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 atasets (sample sklearn  datasets), Apriori algorithm, FP tree algorithm, clustering algorithms, Classification and Regression Trees(CART) and C    5.0. Linear Regression, Multiple Linear Regression,Logistic Regression, Naïve Bayes, Support Vector Machines(SVM) and  Simple neural network.