Python Certification Training

arrow Upcoming Batches

25 AugSat - Sun (6 Weeks)08:00 AM - 10:30 AM (IST)* $25 off * $475 $450
22 SepSat - Sun (6 Weeks)08:00 AM - 10:30 AM (IST)* $25 off * $475 $450

arrow Tools Covered in Python Certification Training

python-logonumpyjupyter-notebookpandas-logomatplotlibscikit-learnDjangoflask-pythonpysparktensor-flowscipyNLTKgithub-cardpython-beautiful-soupmysql

 

arrow Curriculum

  • History
  • Setup Development Environment
  • Create your first Python Script
  • Explore Python IDEs
  • Variables, Keywords, Operators
  • Data types
  • Flow control
  • Lists
  • Tuples
  • Sets
  • Dictionaries
  • Indexing
  • Slicing
  • Xrange
  • Files overview
  • Working with files
  • File Modes
  • File attributes
  • File Methods
  • Directories
  • Directory Methods
  • Functions introduction
  • Arguments
  • Global Variables
  • Scope
  • Lambda Functions
  • Sorting
  • Regular Expressions
  • Packages, modules, path
  • What is an exception
  • Handle Single Exception
  • Handle Multiple Exceptions
  • Exception hierarchy using modules
  • Exception handling strategy
  • Procedural programming
  • Functional programming
  • Object oriented programming
  • Classes
  • Methods
  • Attributes
  • Abstraction
  • Encapsulation
  • Polymorphism
  • Inheritance
  • Static methods
  • Design patterns
  • Introduction to debugging
  • Debug using pdb
  • Debug using IDE
  • Error classification
  • Logging
  • Troubleshooting
  • Unit Testing
  • Getting Started
  • Getting Started
  • Git Basics
  • Git Branching
  • Git Tools
  • Git commands
  • Installation
  • Introduction to Database
  • RDBMS
  • Basics of SQL
  • Create Schema and Table
  • Use Python to insert data
  • Use Python to update data
  • Use Python to extract data
  • World Wide Web and http
  • Introduction to REST
  • Microservices
  • Service oriented architecture
  • Installation
  • Quick Start
  • Flaskr
  • Creating the folders
  • Database schema
  • pplication setup code
  • Database connections
  • View functions
  • Templates
  • Adding Style
  • Testing the Application
  • Configuration Handling
  • Signals
  • Pluggable Views
  • Application Context
  • Request Context
  • Modular Application
  • Command Line
  • Development Server
  • Web applications introduction
  • First Steps
  • Model Layer
  • View layer
  • Template layer
  • Forms
  • Development process
  • Administration
  • Security
  • Internationalization and localization
  • Performance and Optimization
  • Geographic framework
  • Common Web Application tools
  • Other core functionalities
  • The basics
  • Array creation
  • Printing arrays
  • Basic operations
  • Indexing, Slicing and Iterating
  • Shape manipulation
  • Copies and Views
  • Functions and methods overview
  • Fancy indexing and index tricks
  • Linear Algebra
  • Tricks and Tips
  • Introduction
  • Basic functions
  • SciPy.special
  • Integration
  • Optimization
  • Interpolation
  • Fourier Transformations
  • Signal processing
  • Sparse Eigenvalue
  • Compressed Sparse Graph Routines
  • Spatial data structures and algorithms
  • Multidimensional image processing
  • Introductions
  • Idioms
  • Selection
  • Multi Indexing
  • Missing data
  • Grouping
  • Time series
  • Merge
  • Plotting
  • Data In/Out          
    • Csv
    • Sql
    • Excel
    • HTML
    • Binary files
    •        
  • Computation
  • Timedeltas
  • Aliasing Axis Names
  • Creating Example Data
  • Introductory
  • Sample Plots
  • Customizing Matplotlib
  • Images
  • Usage guide
  • Tutorial
  • Lifecycle of a plot
  • Intermediate
  • Styling with cycler
  • Legend Guide
  • Customizing Location of Subplot using GridSpec
  • Artist Tutorial
  • Tight Layout Guid
  • Advanced
  • Path effects guide
  • Path Tutorial
  • Transformations Tutorial
  • Colors
  • Specifying Colors
  • Customized colors bar tutorial
  • Colormap normalization
  • Colormap in matplotlib
  • Text
  • Annotations
  • Text Rendering with LaTeX
  • Typesetting
  • Writing Mathematical Expressions
  • Text introduction
  • Text properties and layout
  • Mean
  • Median
  • Mode
  • Standard Deviation
  • Normal Distribution
  • Z-Score
  • Machine Learning
  • Supervised Learning
  • Unsupervised learning
  • Regression
  • Classification
  • Loading a dataset
  • Learning and predicting
  • Model persistence
  • Conventions
  • What is NLP?
  • Language processing & Python
  • Accessing Text Corpora and Lexical Resources
  • Processing Raw Text
  • Writing structured programs
  • Categorizing and tagging words
  • Learning to classify Text
  • Extracting information from Text
  • Analyzing sentence structure
  • Building feature based grammars
  • Analyzing the meaning of sentences
  • Managing linguistic data
  • Next steps
  • Neural networks
  • Getting started with TensorFlow
  • Tensors
  • Computational Graph
  • Training
  • Estimation
  • Next Steps
  • Spark Introduction
  • Spark Resilient Distributed Datasets (RDD)
  • Transformation and Actions in Spark
  • Caching, Accumulators and UDF
  • PySpark Shell
  • Reading a file
  • View the RDD contents
  • RDD Partitions
  • Installation
  • Getting started
  • Navigating the tree
  • Modify the tree
  • Output
  • Encoding
  • Troubleshooting

