Python Training



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Target Audience

10th & 12th class students Undergraduates, Graduates Post-Graduates & Job aspirants

Pre-requisites

C & OOPS Concepts would be an advantage

Duration

45 days Duration, Classes taken 5 Days a week


Batches

5 day in a week, Weekend batch, Only Sunday batch

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Fee Structure

Starting from 5000/-

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    A hands on course for mastering the essentials of Python programming language and using it to solve real-world problems.

  • Getting Started
    • Quick introduction to Python programming language, including basic data types, functions, methods, modules, conditionals and loops.

  • Working with Data
    • Introduction to Python’s powerful data structures including, lists and dictionaries. Also covers list comprehensions, processing text and working with files.

  • Modules
    • Introduction to Python module system, importing modules, writing custom modules, documenting code using docstrings and installing third-party modules. This section concludes with a tour of Python standard library covering modules related to file system handling, downloading stuff from web, JSON and working with APIs.

  • Classes and Objects
    • Introduction to object-oriented programming with Python. Describes how classes offer a different programming model. Covers writing classes, object creation, inheritance and exception handling.

  • Testing Python Programs
    • Introduction to automated testing using unittest and py.test modules.

    An intensive hands-on course that dives deep into the Python internals, advanced features like decorators, generators, meta classes etc. and best practices of Python programming language.

  • Warm up
    • Review of Python programming language. Topics covered are lists, dictionaries, list comprehensions and importing modules.

  • Higher ­Order Functions and Decorators
    • A deeper look at functions in Python. Covers functions with variable arguments, keyword and default arguments, scoping rules, recursion, higher order functions and decorators.

  • Iterators and Generators
    • Introduction to Iterators, generators and generator expressions with emphasis on working with large data and how these techniques help to make code more readable. Also explores coroutines using the new async and await syntax and async programming.

  • Deeper look at classes and objects
    • Understanding classes and objects at a deeper level. Covers old-style and new-style classes, static methods, class methods, special methods for operator overloading, slots, descriptors, context managers and meta classes.

  • Writing Python Libraries
    • Covers best practices of writing, documenting, testing and distributing python libraries.

Django Syllabus:-

Introduction to Django

  • What is Django?
  • Django and Python
  • Django’s take on MVC: Model, View and Template
  • DRY programming: Don’t Repeat Yourself
  • How to get and install Django

Deploying Django

  • Packaging Django Applications
  • Introduction to python packaging
  • pip and distribute
  • pip requirements
  • Scaling Django Applications
  • Separating out the DB server
  • Separating out the media server
  • Load Balancing and Redundancy
  • Django setup via virtualenv and

Getting started with Django

  • About the 3 Core Files:
    • models.py
    • urls.py
    • views.py
  • Setting up database connections
  • Managing Users & the Django admin tool
  • Installing and using ‘out of the box’ Django features

Django URL Patterns and Views

  • Designing a good URL scheme
  • Generic Views

Forms Framework

  • Widget, Field, Form and Form Media
  • Tying form objects with Views
  • Custom Validation Rules
  • Customizing the look and feel
  • Creating forms from models

Template Engine

  • Using the template system
  • Template, Context, Variables, Template Tags and Filters
  • HTML escaping
  • Using templates in views
  • Template Inheritance
  • Template Loaders

    • Extending the Template System

    • Creating a template library
    • Writing custom template filters
    • Writing custom template tags
    • Writing custom template loaders

    Middleware

    • Session handling
    • Enabling sessions
    • Configuring session engine
    • Using sessions in views

    Request and Response Objects

    • HttpRequest object
    • UploadedFile object
    • QueryDict object
    • HttpResponse object

    Admin site

    • Activating the admin site
    • Customizing the admin form
    • Adding related objects

    Authentication

    • Authentication Overview: Users, Permissions and Groups
    • User Management
    • How passwords are stored
    • Using authentication in web requests — login, logout and password changes

