Applied Machine Learning in Python
About Course
Dive deep into the world of machine learning with this comprehensive course from the University of Michigan. This Python-based course focuses on the practical application of machine learning techniques, equipping you with the skills to analyze data, build predictive models, and solve real-world problems. Learn about supervised and unsupervised learning, data clustering, cross-validation, overfitting, and more. You’ll also discover advanced techniques like ensemble construction and the limitations of predictive models. This course is perfect for those who have a basic understanding of data science in Python and are ready to take their machine learning skills to the next level.
Key Features:
- Hands-on learning with the scikit-learn toolkit
- Practical applications of machine learning algorithms
- Understanding of data dimensions and data clustering
- Building predictive models using supervised learning
- Mastering advanced techniques like ensemble construction
What You’ll Learn:
- Differentiate between supervised and unsupervised learning techniques
- Choose the right machine learning technique for different datasets and needs
- Perform data engineering to meet specific requirements
- Write Python code for machine learning analysis
This course is completely FREE!
Enroll now and access this course, along with thousands of other free courses from leading platforms like Udemy, Udacity, Coursera, MasterClass, NearPeer, and more, all on Theetay!
Course Content
Coursera – Applied Machine Learning in Python
-
A Message from the Professor
-
002 02_introduction.mp4
00:00 -
004 03_key-concepts-in-machine-learning.mp4
00:00 -
006 04_python-tools-for-machine-learning.mp4
00:00 -
008 05_an-example-machine-learning-problem.mp4
00:00 -
010 06_examining-the-data.mp4
00:00 -
012 07_k-nearest-neighbors-classification.mp4
00:00 -
014 01_introduction-to-supervised-machine-learning.mp4
00:00 -
016 02_overfitting-and-underfitting.mp4
00:00 -
018 03_supervised-learning-datasets.mp4
00:00 -
020 04_k-nearest-neighbors-classification-and-regression.mp4
00:00 -
022 05_linear-regression-least-squares.mp4
00:00 -
024 06_linear-regression-ridge-lasso-and-polynomial-regression.mp4
00:00 -
026 07_logistic-regression.mp4
00:00 -
028 08_linear-classifiers-support-vector-machines.mp4
00:00 -
030 09_multi-class-classification.mp4
00:00 -
032 10_kernalized-support-vector-machines.mp4
00:00 -
034 11_cross-validation.mp4
00:00 -
036 12_decision-trees.mp4
00:00 -
037 13_a-few-useful-things-to-know-about-machine-learning_cacm12.pdf
00:00 -
039 01_model-evaluation-selection.mp4
00:00 -
041 02_confusion-matrices-basic-evaluation-metrics.mp4
00:00 -
043 03_classifier-decision-functions.mp4
00:00 -
045 04_precision-recall-and-roc-curves.mp4
00:00 -
047 05_multi-class-evaluation.mp4
00:00 -
049 06_regression-evaluation.mp4
00:00 -
050 07_practical-guide-to-controlled-experiments-on-the-web.pdf
00:00 -
052 08_model-selection-optimizing-classifiers-for-different-evaluation-metrics.mp4
00:00 -
054 01_introduction.mp4
00:00 -
056 02_naive-bayes-classifiers.mp4
00:00 -
058 03_random-forests.mp4
00:00 -
060 04_gradient-boosted-decision-trees.mp4
00:00 -
062 05_neural-networks.mp4
00:00 -
064 06_deep-learning-optional.mp4
00:00 -
066 08_data-leakage.mp4
00:00 -
Section Quiz
Earn a certificate
Add this certificate to your resume to demonstrate your skills & increase your chances of getting noticed.
Student Ratings & Reviews
