Import and work with large dataset (Python beginners)
This question is high level, so I'll provide some broad approaches for reducing memory usage in Dask:

Use a columnar file format like Parquet so you can leverage column pruning
Use column dtypes that require less memory int8 instead of int64
Strategically persist in memory, where appropriate
Use a cluster that's sized well for your data (running an analysis on 2GB of data requires different amounts of memory than 2TB)
split data into multiple files so it's easier to process in parallel

Your data has 100 million rows, which isn't that big (unless it had thousands of columns).  Big data typically has billions or trillions of rows.
Feel free to add questions that are more specific and I can provide more specific advice.  You can provide the specs of your machine / cluster, the memory requirements of the DataFrame (via ddf.memory_usage(deep=True)) and the actual code you're trying to run.
since i couldn't find the best way to deal with my issue i came here to ask..
I'm a beginner with Python but i have to handle a large dataset.
However, i don't know what's the best way to handle the "Memory Error" problem.
I already have a 64 bits 3.7.3 Python version.
I saw that we can use TensorFlow or specify chunks in the pandas instruction or use the library Dask but i don't know which one is the best to fit with my problem and as a beginner it's not very clear.
I have a huge dataset (over 100M  observations) i don't think reducing the dataset would decrease a lot the memory.
What i want to do is to test multiple ML algorithms with a train and test samples. I don't know how to deal with the problem.
Thanks!