 

arrow What can you do after this course?

We are training you in this course to understand Python in depth. You will be able to become a full stack developer and create end to end web applications. You will also learn to create RESTful web services that is the core of service oriented architecture. You will be able to create the complete backend for a mobile app. Last but not the least, we will give you an in depth understanding of data handling and data science aspects of Python. This will set you up to kick start your career in data science.

 

Following are the key highlights:

  • You will learn A-Z of Python as a programming language and become proficient Python Programmer
  • You will be able to use DJango and create a full stack web application.
  • You will be able to use Flask to create RESTful web services
  • Perform data scientist operations of machine learning, NLP and deep learning
  • Crawl the web and parse the web pages of the world wide web using BeautifulSoup
  • You will be able to independently use the scientific Python libraries like NumPy, SciPy, Pandas, Matplotlib, Scikit-learn, NLTK

 

arrow FAQ

Yes you can.
Our course doesn’t require a prior knowledge of any programming. We devote the entire course in giving an in depth and hands on understanding of Python. In fact most of our students don’t know any programming before they join our course. This course is ideal for people who do not have any programming experience.

Yes.
Before the training starts we send out a detailed step by step guide on what to do before your first class. Our handbook will give all the links, resources and guide necessary to install the relevant software on all the popular operating systems. It takes about 15 minutes to go through the instructions and make the necessary installations before the class starts.

Sometimes students face issues installing and we help them get over their issue. In short you will not have any issue in getting setup before the training starts.

All the downloads and installs are free and open source.

Python is one of the Top 3 programming languages of the world. The main reason for it to hold this spot is its simplicity and ease of use. If there is any programming language that somebody wants to pick for understanding software, Python is a good choice. It is simple, data friendly and last but not the least open source.

There are many reasons why Python is ideal for Data Science. Of the many the most important ones are as follows:

One of the top 5 programming languages of the world – You can do a google search for top programming languages and you will find Python in the top 5 list. Its a program that is immensely popular among data scientists. Its the default programming language for machine learning, data analysis and data munging. All the leading libraries of today provide Python extensions. For example Apache Spark can be accessed using Python.

