[Free] Learn Fundamentals Of Programming In Machine Learning
Gain Hands-On Experience with Popular Machine Learning Models – Free Course
What you’ll learn
- Grasp fundamental principles underlying machine learning – including supervised learning, unsupervised learning, and reinforcement learning
- Learn programming fundamentals necessary for machine learning, with a focus on Python programming language
- Learn techniques for handling and preprocessing data, including cleaning, transforming, and scaling datasets.
- Learn how to evaluate the performance of machine learning models using various metrics and techniques – like accuracy, precision
Requirements
- Python programming experience and computer with installation privileges
Description
In today’s data-driven world, Machine Learning (ML) has become an indispensable tool for extracting insights and making informed decisions. This comprehensive course is designed to equip you with the fundamental knowledge and practical skills needed to navigate the exciting realm of Machine Learning.Through this course, you will embark on a journey that begins with an introduction to the core concepts of ML, including its types, applications, workflow, and challenges. You will gain a solid understanding of the ML process, from data preparation to model deployment.
The course delves into various supervised learning algorithms, such as linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and naive Bayes. You will learn how to implement these algorithms, evaluate their performance, and apply them to real-world scenarios.
Unsupervised learning techniques, including clustering algorithms like K-means and dimensionality reduction methods, will also be covered. These techniques are essential for exploring and understanding the underlying patterns in data without relying on labeled examples.
Cross-validation, a crucial aspect of model evaluation, will be explored in-depth. You will learn about different evaluation metrics for classification and regression tasks, and how to apply cross-validation techniques to ensure the robustness and generalizability of your models.Furthermore, the course will introduce you to the concept of bias-variance tradeoff and regularization techniques, which are essential for optimizing model performance and preventing overfitting or underfitting. You will also explore ensemble methods, such as random forests and gradient boosting, which combine multiple models to improve overall accuracy and robustness.
Finally, the course will provide an introduction to the exciting world of deep learning and neural networks. You will gain an understanding of convolutional neural networks (CNNs) and their applications in areas like computer vision and image recognition. Additionally, you will learn about the training process for neural networks and the techniques used to optimize their performance.
Throughout the course, you will have the opportunity to apply your knowledge through hands-on coding exercises and real-world case studies, utilizing popular ML libraries and frameworks. By the end of this course, you will have a solid foundation in Machine Learning and be well-equipped to tackle a wide range of data-driven challenges in various domains.
Author(s): Techjedi LLP
