  Lecture 7: Univariate Linear Regression  Lecture 8: Cost Function Intuition  Lecture 9: Gradient Descent Algorithm  Lecture 10: Linear Regression with Multiple VariablesSection 3: Logistic Regression  Lecture 11: Introduction to Logistic Regression Lecture 12: Cost function  Lecture 13: Multi-Class ClassificationSection 4: Neural Networks  Lecture 14: Introduction to Neural Networks Part 1  Definition of Neural Networks  Artificial Neuron  Types of Activation Functions  Lecture 15: Introduction to Neural Networks Part 2  Neural Network Architectures  Capacity of Single NeuronNeural Network  Multi-layer Neural Networks  Softmax Activation Function  Lecture 16: Biological Neural Networks