I am shubham sharma graduated from Acropolis Institute of technology in Computer Science and Engineering. I have spent around 2 years in field of Machine learning. I am currently working as Data Scientist in Reliance industries private limited Mumbai. Mainly focused on problems related to data handing, data analysis, modeling, forecasting, statistics and machine learning, Deep learning, Computer Vision, Natural language processing etc. Area of interests are Data Analytics, Machine Learning, Machine learning, Time Series Forecasting, web information retrieval, algorithms, Data structures, design patterns, OOAD.

1. Machine Learning Using
Python
Instructor :- Shubham Sharma

2. Outlines
• Some Training Pics
• Why learn Python for Machine Leaning ?
• Python Libraries For Machine Leaning
• Machine Learning key phases
• Series and Data-frames
• Case Studies :- Load Prediction Problem
• Python/Predictive model in data analytics
• Main Resources

3. Some Training pics

4. Some Training pics

5. Some Training pics

6. Why learn Python for Machine Leaning ?
• Python has gathered a lot of interest recently as a choice of language
for data analysis. I had compared it against SAS & R some time back.
Here are some reasons which go in favour of learning Python:
• Open Source – free to install
• Awesome online community
• Very easy to learn
• Can become a common language for data science and production of
web based analytics products.
•

7. Python Libraries For Machine Leaning
• NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array.
This library also contains basic linear algebra functions, Fourier transforms, advanced random
number capabilities and tools for integration with other low level languages like Fortran, C and
C++
• SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for
variety of high level science and engineering modules like discrete Fourier transform, Linear
Algebra, Optimization and Sparse matrices.
• Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots..
You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these
plotting features inline. If you ignore the inline option, then pylab converts ipython environment
to an environment, very similar to Matlab. You can also use Latex commands to add math to your
plot.
• Pandas for structured data operations and manipulations. It is extensively used for data munging
and preparation. Pandas were added relatively recently to Python and have been instrumental in
boosting Python’s usage in data scientist community.
• .

8. • Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this
library contains a lot of effiecient tools for machine learning and statistical
modeling including classification, regression, clustering and dimensionality
reduction.
• Statsmodels for statistical modeling. Statsmodels is a Python module that allows
users to explore data, estimate statistical models, and perform statistical tests. An
extensive list of descriptive statistics, statistical tests, plotting functions, and
result statistics are available for different types of data and each estimator.
• Seaborn for statistical data visualization. Seaborn is a library for making attractive
and informative statistical graphics in Python. It is based on matplotlib. Seaborn
aims to make visualization a central part of exploring and understanding data.
• Bokeh for creating interactive plots, dashboards and data applications on modern
web-browsers. It empowers the user to generate elegant and concise graphics in
the style of D3.js. Moreover, it has the capability of high-performance
interactivity over very large or streaming datasets
Python Libraries For Machine Leaning

9. • Blaze for extending the capability of Numpy and Pandas to distributed and
streaming datasets. It can be used to access data from a multitude of sources
including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together
with Bokeh, Blaze can act as a very powerful tool for creating effective
visualizations and dashboards on huge chunks of data.
• Scrapy for web crawling. It is a very useful framework for getting specific patterns
of data. It has the capability to start at a website home url and then dig through
web-pages within the website to gather information.
• SymPy for symbolic computation. It has wide-ranging capabilities from basic
symbolic arithmetic to calculus, algebra, discrete mathematics and quantum
physics. Another useful feature is the capability of formatting the result of the
computations as LaTeX code.
• Requests for accessing the web. It works similar to the the standard python
library urllib2 but is much easier to code. You will find subtle differences with
urllib2 but for beginners, Requests might be more convenient.
Python Libraries For Machine Leaning

10. Machine Learning key phases
• We will take you through the 3 key phases:
• Data Exploration – finding out more about the data we have
• Data Munging – cleaning the data and playing with it to make it better
suit statistical modeling
• Predictive Modeling – running the actual algorithms and having fun

11. Python math and cmath libs
• math provides access to the mathematical functions defined by the C
standard.
• These functions cannot be used with complex numbers;
• cmath
• It provides access to mathematical functions for complex numbers.
The functions in this module accept integers, floating-point numbers
or complex numbers as arguments. They will also accept any Python
object that has either a __complex__() or a __float__() method:
•

12. Series and Dataframes
• Numpy and Scipy Documentation
• Introduction to Series and Dataframes
• Series can be understood as a 1 dimensional labelled / indexed array.
You can access individual elements of this series through these labels.

