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Jean de Nyandwi Profile picture
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7 Oct, 7 tweets, 3 min read
The most useful courses are free. They are only challenging and hard to complete, which is why they are useful.

Here are 4 examples of the free machine learning courses that with enough dedication can help you get useful skills.

🧵
1. Machine Learning by Andrew Ng. on Coursera

Price: Free
Students: Over 4 million people

coursera.org/learn/machine-…
2. Full Stack Deep Learning by UC Berkeley

Price: Free

fullstackdeeplearning.com/spring2021/
3. Practical Deep Learning for Coders by @fastdotai

Price: Free

course.fast.ai/#
4. CS231n: Convolutional Neural Networks for Visual Recognition

Price: Free
Students: Over 2 million people

youtube.com/playlist?list=…
There are many more great courses that are free.

You are welcome to add any course that you think is helpful and is available for free online.
If you find any of the things I share helpful, follow @Jeande_d.

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More from @Jeande_d

Jean de Nyandwi Profile picture
4 Oct
How to think about precision and recall:

Precision: What is the percentage of positive predictions that are actually positive?

Recall: What is the percentage of actual positives that were predicted correctly?
The fewer false positives, the higher the precision. Vice-versa.

The fewer false negatives, the higher the recall. Vice-versa. Image
How do you increase precision? Reduce false positives.

It can depend on the problem, but generally, that might mean fixing the labels of those negative samples(being predicted as positives) or adding more of them in the training data.
Read 10 tweets
Jean de Nyandwi Profile picture
26 Sep
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
Read 10 tweets
Jean de Nyandwi Profile picture
25 Sep
Here are 7 samples from what's coming tomorrow:

1. Data visualization with Seaborn

◆Relational Plots
◆Distribution Plots
◆Categorical Plots
◆Regression Plots
◆Multiplots
◆Matrix Plots: Heat and Cluster Maps
◆Style and Color

colab.research.google.com/drive/1Qkf53B4…
2. Exploratory Data Analysis

◆A quick look into the dataset
◆Summary statistics
◆Finding the basic information about the dataset
◆Checking missing data
◆Checking feature correlations

colab.research.google.com/drive/1iMpQOWH…
3. A Friendly Intro to Machine Learning

◆Intro to ML Paradigm
◆Machine Learning Workflow
◆Evaluation Metrics
◆Handling Underfitting and Overfitting

colab.research.google.com/drive/14uySoOh…
Read 9 tweets
Jean de Nyandwi Profile picture
20 Sep
TensorFlow or PyTorch?

Forget about numbers. They are both great at what they do, which is putting machine learning codes together.
TensorFlow is most popular in industries, and PyTorch in research organizations/academics,

but the number of industries that use PyTorch and the number of researches made with TensorFlow have been all increasing.
If you are choosing what to learn for the first time, what is the best than the other does not really matter that much.

Focus on one, know its ins and outs, avoid going back and forth learning all of them, and let everybody else use their favorite tools.
Read 5 tweets
Jean de Nyandwi Profile picture
20 Sep
The key differences between shallow learning and deep learning models:

Shallow learning models:

◆ Most of them are simple and require less hyper-parametrization
◆ They need the features to be pre-extracted
◆ They are best suited for tabular datasets
◆ Their architectural changes are very limited.
◆ They don't require huge computation resources
◆ Their results are interpretable than deep learning models
◆ Because of the limit in their design change, there are little researches going on in these models.
Example of shallow learning models:

◆Linear and logistic regression
◆Support vector machines
◆Decision trees
◆Random forests
◆K-Nearest neighbors
Read 9 tweets
Jean de Nyandwi Profile picture
11 Sep
Popular deep learning architectures:

◆ Densely connected neural networks
◆ Convolutional neural networks
◆ Recurrent neural networks
◆ Transformers

Let's talk about these architectures and their suites of datasets in-depth 🧵
Machine learning is an experimentation science. An algorithm that was invented to process images can turn out to work well on texts too.

The next tweets are about the main neural network architectures and their suites of datasets.
1. Densely connected neural networks

Densely connected networks are made of stacks of layers that go from the input to the output.

Generally, networks are organized into layers. Each carry takes input data, processes it, and gives the output to the next layer.
Read 34 tweets

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