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Jean de Nyandwi Profile picture
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28 Oct, 5 tweets, 2 min read
All in just one repository:

◆Data visualization with Matplotlib & Seaborn
◆Data preprocessing with Pandas
◆Classical machine learning with Scikit-Learn: From linear models, trees, ensemble models to PCA
◆Neural networks with TensorFlow & Keras: ConvNets, RNNs, BERT, etc...
You can get all of the above here:

github.com/Nyandwi/machin…
View the notebooks easily here:

nbviewer.org/github/Nyandwi…
Or run in @DeepnoteHQ

deepnote.com/project/machin…
Or run in Google Colab

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

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More from @Jeande_d

Jean de Nyandwi Profile picture
26 Oct
The following are 5 main types of machine learning systems based on the level of supervision involved in the training process:

◆Supervised learning
◆Unsupervised learning
◆Semi-supervised learning
◆Self-supervised learning
◆Reinforcement learning

Let's talk about them...🧵
1. Supervised learning

This is the common most type of machine learning. Most ML problems that we encounter falls into this category.

As the name implies, a supervised learning algorithm is trained with input data along with some form of guidance that we can call labels.
In other words, a supervised learning algorithm maps the input data (or X in many textbooks) to output labels (y).

Labels are also known as targets and they acts as a description of the input data.
Read 30 tweets
Jean de Nyandwi Profile picture
24 Oct
My summary of the week on Twitter ML:

◆ 3 threads on explaining complex concepts
◆ 2 on practical learning resources and
◆ 1 good news

🧵🧵
EXPLAINED CONCEPTS/IDEAS

#1 @fchollet on the nature of generalization in deep learning, clearly explaining interpolation and manifold hypothesis.

A long thread that is worth reading

#2 @svpino on what you didn't know about machine learning pipelines.

Read 8 tweets
Jean de Nyandwi Profile picture
18 Oct
Kaggle's 2021 State of Data Science and Machine Learning survey was released a few days ago.

If you didn't see it, here are some important takeaways 🧵
Top 5 IDEs

1. Jupyter Notebook
2. Visual Studio Code
3. JupyterLab
4. PyCharm
5. RStudio
ML Algorithms Usage: Top 10

1. Linear/logistic regression
2. Decision trees/random forests
3. Gradient boosting machines(Xgboost, LightGBM)
5. Convnets
6. Bayesian approaches
7. Dense neural networks(MLPs)
8. Recurrent neural networks(RNNs)
9. Transformers(BERT, GPT-3)
10. GANs
Read 12 tweets
Jean de Nyandwi Profile picture
17 Oct
Source of errors in building traditional programs:

◆Wrong syntaxes
◆Inefficient codes
Source of errors in machine learning:

◆Solving a wrong problem
◆Using a wrong evaluation metric
◆Not being aware of a skewed data
◆Inconsistent data preprocessing functions
More sources of errors in ML:

◆Putting too much emphasis on models than data
◆Data leakage
◆Training on the test data
◆Model and data drifts
Read 7 tweets
Jean de Nyandwi Profile picture
11 Oct
Python image processing libraries

◆Scikit-Image
◆Pillow
◆NumPy(image is just an array of pixels)

The following are more than image processing, they provide state-of-the-art computer vision and machine learning algorithms:

◆OpenCV
◆OpenMMLab
Also, most machine learning frameworks have image processing functions.

TensorFlow has tf.image and Keras took that further to image processing layers that you can insert inside the model.
tf.image: tensorflow.org/api_docs/pytho…
Read 4 tweets
Jean de Nyandwi Profile picture
8 Oct
The machine learning research community is very and very vibrant.

Here is what I mean...🧵🧵
In 1958, Frank Rosenblatt invented a perceptron, a very simple algorithm that would later turn out to be the core and origin of to days intelligent machines.
In essence, the perceptron is a simple binary classifier that can determine whether or not a given input belongs to a specific class.

Here is the algorithm of perceptron:
Read 27 tweets

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