Difference between revisions of "Category:Machine Learning"
From Christoph's Personal Wiki
(→Courses) |
(→Blog posts) |
||
| (One intermediate revision by the same user not shown) | |||
| Line 3: | Line 3: | ||
==Courses== | ==Courses== | ||
;Udemy | ;Udemy | ||
| + | * [https://deeplearningcourses.com/course_order Course order] | ||
* [https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python/ (The Numpy Stack in Python)] — 2 hours | * [https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python/ (The Numpy Stack in Python)] — 2 hours | ||
* [https://www.udemy.com/data-science-linear-regression-in-python/ Linear Regression in Python] — 3.5 hours | * [https://www.udemy.com/data-science-linear-regression-in-python/ Linear Regression in Python] — 3.5 hours | ||
| Line 72: | Line 73: | ||
==Blog posts== | ==Blog posts== | ||
''Note: The following are blog posts I wrote related to Machine Learning.'' | ''Note: The following are blog posts I wrote related to Machine Learning.'' | ||
| − | * [https://www.redapt.com/ | + | * [https://www.redapt.com/blog/apache-spark-for-machine-learning-part-1 Using Apache Spark for Machine Learning - Part 1] |
| − | * [https://www.redapt.com/ | + | * [https://www.redapt.com/blog/using-apache-spark-for-machine-learning-part-2 Using Apache Spark for Machine Learning - Part 2] |
| − | * [https://www.redapt.com/ | + | * [https://www.redapt.com/blog/using-apache-spark-for-machine-learning-part-3 Using Apache Spark for Machine Learning - Part 3] |
[[Category:Technical and Specialized Skills]] | [[Category:Technical and Specialized Skills]] | ||
Latest revision as of 18:28, 27 November 2019
Machine learning is the sub-field of computer science that gives computers the ability to learn without being explicitly programmed.
Contents
Courses
- Udemy
- Course order
- (The Numpy Stack in Python) — 2 hours
- Linear Regression in Python — 3.5 hours
- Logistic Regression in Python — 3 hours
- (Supervised Machine Learning in Python) —
- (Bayesian Machine Learning in Python: A/B Testing) — 5.5 hours
- Deep Learning in Python — 5.5 hours
- Practical Deep Learning in Theano and TensorFlow — 3.5 hours
- (Supervised Machine Learning in Python 2: Ensemble Methods) —
- Convolutional Neural Networks in Python —
- (Easy NLP) — 5.5 hours
- (Cluster Analysis and Unsupervised Machine Learning) —
- Unsupervised Deep Learning —
- (Hidden Markov Models) —
- Recurrent Neural Networks in Python —
- Artificial Intelligence: Reinforcement Learning in Python —
- Natural Language Processing with Deep Learning in Python —
- Advanced AI: Deep Reinforcement Learning in Python — 4 hours
- ---
- Deep Learning: Advanced NLP and RNNs — 7.5 hours
- Deep Learning: Advanced Computer Vision — 6 hours
- Deep Learning: GANs and Variational Autoencoders — 7 hours
- Ensemble Machine Learning in Python: Random Forest, AdaBoost — 5 hours
- ---
- Machine Learning A-Z™: Hands-On Python & R In Data Science — 37.5 hours
Deep Learning
- What Deep Learning is good at
- Examples of common Deep Learning algorithms
- Analysis
- Prediction
- Recommendation
- Dectection
- Classification
- Segmentation
- Natural Language Processing (NLP)
- Use cases
- Image/video classification
- Speech recognition
- NLP
- Cancer cell detection
- Drug discovery
- Video captioning
- Content-based search
- Real-time translation
- Face recognition
- Video surveillance
- Cyber security
- Pedestrian detection
- Lane tracking
- Recognize traffic signs
NumPy / Pandas / matplotlib / SciPy
pip install -U numpy scipy matplotlib pandas ipython
- See:
- NumPy
- SciPy
- matplotlib
- pandas
- scikit-learn
- scikit-image
People to follow
Blog posts
Note: The following are blog posts I wrote related to Machine Learning.
- Using Apache Spark for Machine Learning - Part 1
- Using Apache Spark for Machine Learning - Part 2
- Using Apache Spark for Machine Learning - Part 3
Pages in category "Machine Learning"
The following 6 pages are in this category, out of 6 total.
