This introductory course covers the basics of Machine Learning and Deep Learning through video tutorials. It is divided into three main modules: Introduction to Machine Learning, Deep Learning Basics, and Additional ML Resources. The first module introduces concepts such as Supervised and Unsupervised Machine Learning, Bias and Variance, and Evaluation Metrics. The second module delves into Deep Learning, explaining its relationship with Machine Learning and discussing topics like Activation Functions, Backpropagation, Regularization, and Batch Normalization. Finally, the course provides additional resources for further learning, including popular ML platforms, podcasts, blogs, YouTube accounts, and conferences to attend.