    Caching

    • Setting up the cache
    • Various caching backends available
    • Cache configuration
    • Per-Site Cache
    • Per-View Cache
    • Caching Template Fragments
    • Caching specific objects using low-level Cache API
    • Handling issues with upstream caches
    • Using Vary Headers

    Media Storage

    • Managing media files
    • Default file storage
    • Custom storage systems
    • Django storages plugin

    Numpy

    • Introduction
    • Installation
    • NDARRAY
    • Datatypes
    • Array
    • Indexing and Slicing
    • Broadcasting
    • Binary operations
    • String Functions
    • Mathematical Functions
    • Arithmetic Operations
    • Statistical Functions
    • Sort and Search
    • Byte Views
    • Linear Algebra
    • Matrix Library
    • I/O operations

    SciPy

    • Introduction
    • Basic functions
    • Optimization and Minimization
    • Interpolation
    • Integration
    • Statistics
    • Special functions (scipy.special)
    • Complex Functions
    • Linear Algebra (scipy.linalg)
    • Compressed Sparse Graph Routines (scipy.sparse.csgraph)
    • Spatial data structures and algorithms (scipy.spatial)
    • Statistics (scipy.stats)
    • File IO (scipy.io)

    Pandas

    • Introduction
    • Series
    • DataFrame
    • Panel
    • Basic Functionality
    • Descriptive Statistics
    • Function Application
    • Reindexing
    • Iteration
    • Sorting
    • Working with Text Data
    • Options and Customization
    • Indexing and Selecting Data
    • Statistical Function
    • Windows Function
    • Aggregation
    • Missing Data
    • GroupBy
    • Merging/Joining
    • Time Delta
    • Categorial Data
    • I/O

    MatPlotLib

    • Introduction
    • Plotting
    • Bar Charts
    • Horizontal Bar Charts
    • Broken Bar Charts
    • Box and Whisker Plots
    • Contour Plots
    • Histogram
    • Log plots
    • Pie Charts
    • Polar Plots
    • Arrow Plots
    • Scatter Plots
    • Sparsity Pattern Plots
    • Stem Plots
    • Date Plots
    1. Introduction:
      What is Machine Learning?
      What is Artificial Intelligence?

    2. Landscape of problems
      1. Supervised versus unsupervised learning
      2. Classification versus forecasting
      3. Classifying data sets: Tall, wide, and dense data
      4. Predictive modeling/ policy intervention

    3. Python basics
      1. Anaconda
      2. Spyder
      3. Numpy, Scipy
      4. Matplotlib
      5. Scikit Learn

    4. Model complexity
      1. Overfitting
      2. Training/validation/testing

    5. Simplest models
      1. Getting started with scikit-Learn
      2. K nearest neighbors
      3. Application: Iris data
      4. Linear regression

    6. Controlling model complexity
      1. Regularization
      2. Ridge regression
      3. Lasso regression

    7. Classification
      1. Logistic regression
      2. Support vectors
      3. Naive Bayes
      4. Linear discriminant
      5. Multi(k>2) class problems

    8. Decision trees
      1. Controlling complexity again
      2. Feature importance and reading trees
      3. Bagging predictors
      4. Multiple trees (The Random Forest)
      5. Boosting

    9. Recommendation systems
      1. Content based recommendation
      2. Collaborative filtering

    10. Clustering
      1. K-means and Hierarchical clustering
      2. Principal component analysis

    11. Model evaluation
      1. Cross validation
      2. Evaluation metrics
      3. Tuning Models
    1. Introduction:
      What is Machine Learning?
      What is Artificial Intelligence?

