machine learning the data modeling phase can' start until you have qualitative raw data you can understand but prior to thatthe data preparation phase can benefit from the use of machine learning an example would be cleansing list of text stringsmachine learning can group similar strings together so it becomes easier to correct spelling errors machine learning is also useful when exploring data algorithms can root out underlying patterns in the data where they' be difficult to find with only charts given that machine learning is useful throughout the data science processit shouldn' come as surprise that considerable number of python libraries were developed to make your life bit easier python tools used in machine learning python has an overwhelming number of packages that can be used in machine learning setting the python machine learning ecosystem can be divided into three main types of packagesas shown in figure pandas matplotlib numpy data sympy scipy theano statsmodels data fits in memory modeling scikit-learn rpy text analysis nltk numba pycuda use gpu numbapro use code optimize your code out of memory cython blaze distributed dispy ipcluster parallel pp python for machine learning pydoop big data pyspark hadoopy figure overview of python packages used during the machine-learning phase