Big Data / Data Science

Data Science – Machine Learning with Python

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of Computer Programs that can change when exposed to new data. Python is the most sought-after skill in today’s machine learning and deep learning marketplace.


Objective of the Course:

The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms using Python.



  • Data Scientist
  • Data Analyst
  • Fresher from Mathematics, Stats and Engineering backgrounds
  • Statistician
  • Data Science professionals


Course Content:

Python Programming

Introduction to Python

  • What are Python Language and features
  • Why Python and why it is different from other languages
  • Installation of Python
  • Anaconda Python distribution for Windows, Mac, Linux.
  • Run a sample python script
  • Working with Python IDE’s.
  • Running basic python commands – Data types, Variables, Keywords, etc

Basic constructs of Python language

  • Indentation(Tabs and Spaces) and Code Comments (Pound # character)
  • Variables and Names
  • Built-in Data Types in Python – Numeric: int, float, complex – Containers: list, tuple, set, dict – Text Sequence: Str (String) – Others: Modules, Classes, Instances, Exceptions, Null Object, Ellipsis Object – Constants: False, True, None, NotImplemented, Ellipsis, __debug__
  • Basic Operators: Arithmetic, Comparison, Assignment, Logical, Bitwise, Membership, Identity
  • Slicing and The Slice Operator [n:m]
  • Control and Loop Statements: if, for, while, range(), break, continue, else


Writing Object-Oriented Program in Python and connecting with Database

  • Classes – classes and objects, access modifiers
  • Instance and class members OOPS paradigm – Inheritance
  • Polymorphism and Encapsulation in Python
  • Functions: Parameters and Return Types
  • Lambda Expressions, Making a connection with Database for pulling data.

File Handling, Exception Handling in Python

  • Open a File, Read from a File, Write into a File
  • Resetting the current position in a File
  • The Pickle (Serialize and Deserialize Python Objects)
  • The Shelve (Overcome the limitation of Pickle)
  • What is an Exception
  • Raising an Exception
  • Catching an Exception;


Machine Learning with Python

Python Programming

Mathematical Computing with Python (NumPy)

  • Arrays and Matrices
  • ND-array object
  • Array indexing
  • Data Types
  • Array math Broadcasting
  • Std Deviation
  • Conditional Prob
  • Covariance and Correlation.
  • Hands-on Exercise
    • Import numpy module
    • Create an array using ND-array
    • Calculate std deviation on an array of numbers
    • Calculate correlation between two variables

Scientific Computing with Python (SciPy)

  • Builds on top of NumPy, SciPy and its characteristics, sub packages: cluster, fftpack, linalg, signal, integrate, optimize, stats
  • Bayes Theorem using SciPy
  • Hands-on Exercise
  • Import SciPy
  • Apply Bayes theorem using SciPy on the given dataset

Data Visualization (Matplotlib)

  • Plotting Graphs and Charts (Line, Pie, Bar, Scatter, Histogram, 3-D)
  • Subplots
  • The Matplotlib API
  • Hands-on Exercise
    • Plot Line
    • Pie
    • Scatter
    • Histogram and other charts using Matplotlib

Data Analysis and Machine Learning (Pandas) OR Data Manipulation with Python

  • Data frames, NumPy array to a data frame
  • Import Data (csv, json, excel, sql database)
  • Data operations: View, Select, Filter, Sort, Groupby, Cleaning, Join/Combine, Handling Missing Values
  • Introduction to Machine Learning(ML)
  • Linear Regression
  • Time Series
  • Hands-on Exercise
    • Import Pandas
    • Use it to import data from a json file
    • Select records by a group and apply filter on top of that
    • View the records
    • Perform Linear Regression analysis
    • Create a Time Series

Natural Language Processing Machine Learning (Scikit-Learn)

  • Introduction to Natural Language Processing (NLP)
  • NLP approach for Text Data
  • Environment Setup (Jupyter Notebook)
  • Sentence Analysis
  • ML Algorithms in Scikit-Learn
  • What is Bag of Words Model
  • Feature Extraction from Text
  • Model Training; Search Grid
  • Multiple Parameters
  • Build a Pipeline
  • Hands-on Exercise
    • Setup Jupyter Notebook environment
    • Load a dataset in Jupyter
    • Use algorithm in Scikit-Learn package to perform ML techniques
    • Train a model Create a search grid

Web Scraping for Data Science

  • What is Web Scraping
  • Web Scraping Libraries (Beautifulsoup, Scrapy)
  • Installation of Beautifulsoup
  • Install lxml Python Parser
  • Making a Soup Object using an input html
  • Navigating Py Objects in the Soup Tree
  • Searching the Tree
  • Output Print
  • Parsing Full or Partial
  • Hands-on Exercise
    • Install Beautifulsoup and lxml Python parser
    • Make a Soup object using an input HTML file
    • Navigate Py objects in the soup tree
    • Search tree
    • Print output
  • python programming
  • Supervised learning


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