    2. Landscape of problems
      1. Supervised versus unsupervised learning
      2. Classification versus forecasting
      3. Classifying data sets: Tall, wide, and dense data
      4. Predictive modeling/ policy intervention

    3. Python basics
      1. Anaconda
      2. Spyder
      3. Numpy, Scipy
      4. Matplotlib
      5. Scikit Learn

    4. Model complexity
      1. Overfitting
      2. Training/validation/testing

    5. Simplest models
      1. Getting started with scikit-Learn
      2. K nearest neighbors
      3. Application: Iris data
      4. Linear regression

    6. Controlling model complexity
      1. Regularization
      2. Ridge regression
      3. Lasso regression

    7. Classification
      1. Logistic regression
      2. Support vectors
      3. Naive Bayes
      4. Linear discriminant
      5. Multi(k>2) class problems

    8. Decision trees
      1. Controlling complexity again
      2. Feature importance and reading trees
      3. Bagging predictors
      4. Multiple trees (The Random Forest)
      5. Boosting

    9. Recommendation systems
      1. Content based recommendation
      2. Collaborative filtering

    10. Clustering
      1. K-means and Hierarchical clustering
      2. Principal component analysis

    11. Model evaluation
      1. Cross validation
      2. Evaluation metrics
      3. Tuning Models

      Part I : Applied Math and Machine Learning Basics
      1. Linear Algebra & Probability
      1.1 Scalars, Vectors, Matrices and Tensors
      1.2 Multiplying Matrices and Vectors
      1.3 Identity and Inverse Matrices
      1.4 The Determinant
      1.5 Example: Principal Components Analysis
      1.6 Random Variables & Probability Distributions
      1.7 Conditional Probability
      1.8 Bayes’ Rule

      2. Numerical Computation
      2.1 Overflow and Underflow
      2.2 Gradient-Based Optimization .
      2.3 Constrained Optimization

      3. Machine Learning Basics
      3.1 Learning Algorithms
      3.2 Capacity, Overfitting and Underfitting
      3.3 Hyperparameters and Validation Sets
      3.4 Estimators, Bias and Variance
      3.5 Maximum Likelihood Estimation
      3.6 Supervised Learning Algorithms
      3.7 Unsupervised Learning Algorithms
      3.8 Stochastic Gradient Descent
      3.9 Building a Machine Learning Algorithm

      Part II : Neural Network Basics
      1. Biological Neural Networks
      1.1 The vertebrate nervous system
      1.2 Peripheral and central nervous system
      1.3 The neuron & Components
      1.4 Electrochemical processes in the neuron
      1.5 Receptor cells
      1.6 Information processing within the nervous system

      2. Components of Artificial Neural Networks
      2.1 Components of neural networks
      2.2 Network topologies
      2.3 Orders of activation
      2.4 Input and output of data

      3. Perceptron, Backpropagation and it’s Variants
      3.1 The singlelayer perceptron
      3.2 Linear separability
      3.3 The multilayer perceptron
      3.4 Backpropagation of error
      3.5 Initial configuration of a multilayer perceptron

      Part III : Introduction: Deep Neural Networks
      1. Deep Feedforward Networks
      1.1 Gradient-Based Learning
      1.2 Hidden Units
      1.3 Architecture Design
      1.4 Back-Propagation and Other Differentiation Algorithms

      2. Regularization for Deep Learning
      2.1 Parameter Norm Penalties
      2.2 Regularization and Under-Constrained Problems
      2.3 Dataset Augmentation
      2.4 Noise Robustness
      2.5 Semi-Supervised Learning
      2.6 Multi-Task Learning
      2.7 Early Stopping
      2.8 Parameter Tying and Parameter Sharing
      2.9 Bagging and Other Ensemble Methods
      2.10 Dropout
      2.11 Adversarial Training
      2.12 Tangent Distance, Tangent Prop, and Manifold Tangent Classifier

      3. Convolutional Neural Networks
      3.1 The Convolution Operation
      3.2 Motivation
      3.3 Pooling
      3.4 Convolution and Pooling as an Infinitely Strong Prior
      3.5 Variants of the Basic Convolution Function
      3.6 Structured Outputs
      3.7 Data Types
      3.8 Efficient Convolution Algorithms

      Part IV : Segmentation: Some Other Deep Networks
      1. U-Net
      2. E-Net
      3. Seg-Net
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    Kharghar Navi Mumbai, Maharashtra-410210
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