Self-Taught-Machine-Learning
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BeeGass
I have had trouble in the past finding a place where I could learn about statistical learning algorithms, resources as to how to learn them and the code associated with it. This is my attempt at remed...
Self Taught Machine Learning
From Knowing Nothing To Being An AI Expert: Roadmap
CURRICULUM 1: Cornell's
-
k-Nearest Neighbors / Curse of Dimensionality
2.1 KNN Resources
- Great explanation of nearest neighbor
- Video Of KNN
- Video explanation of K-nearest neighbor classification
Great explanations of the algorithm
2.2 PCA Resources
I Learned About PCA's (Principal Component Analysis) To Get My KNN To Work, This Is What I Used
Other Resoucres To Check Out For PCA:
-
Logistic Regression / Maximum Likelihood Estimation / Maximum a Posteriori
- Some Things That Might Help:
CURRICULUM 2: Andrew Ng's Machine Learning Coursera Course
Week 1: Introduction, Linear Regression With One Variable, Linear Algebra Review
Week 2: Linear Regression With Multiple Variables, Octave/Matlab Tutorial
Week 3: Logistic Regression, Regularization
Week 4: Neural Networks: Representation
Week 5: Neural Networks: Learning
Week 6: Advice For Applying Machine Learning, Machine Learning System Design
Week 7: Support Vector Machine
Week 8: Unsupervised Learning, Dimensionality Reduction
Week 9: Anomaly Detection, Recommender Systems
Week 10: Large Scale Machine Learning
Week 11: Application Example: Photo OCR
- This may help you
CURRICULUM 3: Yet Another Machine Learning Course
- Lecture (introduction to ML, accuracy & loss functions): PDF
- Lecture (greedy step-wise classification, training versus testing): PDF
- Lecture (linear regression): PDF
- Lecture (more on linear regression): PDF
- Lecture (gradient descent): PDF
- Lecture (polynomial regression, overfitting): PDF
- Lecture (regularization, logistic regression): PDF
- Lecture (softmax regression, cross-entropy): PDF
- Lecture (stochastic gradient descent, convexity): PDF
- Lecture (positive semi-definiteness, constrained optimization): PDF
- Lecture (support vector machines): PDF
- Lecture (soft versus hard margin SVM, linear separability): PDF
- Lecture (kernelization): PDF
- Lecture (more on kernelization): PDF
- Lecture (Gaussian RBF kernel, nearest neighbors): PDF
- Lecture (principal component analysis): PDF
- Lecture (k-means): PDF
- Lecture (introduction to neural networks): PDF
- Lecture (more on neural networks, XOR problem): PDF
- Lecture (gradient descent for neural networks, Jacobian matrices): PDF
- Lecture (chain rule and backpropagation): PDF
- Lecture (L1 and L2 regularization, dropout): PDF
- Lecture (unsupervised pre-training, auto-encoders): PDF
- Lecture (convolution, pooling): PDF
- Lecture (convolutional neural networks, recurrent neural networks): PDF
- Lecture (practical suggestions): PDF
Supplemental Things I Plan To Use:
- StatQuest YouTube Channel
- Intuitive Machine Learning YouTube Channel
- An Introduction To Statistical Learning With Applications In R
- Pattern Recognition and Machine Learning
- Python Data Science Handbook
- Hands-On Machine Learning with Scikit-Learn and TensorFlow
- Hands On Machine Learning github
- Helpful Stuff
Homework
Homework questions come from the end of each applicable chapter in "An Introduction To Statistical Learning With Applications In R" Or "Pattern Recognition and Machine Learning". These are ideally done in python and not in R, however...
For answers for "An Introduction To Statistical Learning With Applications In R" refer to:
- https://rpubs.com/ppaquay
- http://yahwes.github.io/ISLR/
- https://github.com/yahwes/ISLR
- https://altaf-ali.github.io/ISLR/index.html
- https://blog.princehonest.com/stat-learning/
- https://github.com/asadoughi/stat-learning
- https://www.kaggle.com/lmorgan95/notebooks
- For Answers Specific In Python:
- https://botlnec.github.io/islp/
- Introduction To Statistical Learning With Application In Python
For answers for "Pattern Recognition and Machine Learning" refer to:
- Chapter Overviews and Solutions
- github of the solution stuff
- Official Solution Manual
- Another Official Looking Solution Set
For answers for "The Elements of Statistical Learning Data Mining, Inference, and Prediction" refer to:
- A Solution Manual and Notes for: The Elements of Statistical Learning by Jerome Friedman, Trevor Hastie, and Robert Tibshirani
- A GUIDE AND SOLUTION MANUAL TO THE ELEMENTS OF STATISTICAL LEARNING by JAMES CHUANBING MA
- Solutions to Select Problems of The Elements of Statistical Learning by talwarabhimanyu
- Elements of Statistical Learning (Solutions) by Andrew Tulloch
For answers for "Machine Learning: A Probabilistic Perspective" refer to:
Math Resources To Help
- Probability And Statistics
- Precalculus and Calculus 1-3
- Linear Algebra
Fequently Asked Questions
- Where is your section on Deep Learning?
- There is a lot of math here, how do I get into machine learning without a lot of math?
- Unfortunately, there really is no way to truly learn machine learning without the math. You may be looking for an applied way to learn machine learning which requires less math. The intent of this repository is to teach the theory from the ground up.
- Oh my lord! I need a service that will efficiently compile all possible hotels near the area im visiting and show me the cheapest option in a singlar place.
- Not a question but I believe you are looking for trivago
BeeGass