This course provides a comprehensive introduction to the fundamental concepts of machine learning. It is designed for beginners who have little to no prior knowledge of the subject. Throughout the course, learners will explore the basic principles, techniques, and applications of machine learning, gaining the skills needed to understand and implement simple machine learning models.
Understand what machine learning is, its importance, and various real-world applications. Learn about different types of machine learning, including supervised, unsupervised, and reinforcement learning.
Learn the essential techniques for preparing data for machine learning. This includes data cleaning, normalization, and transformation. Understand the importance of handling missing values and scaling data.
Explore various supervised learning algorithms such as linear regression, logistic regression, decision trees, and support vector machines. Learn how these algorithms work and how to implement them using popular machine learning libraries.
Dive into unsupervised learning techniques, including clustering and dimensionality reduction. Study algorithms like k-means clustering, hierarchical clustering, and principal component analysis (PCA).
Understand the importance of evaluating and validating machine learning models. Learn about different metrics used for evaluation, such as accuracy, precision, recall, and F1 score. Explore techniques for cross-validation and splitting data into training and test sets.
Get introduced to the basics of neural networks and deep learning. Learn about the structure of neural networks, activation functions, and the backpropagation algorithm. Understand the concepts of training deep learning models using frameworks like TensorFlow and PyTorch.
Explore practical applications of machine learning in various fields such as healthcare, finance, and marketing. Learn how to approach real-world problems and develop machine learning solutions.