Thread by @Jeande_d

Releasing a complete machine learning package containing over 30 end to end notebooks for:

◆Data analysis
◆Data visualization
◆Data cleaning
◆Classical ML
◆Computer vision
◆Natural language processing

Everything is now accessible here:

github.com/Nyandwi/machin…

Every single notebook is very interactive.

It starts with a high-level overview of the model/technique being covered and then continues with the implementation.

And wherever possible, there are visuals to support the concepts.

Here is an outline of what you will find there:

PART 1 - Intro to Programming and Working with Data

◆Intro to Python for Machine Learning
◆Data Computation With NumPy
◆Data Manipulation with Pandas
◆Data Visualization
◆Real EDA and Data Preparation

PART 2 - Machine Learning

◆Intro to Machine Learning
◆Machine Learning with Scikit-Learn (90% percent of models/techniques are covered, from linear models, decision trees, PCA, to ensemble methods)

PART 3 - Deep Learning

Intro to Artificial Neural Networks and TensorFlow

◆Intro to Artificial Neural Networks
◆Intro to TensorFlow
◆Neural Networks for Regression with TensorFlow
◆Neural Networks for Classification with TensorFlow

PART 3 - Continued

Deep Computer Vision with TensorFlow

◆Intro to Computer Vision with Convolutional Neural Networks(CNN)
◆ConvNets for Real-World Data and Image Augmentation
◆ConvNets Architectures and Transfer Learning

PART 3 - Contd

Natural Language Processing with TensorFlow

◆Intro to NLP and Text Processing with TensorFlow
◆Using Word Embeddings to Represent Texts
◆Recurrent Neural Networks (RNNs)
◆Using ConvNets for Texts Classification
◆Using Pretrained BERT for Text Classification

The best way to view the notebooks is to read them on Nbviewer

nbviewer.jupyter.org/github/Nyandwi…

You can also run everything in Colab

colab.research.google.com/github/Nyandwi…

You can also run it in @DeepnoteHQ

deepnote.com/project/machin…