@@ -8,15 +8,15 @@ Empowering you to use machine learning to get valuable insights from data.
88- 🤖 Learn when to use specific ML techniques for your meaningful tasks.<br >
99- 🔥 Implement basic ML algorithms and deep neural networks with <a href =" https://pytorch.org/ " target =" _blank " style =" color #ee4c2c " >PyTorch</a >.<br >
1010- 🖥️ Run everything on the browser without any set up using Google Colab.
11- - 🏆 Learn object-oriented ML to code for products, not just tutorials
11+ - 📦 Learn object-oriented ML to code for products, not just tutorials
1212
1313## Notebooks
1414| Basics| Deep Learning| Advanced| Topics|
1515| -| -| -| -|
1616| 📓 [ Notebooks] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/00_Notebooks.ipynb ) | 🔥 [ PyTorch] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/07_PyTorch.ipynb ) | 📚 [ Advanced RNNs] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/14_Advanced_RNNs.ipynb ) | 📸 [ Computer Vision] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/15_Computer_Vision.ipynb ) |
1717| 🐍 [ Python] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/01_Python.ipynb ) | 🎛️ [ Multilayer Perceptrons] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/08_Multilayer_Perceptron.ipynb ) | 🏎️ Highway and Residual Networks| ⏰ Time Series Analysis|
1818| 🔢 [ NumPy] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/02_NumPy.ipynb ) | 🔎 [ Data & Models] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/09_Data_and_Models.ipynb ) | 🔮 Autoencoders| 🏘️ Topic Modeling|
19- | 🐼 [ Pandas] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/03_Pandas.ipynb ) | 📦 [ ObjectOriented ML] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/10_Object_Oriented_ML.ipynb ) | 🎭 Generative Adversarial Networks| 🛒 Recommendation Systems|
19+ | 🐼 [ Pandas] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/03_Pandas.ipynb ) | 📦 [ Object- Oriented ML] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/10_Object_Oriented_ML.ipynb ) | 🎭 Generative Adversarial Networks| 🛒 Recommendation Systems|
2020| 📈 [ Linear Regression] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/04_Linear_Regression.ipynb ) | 🖼️ [ Convolutional Neural Networks] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/11_Convolutional_Neural_Networks.ipynb ) | 🐝 Spatial Transformer Networks| 🗣️ Pretrained Language Modeling|
2121| 📊 [ Logistic Regression] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/05_Logistic_Regression.ipynb ) | 📝 [ Embeddings] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/12_Embeddings.ipynb ) || 🤷 Multitask Learning|
2222| 🌳 [ Random Forests] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/06_Random_Forests.ipynb ) | 📗 [ Recurrent Neural Networks] ( https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/13_Recurrent_Neural_Networks.ipynb ) || 🎯 Low Shot Learning|
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