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Python Certification Training

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Avvacado Tech Info Python course helps you gain expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers by transforming your career into Data Scientist role. You will use libraries like Pandas, Numpy, Matplotlib, Scipy, Scikit, Pyspark and master the concepts like Python machine learning, scripts, sequence, web scraping and big data analytics leveraging Apache Spark. 

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Course Duration

You will undergo self-paced learning where you will get an in-depth knowledge of various concepts that will be covered in the course.

Real-life Case Studies

Towards the end of the training, you will be working on a project where you will implement the techniques learnt to visualize.

Assignments

Each class has practical assignments which shall be finished before the next class and helps you to apply the concepts taught during

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.

Forum

We have a community forum for all our customers that further facilitates learning through peer interaction and knowledge

   About the training

Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing. 

Avvacado Tech Info Python Certification Training not only focuses on fundamentals of Python, Statistics, Machine Learning and Spark but also helps one gain expertise in applied Data Science at scale using Python. The training is a step by step guide to Python and Data Science with extensive hands on. The course is packed with several activity problems, quiz and assignments and scenarios that help you gain practical experience in addressing an automation problem that would either require only Python or Machine Learning using Python. Starting from basics of Statistics such as mean, median and mode to exploring features such as Data Analysis, Regression, Classification, Clustering, Naive Bayes, Cross Validation, Label Encoding, Random Forests, Decision Trees and Support Vector Machines with a supporting example and exercise help you get into the weeds. 

With the exponential growth in data (as is evident from new Kaggle competitions that now support Torrent for downloading humongous data sets), it goes without saying that Data Scientist is incomplete without Big Data. So, in this course, we ensure you become a fully qualified Data Scientist by also teaching you basics of Spark in the context of data analysis and Machine Learning. 

Python course will also cover both basic and advanced concepts of Python like writing Python scripts, sequence and file operations in Python. You will use libraries like pandas, numpy, matplotlib, scipy, scikit, my spark and master the concepts like Python machine learning, scripts, sequence, web scraping and big data analytics leveraging Apache Spark.

   Training Objectives

During this Python online course, our experts instructors will help you: 

  • Write Python scripts, unit test code
  • Understand different types of Machine Learning problems and related data
  • Programmatically download and analyze data
  • Apply machine learning techniques and algorithms over data
  • Learn feature engineering techniques like PCA
  • Ascertain accuracy of predictions using RMSE, Log Loss, AUC, Cross Validation
  • Learn techniques to deal with different types of data – ordinal, categorical, encoding
  • Compare algorithms and improve accuracy
  • Learn data visualization
  • Using IPython notebooks, master the art of presenting step by step data analysis.

Get Python certification post completion of this course

   Why learn Python?

It's continued to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built-in debugger. 

It runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.

It has evolved as the most preferred Language for Data Analytics and the increasing search trends on Python also indicates that it is the " Next Big Thing "and a must for Professionals in the Data Analytics domain.


   Who should go for this course?

There is a booming demand for skilled data scientists and machine learning with Python professionals across all industries that make this course suited for participants at all levels of experience. We recommend this Python course especially for the following professionals:

Below are the professionals which should definitely take this course. 

  • Programmers, Developers, Technical Leads, Architects
  • Data Scientists & Data Analyst
  • Business Analysts
  • Business Intelligence Manager
  • Statisticians and Analysts
  • Project Managers

   What are the pre-requisites?

There are no hard pre-requisites. Basic understanding of Computer Programming terminologies is sufficient. 

Also, basic concepts related to Data analysis are beneficial but not mandatory. 

You will also get familiar with basics of statistics during the course. 

   Introduction to Python

  • Goal : Give brief idea of what Python is and touch on basics.

    Objectives:

      • Define Python
      • Know why Python is popular
      • Setup Python environment
      • Discuss flow control
      • Write your first Python program


  •

    Topics:

      • Get an overview of Python  
      • Learn about Interpreted Languages  
      • List the Advantages/Disadvantages of Python  
      • Explore Pydoc  
      • Start Python  
      • Discuss Interpreter PATH
      • Use the Interpreter  
      • Run a Python Script  
      • Discuss Python Scripts on UNIX/Windows  
      • Explore Python Editors and IDEs  
      • Use Variables, Keywords, Built-in Functions, Strings, Different literals, Math operators and expressions, Writing to the screen, String formatting, Command line parameters and Flow Control. 

    •

    Hands On:

    • Data types - string, numbers, dates
    • Keywords
    • Variables
    • Literals

   Sequences and File Operations

    • Goal : Learn different types of sequence structures, related operations and their usage. Also learn diverse ways of opening, reading, and writing to files.

