# Machine Learning Tutorial in Python

## Course Description

Machine Learning Tutorial in Python is an introductory course designed to equip learners with the basic knowledge and skills required to build and deploy machine learning models using Python programming language. The course is aimed at beginners who are interested in exploring the field of machine learning and want to learn how to use Python for data analysis and machine learning tasks. Throughout the course, learners will be introduced to various machine learning concepts, including supervised and unsupervised learning, classification, regression, clustering, and dimensionality reduction. They will also learn how to use popular Python libraries such as NumPy, Pandas, Scikit-learn, and TensorFlow to build and train machine learning models. The course begins with an overview of machine learning and its applications, followed by an introduction to Python programming. Learners will learn how to install Python and the necessary libraries required for machine learning tasks. They will also learn how to load and manipulate datasets using NumPy and Pandas. The next section of the course focuses on supervised learning, where learners will learn how to build and train classification and regression models using Scikit-learn. They will also learn how to evaluate the performance of these models using various metrics such as accuracy, precision, recall, and F1 score. The course then moves on to unsupervised learning, where learners will learn how to use clustering algorithms such as K-means and hierarchical clustering to group similar data points together. They will also learn how to use dimensionality reduction techniques such as Principal Component Analysis (PCA) to reduce the number of features in a dataset. Finally, the course covers deep learning, where learners will learn how to build and train neural networks using TensorFlow. They will learn how to create basic neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) for image recognition, natural language processing, and time-series analysis. Throughout the course"Machine Learning Tutorial in Python", learners will have access to practical exercises and quizzes that will test their understanding of the concepts covered. By the end of the course, learners will have a solid understanding of machine learning and the ability to build and deploy machine learning models using Python. Author: edureka!