PYTHON & MACHINE LEARNING LAB

Exercises given will be covering entire syllabi as follows:

            Jupyter Installation for Python, Features of Python, Python                         Applications

·   Basics of Python: variables, data types, operators & expressions decision       statements.

·     Loop control statements.

·      Functions.

·      Understand the difference between a function and an object.

·       String manipulation.

·      Tuples, sets and dictionaries: Need, Creation, Operations and in-built          functions

·       importing pandas library, Using series and Data Frames, indexing,                Grouping, aggregating, Merging  dataFrames, dealing with                          missing values using   

       dropna method, filtering or filling in missing data, creating data                    frames

       from dictionaries or nested dictionaries, accessing and changing values         of  data frame using loc,at,replace methods,reading and writing                   csv,excel files.

·        Importing Visualisation libraries: Matplotlib: format parameter of                 pyplot.plot,subplots method, checking and defining                                     ranges of axes, using linspace  and linstyle, specifying legend, title               Style,  creating Scatter plots ,Bar charts.

·       Implementing Machine Learning with scikit-learn: loading and                      Visualizing  datasets (sample sklearn datasets), Apriori algorithm, FP             tree algorithm, clustering algorithms, Classification and Regression              Trees (CART) and C5.0. Linear Regression, Multiple Linear Regression,

        Logistic Regression, Naïve  Bayes, Support Vector Machines(SVM)                and  Simple neural network