    • Objectives:
      • Define Reserved Keywords and Command Line Arguments
      • Describe how to Get User Input from Keyboard
      • Describe Flow Control and Sequences
      • Practice Working with Files
      • Define and Describe Dictionaries and Sets

    •
    • Topics:
      • Lists
      • Tuples
      • Indexing and Slicing
      • Iterating through a sequence
      • Functions for all sequences
      • Using enumerate()
      • Operators and keywords for sequences
      • The xrange()function
      • List comprehensions
      • Generator expressions
      • Dictionaries and sets.
      • Working with files
      • Modes of opening a file
      • File attributes
      • File methods

    •
    • Hands On:
      • List - properties, related operations
      • Tuple - properties, related operations, comparison with list
      • Dictionary - properties, related operations, comparison with list
      • Set - properties, related operations, comparison with dictionary

   Deep Dive – Functions, Sorting, Errors and Exception, Regular Expressions and Packages

    • Goal : Learn how to create generic python scripts, how to address errors/exceptions in code and finally how to extract/filter content using regex.

    • Objectives:
      • Explain Functions and various forms of Function Arguments
      • Explain Standard Library
      • Define Modules
      • Describe Zip Archives and Packaging

    •
    • Topics:
      • Functions
      • Function Parameters
      • Global variables
      • Variable scope and Returning Values
      • Sorting
      • Alternate Keys
      • Lambda Functions
      • Sorting collections of collections
      • Sorting dictionaries
      • Sorting lists in place
      • Errors and Exception Handling
      • Handling multiple exceptions
      • The standard exception hierarchy using Modules
      • The Import statement
      • Module search path
      • Package installation waysModule Aliases and Regular Expressions

    •
    • Hands On / Demo :
      • Functions - syntax, arguments, keyword arguments, return values
      • Lambda - features, syntax, options, comparison with functions
      • Sorting - sequences, dictionaries, limitations of sorting
      • Errors and exceptions - types of issues, remediation
      • Packages and module - modules, import options, sys path

   Object Oriented Programming in Python

      • Goal : Understand the Object-Oriented Programming world in Python and use of standard libraries.

      • Objectives:
          • Implement Regular Expression and its Basic Functions
          • Use Classes, Objects, and Attributes
          • Develop applications based on Object Oriented Programming and Methods
    • Topics:

    • The sys Module

      •
      • Hands On:
    • Interpreter information
    • STDIO
    • Launching external programs
    • Paths
    • Directories and filenames
    • Walking directory trees
    • Math Function
    • Random Numbers
    • Dates and Times
    • Zipped Archives
    • Introduction to Python Classes
    • Defining Classes
    • Initializes
    • Instance methods
    • Properties
    • Class methods and data
    • Static methods
    • Private methods and Inheritance
    • Regular expressions - regex library, search/match object, findall, sub, compile
    • Classes - classes and objects, access modifiers, instance and class members
    • OOPS paradigm - Inheritance, Polymorphism and Encapsulation in Python

       Debugging, Databases and Project Skeletons

    Goal : Learn how to debug, how to use databases and how a project skeleton looks like in Python.

    Objectives:

    • Debug python scripts using pdb
    • Debug python scripts using IDE
    • Classify Errors
    • Develop Unit Tests
    • Create project Skeletons
    • Implement Database using SQLite
    • Perform CRUD operations on SQLite database

    Topics:

    • Debugging
    • Dealing with errors
    • Using unit tests
    • Project Skeleton
    • Required packages
    • Creating the Skeleton
    • Project Directory
    • Final Directory Structure
    • Testing your set up
    • Using the skeleton
    • Creating a database with SQLite 3
    • CRUD operations
    • Creating a database object.

    Hands On:

    • Debugging - debugging options, logging, troubleshooting
    • Unit testing - TDD, unittest library, assertions, automated testing
    • Project skeleton - industry standard, configurations, sharable libraries
    • RDBMS - Python for RDBMS, PEP 49, CRUD operations on Sqlite

       Statistics - Machine Learning Prerequisites

    Goal : Understand the Object-Oriented Programming world in Python and use of standard libraries.

    Objectives:

    • Implement Regular Expression and its Basic Functions
    • Use Classes, Objects, and Attributes
    • Develop applications based on Object Oriented Programming and Methods

    Topics:

    • The sys Module
    • Interpreter information
    • STDIO
    • Launching external programs
    • Paths
    • Directories and filenames
    • Walking directory trees
    • Math Function
    • Random Numbers
    • Dates and Times
    • Zipped Archives
    • Introduction to Python Classes
    • Defining Classes
    • Initializes
    • Instance methods
    • Properties
    • Class methods and data
    • Static methods
    • Private methods and Inheritance

    Hands On:

    • Regular expressions - regex library, search/match object, findall, sub, compile
    • Classes - classes and objects, access modifiers, instance and class members
    • OOPS paradigm - Inheritance, Polymorphism and Encapsulation in Python

       Machine Learning using Python – Essentials

    Goal : Learn in detail about Supervised and Unsupervised learning and examples for each category.

