Python for Machine Learning
At a Glance
The Python for Machine Learning course is designed to provide participants with a comprehensive understanding of the essential concepts, techniques, and tools used in machine learning with Python. Participants will learn how to apply popular machine learning algorithms using libraries such as scikit-learn and TensorFlow to solve real-world problems.
Objectives
By the end of the course, participants will be equipped to:
- Understand the fundamentals of machine learning and its applications
- Learn how to use Python libraries for data preprocessing and manipulation
- Implement supervised and unsupervised machine learning algorithms for classification, regression, and clustering tasks
- Perform model evaluation and hyperparameter tuning
- Gain familiarity with deep learning concepts and frameworks such as TensorFlow and Keras
- Apply machine learning techniques to real-world datasets and projects
Prerequisite
Basic knowledge of Python programming and is required. Prior experience with machine learning is helpful but not required.
Curriculum
- 8 Sections
- 0m Duration
Introduction to Machine Learning with Python
Data Preprocessing and Feature Engineering
Supervised Learning: Classification & Regression
Unsupervised Learning: Clustering & Dimensionality Reduction
Model Evaluation and Hyperparameter Tuning
Introduction to Deep Learning
Deep Learning with Tensorflow and Keras
Practical Implementation: Implementation of Machine Learning Techniques in Industry-relevant Problems
Related Courses
Mastering Unsupervised Learning
- Unsupervised learning techniques
- Real-world case studies & implementation
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Mastering Supervised Learning
- Supervised learning techniques
- Real-world case studies & implementation
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