Simple and easy to learn – Python is a simple programming language. It is easy to setup and get started on it quickly. Its syntax is simple and easy to understand. It can be picked up quickly by any new programmer within hours.

Open Source – Its an open source programming language. Its freely available and you don’t have to pay any licensing fees.

Cross Platform – It is an interpreted language and it can be run on all major operating systems. We have students that use Mac, Windows, Linux, Ubuntu etc and it works exactly the same in all the platforms.

There are many more reasons that make Python a great choice for data science but the ones above are the most important ones that come to our mind.

The most important cientific libraries are numPy, Sci-Py, Pandas, MatplotLib & scikit-learn. numPy and Pandas makes data loading and data manipulation a breeze.

MatplotLib gives intuitive out of the box charts that makes data visualization a treat to the eyes. Scikit-learn is packed with all the important supervised and unsupervised learning algorithms.

In our course we cover these libraries in detail to get you ready for real world.

Our instructors are professionals with more than 15 years of experience and are currently working on projects for fortune 500 companies.

Our instructors not only bring the knowledge of machine learning to the course but with specially crafted assignments bring the latest and greatest in machine learning being done in the biggest companies across the globe.

Our course will not only make you Python champ but will make you ready to work on real life project.

The typical duration of the course is 5-6 Weeks. Its the right amount of time that lets you absorb the theory and do the practice exercises. This structure is best suited to absorb the challenging concepts of machine learning using Python.

The course will have 3 types of hands on activities:

1. The instructor will do exercises in the classes live to show key concepts

2. During the live classroom the instructor will give out assignments to students and have them work on it. Students are asked to present their solution to other students during these exercise which further bolsters the understanding of the student

3. We give detailed and well researched home assignments that students work on during the week to revise the concepts taught in the class.

Yes this course is designed with working professionals in mind. It is conducted on weekends to make sure that it doesn’t interfere with the existing work schedule of working professionals. The live classroom sessions are conducted on Saturday and Sunday.

While we like all of our students to attend all the sessions, sometimes there are are some students that are not able to attend one or two class. We make sure we record all our sessions in high definition and share the videos in our state of the art learning management system for them to access it for lifetime. Together with session videos we will also share home assignments, handouts and other reference materials for you to keep and use for your life time.

Don’t worry if you miss a session, you can always attend the missed session in the next batch.

All our classroom sessions are recorded in high definition. The session videos are processed in our state of the art lab and made available to students for them to keep for lifetime. Together with session videos we will also share home assignments, handouts and other reference materials for you to keep and use for your life time

When the instructor is giving the lecture to the class, the session will be recorded. After the class the recorded video will be uploaded to our state of the art e-learning portal and its access will be given to you for lifetime.

Together with session videos we will also share home assignments, handouts and other reference materials for you to keep and use for your life time.

We have an award winning and popular infographic resume builder. Its very easy to use and its premium features are available to our students free of charge. Using our resume builder you can create stunning resumes. You can view some samples using the following link: https://mcalglobal.com/socialresume/user/signup.htm

We are training you in this course to understand Python in depth. You will be able to become a full stack developer and create end to end web applications.

You will also learn to create RESTful web services that is the core of service oriented architecture. You will be able to create the complete backend for a mobile app.

Last but not the least, we will give you an in depth understanding of data handling and data science aspects of Python. This will set you up to kick start your career in data science.

We are one of the only training companies that stay in touch with our alumni even after the course is over. We always encourage our students to reach out to us if they need any help. We constantly update our training course. We make sure to share those with our old students so that they are updated with the latest changes in the course. We have a blog where we put valuable material for our community to use and benefit from. You can access our blog at: https://www.mcal.in/blog/

We do create separate batches for groups. Sometimes there are working professionals who would like to have the timings tweaked to fit their needs. There are companies we train who like to have a customized curriculum created to suit their needs.

If you have any customized need, we urge you to contact us at ml@mcal.in so that we talk and look for ways to best serve you.