    Objectives:

    • Define Machine Learning and understand Supervised vs Unsupervised
    • Apply Supervised Learning process flow, regression analysis
    • Apply Unsupervised Learning process flow, clustering
    • Apply Linear Regression, Multivariate Regression 
    • Measure accuracy using Mean Squared Error, Cross Validation
    • Analyze data using Pandas

    Topics:

    • Introduction to Machine Learning
    • Areas of implementation of Machine learning
    • Why Python
    • Major classes of Learning Algorithms
    • Supervised vs. Unsupervised learning
    • Inference models
    • Linear regression and mean squared error
    • Multivariate regression
    • Cross validation
    • Regression Summary
    • Introduction to Pandas
    • Creating Data frames
    • Grouping
    • Sorting
    • Plotting Data
    • Creating functions
    • Converting different formats
    • Combining data from various formats
    • Slicing/Dicing operations 

    Hands On:

    • Supervised learning - Linear Regression and RMSE, Multivariate Regression, Cross Validation
    • Pandas - Series, DataFrames, data analysis involving grouping, sorting, filtering, munging, visualization/plotting and mesh up

       Data Analysis and Machine Learning - Deep Dive

    Goal : Tackle complex machine learning problems requiring classification or clustering.

    Objectives: 
    At the end of this Module, you should be able to:

    • Feature engineer datasets using PCA, Bias/Variance analysis
    • Apply classifications algorithms like KNN, Random Forests, SVM etc.
    • Apply clustering algorithms like K-Means, Hierarchical clustering etc.
    • Compute classification and clustering metrics to ascertain model accuracy

    Topics:

    • Feature engineering
    • Dealing with categorical data
    • Dealing with text data
    • Using encoders
    • Count, TF-IDF Vectorizer
    • Bias/Variance tradeoff
    • Principal Component Analysis (PCA)
    • KNN
    • Decision Trees
    • Random Forests
    • Ensemble Learning
    • Averaging and boosting algorithms
    • Random Forest classifier
    • Support Vector Machines (SVM)
    • Support Vector Classifier
    • Accuracy measures - AUC, ROC, Confusion Matrix, Log Loss
    • Clustering algorithms and accuracy measures
    • K-Means clustering
    • Silhouette coefficient
    • Hierarchical clustering using Dendrogram
    • Density-based clustering using DBSCAN

    Hands On:

    • Data analysis activity using live datasets from Google Finance
    • Encoders, vectorizers, PCA, KNN, CART, Random Forest Ensemble, SVM, Clustering, Accuracy measures using Metrics

       Scalable Machine Learning using Spark

    Goal : Learn Spark basics and run machine learning models over Spark.

    Objectives: 
    At the end of this Module, you should be able to discuss:

    • Apache Spark - Concepts, RDD, MLLib, Data frames
    • Transformations, Actions, Shuffling, Persistence and Data Removal
    • Shared variables - accumulators and broadcast
    • Spark SQL and Data frames
    • Spark MLlib
    • Regression, Classification & Clustering with PySpark

    Topics:

    • Apache Spark introduction
    • Spark engine
    • Spark core API
    • Spark libraries
    • SparkContext and SparkConf
    • Concepts - RDD, Shuffling and Persistence
    • RDD transformations and actions
    • Shared variables - Accumulators, Broadcasts
    • Spark SQL and Dataframes
    • Spark MLlib
    • Regression with PySpark
    • Classification with PySpark
    • Clustering with PySpark

    Hands On:

    • SparkContext and SparkConf
    • RDDs, Accumulators, Broadcasts, data removal
    • Spark SQL DataFrames
    • Regression, Classification and Clustering using Spark MLlib

       Web Scraping in Python and Project Work

    Goal : Discuss about the powerful web scraping using Python and discuss a real-world project.

    Objectives:

    • Discuss web scraping and its advantages
    • Discuss Steps Involved in Web Scraping
    • Use BeautifulSouppackage and its functions
    • Scrape IMDB webpage
    • Fetch Streaming Tweets from Twitter
    • Perform Sentiment Analysis on tweets Fetched from Twitter and determine which is more popular Ferrari or Porsche

    Topics:

    • Web scraping
    • Introduction to Beautiful soup package
    • How to scrape webpages
    • A Real-world project showing scrapping data from Google finance and IMDB.

    Hands On:

    • Scraping - BeautifulSoup and its functions, pulling content using regex, restricting content using SoupStrainer
    • Scraping IMDB, Reddit
    • Tweet sentiment analysis using Twitter API for Python