13. Practice data set – Loan Prediction Problem
• Steps :
• Step1 :- installation
• Install ipython
• Install pandas
• Install numpy
• Install matplotlib
• Then

14. Practice data set – Loan Prediction Problem
• Step 2:- begin with exploration
• To begin, start iPython interface in Inline Pylab mode by typing following on your terminal / windows
command/pydev(eclip) prompt:
• >>>ipython notebook --pylab=inline
• Importing libraries and the data set:
• Importing libraries and the data set:
• import pandas as pd
• import numpy as np
• import matplotlib as plt
• df = pd.read_csv("/home/kunal/Downloads/Loan_Prediction/train.csv") #Reading the dataset in a dataframe
using Pandas
• df.head(10)
• df.describe()
• df['Property_Area'].value_counts()

15. Practice data set – Loan Prediction Problem
• Step 3 :- Distribution analysis
• Lets start by plotting the histogram of ApplicantIncome using the following commands:
• df['ApplicantIncome'].hist(bins=50)
• Next, we look at box plots to understand the distributions. Box plot for fare can be plotted by:
• df.boxplot(column='ApplicantIncome’)
• Categorical variable analysis
• temp1 = df['Credit_History'].value_counts(ascending=True)
• temp2 = df.pivot_table(values='Loan_Status',index=['Credit_History'],aggfunc=lambda x:
x.map({'Y':1,'N':0}).mean())
• print 'Frequency Table for Credit History:'
• print temp1
• print 'nProbility of getting loan for each Credit History class:'
• print temp2

16. Practice data set – Loan Prediction Problem
• Using matplotlib for plotting graph
• import matplotlib.pyplot as plt
• fig = plt.figure(figsize=(8,4))
• ax1 = fig.add_subplot(121)
• ax1.set_xlabel('Credit_History')
• ax1.set_ylabel('Count of Applicants')
• ax1.set_title("Applicants by Credit_History")
• temp1.plot(kind='bar')
• ax2 = fig.add_subplot(122)
• temp2.plot(kind = 'bar')
• ax2.set_xlabel('Credit_History')
• ax2.set_ylabel('Probability of getting loan')
• ax2.set_title("Probability of getting loan by credit history")

17. Practice data set – Loan Prediction Problem
• these two plots can also be visualized by combining them in a stacked
chart::
• temp3 = pd.crosstab(df['Credit_History'], df['Loan_Status'])
• temp3.plot(kind='bar', stacked=True, color=['red','blue'], grid=False)
• 4. Data Munging in Python : Using Pandas
• Check missing values in the dataset
• df.apply(lambda x: sum(x.isnull()),axis=0)
• df['LoanAmount'].fillna(df['LoanAmount'].mean(), inplace=True)

18. Practice data set – Loan Prediction Problem
• 5. Building a Predictive Model in Python
• This can be done using the following code:
• from sklearn.preprocessing import LabelEncoder
• var_mod =
['Gender','Married','Dependents','Education','Self_Employed','Property_Area','Lo
an_Status']
• le = LabelEncoder()
• for i in var_mod:
• df[i] = le.fit_transform(df[i])
• df.dtypes
• Python is really a great tool, and is becoming an increasingly popular language
among the data scientists.

