Master Python Machine Learning: A Comprehensive Guide to Clustering & Classification Techniques
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Course Author
Minerva Singh
Course Title:
Clustering & Classification With Machine Learning In Python
Why Take This Course:
This comprehensive course by Minerva Singh is your one-stop guide to both supervised and unsupervised learning in Python. Perfect for practical data science enthusiasts, it covers essential aspects, eliminating the need for multiple courses or books on Python-based data science.
About the Instructor
Minerva Singh, an Oxford University MPhil graduate with a recent PhD from Cambridge University, brings over five years of experience in analyzing real-life data using data science techniques. Her course bridges the gap often found in Python data science resources, offering a deep understanding of machine learning, specifically clustering and classification using Python.
Course Overview
1. Introduction to Python Data Science and Anaconda:
Comprehensive overview of Python data science and the Anaconda framework.
2. Getting Started with Jupyter Notebooks: Basics of using Jupyter notebooks for implementing data science techniques.
3. Data Structures and Reading in Pandas: Learn about data structures and reading data using Pandas, including CSV, Excel, and HTML formats.
4. Pre-Processing and Wrangling Python Data:
Essential steps in any data science project, covering handling missing data, conditional data, and group data techniques.
5. Machine Learning – Supervised and Unsupervised Learning:
In-depth exploration of both supervised and unsupervised learning in Python.
6. Artificial Neural Networks and Deep Learning:
Explore artificial neural networks (ANN) and deep learning, including their use in classification tasks.
7. Advanced Techniques in Python Data Science:
Delve into advanced techniques, including unsupervised learning, dimension reduction, supervised learning, and an introduction to deep learning using the H2o framework.
No Prior Knowledge Required:
Designed for beginners, this course starts with basics and progressively introduces advanced concepts. Minerva adopts an easy-to-understand, hands-on approach, making even complex Python concepts accessible. Real data from various sources is used throughout the course.
Join the Course Now:
Excel in Python machine learning by mastering techniques and concepts in this course. Elevate your career and gain a competitive advantage in data science. Don't miss this opportunity—enroll now!
Keywords:Free Udemy Coupon, machine learning
What You Will Learn:
1. Utilize Anaconda/iPython for practical data science.
2. Read data into Python from different sources.
3. Conduct basic data pre-processing & wrangling in Python.
4. Implement unsupervised/clustering techniques like k-means clustering.
5. Implement dimensional reduction techniques (PCA) & feature selection.
6. Implement supervised learning techniques/classification, such as Random Forests in Python.
7. Neural network & deep learning-based classification.
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