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In this course, you will :
- Comprehend the fundamental concepts of statistical learning and become acquainted with various methods for developing predictive models
- At each stage of the specialisation, you will gain hands-on experience in data manipulation and skill development, culminating in a capstone project that incorporates all of the concepts taught in the specialisation.
- Examine the syllabus, download all course materials, and set up your system for the course. We will also cover the fundamentals of supervised learning and regression.
- Learn what features are in a dataset and how to work with them in Jupyter notebooks through cleaning, manipulation, and analysis.
- Discover classification and the various methods for implementing it, such as K-nearest neighbours, logistic regression, and support vector machines.
- Understand the significance of properly training and testing a model. Gradient descent will also be implemented in Python and TensorFlow.
- will build on the project started in the first Python Data Products for Predictive Analytics course with simple predictive machine learning algorithms. Find a dataset, clean it, and run basic data analyses on it.
Syllabus :
- Week 1: Supervised Learning & Regression
- Week 2: Features
- Week 3: Classification
- Week 4: Gradient Descent