19. Python with JSON,csv with Pandas
• Best blogs
• https://www.dataquest.io/blog/python-json-tutorial/
• http://blog.danwin.com/examples-of-web-scraping-in-python-3-x-for-
data-journalists/
• https://automatetheboringstuff.com/chapter14/

20. Python/Predictive model in data analytics
• Predictive modeling is a process that uses data
mining and probability to forecast outcomes. Each model is made up
of a number of predictors, which are variables that are likely to
influence future results.
• Sklearn.LabelEncoder()
• It Convert Pandas Categorical Data For Scikit-Learn

21. Python/Perfect way to build a Predictive Model
• Predictive modeling is a process that uses data mining and
probability to forecast outcomes. Each model is made up of a number
of predictors, which are variables that are likely to influence future
results.
• Broadly, it can be divided into 4 parts.
• Descriptive analysis on the Data – 50% time
• Data treatment (Missing value and outlier fixing) – 40% time
• Data Modelling – 4% time
• Estimation of performance – 6% time

22. Python/Perfect way to build a Predictive Model
• Descriptive Analysis
• Descriptive statistics is the initial stage of analysis used to describe and
summarize data. The availability of a large amount of data and very efficient
computational methods strengthened this area of the statistic.: Below are the
steps involved to understand,
• Variable Identification
• Univariate Analysis
• Bi-variate Analysis
• Missing values treatment
• Outlier treatment
• Variable transformation
• Variable creation

23. Python/Perfect way to build a Predictive Model
• Data treatment:
• An important aspect of statistical treatment of data is the handling of
errors. methods to treat missing values
• Deletion: It is of two types: List Wise Deletion and Pair Wise Deletion.
• Mean/ Mode/ Median Imputation: Imputation is a method to fill in the
missing values with estimated ones.
• Prediction Model: Prediction model is one of the sophisticated method for
handling missing data. Here, we create a predictive model to estimate
values that will substitute the missing data.
• KNN Imputation: In this method of imputation, the missing values of
an attribute are imputed using the given number of attributes that are
most similar to the attribute whose values are missing.

24. Python/Perfect way to build a Predictive Model
• Data Modelling : In case of bigger data, you can consider running a
Random Forest. This will take maximum amount of time
• Estimation of Performance : It is measurement of performance
.kfold with k=7 highly effective to take my initial bet. This finally
takes 1-2 minutes to execute and document.

25. Python/time-series-forecast-study
• The problem is to predict the number of monthly sales of champagne for the Perrin
Freres label (named for a region in France).
• The dataset provides the number of monthly sales of champagne from January 1964 to
September 1972, or just under 10 years of data.
• Download the dataset as a CSV file and place it in your current working director
• The steps of this project that we will through are as follows.
• Environment.
• Problem Description.
• Test Harness.
• Persistence.
• Data Analysis.
• ARIMA Models.
• Model Validation.
• y with the filename “champagne.csv“.

26. Python/time-series-forecast-study
• 3. Test Harness
• We must develop a test harness to investigate the data and evaluate candidate models.
• This involves two steps:
• Defining a Validation Dataset.
• Developing a Method for Model Evaluation.
• 3.1 Validation Dataset
• The code below will load the dataset as a Pandas Series and split into two, one for model development
(dataset.csv) and the other for validation (validation.csv).
• from pandas import Series
• series = Series.from_csv('champagne.csv', header=0)
• split_point = len(series) - 12
• dataset, validation = series[0:split_point], series[split_point:]
• print('Dataset %d, Validation %d' % (len(dataset), len(validation)))
• dataset.to_csv('dataset.csv')
• validation.to_csv('validation.csv')

27. Python/time-series-forecast-study
• The specific contents of these files are:
• dataset.csv: Observations from January 1964 to September 1971 (93
observations)
• validation.csv: Observations from October 1971 to September 1972 (12
observations)
•
3.2. Model Evaluation
• Model evaluation will only be performed on the data
in dataset.csv prepared in the previous section.
• Model evaluation involves two elements:
• Performance Measure.
• Test Strategy.

28. Python/Recommender systems
• Movie recommend
• https://cambridgespark.com/content/tutorials/implementing-your-
own-recommender-systems-in-Python/index.html
• Movie recommend
• Matrix factorization recommender
• https://beckernick.github.io/matrix-factorization-recommender/

29. Python/Main resources
• I definitely recommend cs109.org. Harvard CS 109
There are a few different courses, but the one I used when I was
learning was Dataquest
• https://www.dataquest.io/blog/pandas-cheat-sheet/
• https://www.dataquest.io/blog/data-science-portfolio-project/
• Loan prediction
• https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-
learn-data-science-python-scratch-2/