Machine Learning From Scratch Full course
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To master machine learning models, one of the best things you can do is to implement them yourself. Although it might seem like a difficult task, for most algorithms, it is actually easier than you think. So throughout the next 10 days, we will implement one machine learning algorithm each day using Python and sometimes the help of Numpy for certain calculations.
You can find the code in our GitHub repository: https://github.com/AssemblyAI-Examples/Machine-Learning-From-Scratch
The algorithms we will go over are:
1. K-Nearest Neighbours - https://youtu.be/rTEtEy5o3X0
2. Linear Regression - https://youtu.be/ltXSoduiVwY
3. Logistic Regression - https://youtu.be/YYEJ_GUguHw
4. Decision Trees - https://youtu.be/NxEHSAfFlK8
5. Random Forest - https://youtu.be/kFwe2ZZU7yw
6. Naive Bayes - https://youtu.be/TLInuAorxqE
7. PCA - https://youtu.be/Rjr62b_h7S4
8. Perceptron - https://youtu.be/aOEoxyA4uXU
9. SVM - https://youtu.be/T9UcK-TxQGw
10. KMeans - https://youtu.be/6UF5Ysk_2gk
Watch the first lesson: https://youtu.be/rTEtEy5o3X0
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