Udemy – Machine Learning 0 to 100 | python | Artificial Intelligence 2024-9

Published on: 2024-09-17 19:13:42

Categories: 28

Description

Machine Learning course 0 to 100 Python | Artificial intelligence. Learn machine learning skills from basic concepts to practical real-world applications. This comprehensive course is designed to bridge the gap between theory and practice. Whether you’re an aspiring data scientist, an experienced developer, or a business professional looking to take advantage of machine learning capabilities, this course will provide you with the tools and knowledge you need to succeed.

What you will learn

Basics of Machine Learning: You will understand the basic concepts and terms of machine learning. You will distinguish types of machine learning (supervised, unsupervised).
Real World Applications of Machine Learning: You will identify different applications of machine learning in different industries. You will explore case studies of successful machine learning implementations.
Key Machine Learning Terms and Concepts: You will understand key terms such as features, labels, models, training, testing, overfitting, underfitting, and model evaluation criteria.
Different Machine Learning Algorithms: You will learn the basics of different algorithms (linear regression, decision tree, support vector machines and neural networks).
Data collection and preprocessing techniques: You will learn data collection methods from various sources, data cleaning and normalization techniques.
Implement data visualization to gain insights: You will create and interpret various charts and visualizations using libraries such as Matplotlib. You will use visual tools to identify patterns and trends.
Mastering Python for Machine Learning: Write efficient and readable Python code using best practices. Use the main data science libraries (NumPy, pandas, …).
Evaluate and interpret machine learning models: use metrics such as precision, accuracy, recall, F1 score, and ROC-AUC. Perform comprehensive error analysis.
Implement model tuning techniques: use cross-validation, grid search, and random search to find optimal model parameters. Use ensemble models (bagging, boosting).
Solve common problems in machine learning: Identify and solve common problems such as data imbalance, high dimensionality, and multicollinearity. Implement techniques to handle missing data.
Understanding the deployment of machine learning models: Learn the steps and best practices for deploying models in production environments.