DATA SCIENCE & ML USING PYTHON IN NOIDA

DATA SCIENCE & ML USING PYTHON TRAINING IN NOIDA / Greater Noida

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Are you Looking for the Best Institute for Data Science ML using Python training in Noida / Greater Noida? DUCAT offers Data Science ML using Python training classes with live project by expert trainer in Noida. Our Machine learning with Python training program in Noida is specially designed for Under-Graduates (UG), Graduates, working professional and also for Freelancers. We provide end to end learning on Machine learning with Python Domain with deeper dives for creating a winning career for every profile.

This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science! Python is a general-purpose interpreted, interactive, object-oriented and high-level programming language. Currently Python is the most popular Language in IT. Python adopted as a language of choice for almost all the domain in IT including Web Development, Cloud Computing (AWS, OpenStack, VMware, Google Cloud, etc.. ), Infrastructure Automations , Software Testing, Mobile Testing, Big Data and Hadoop, Data Science, etc. This course to set you on a journey in python by playing with data, creating your own application, and also testing the same.

An Introduction to Python

  • Brief History
  • Why Python
  • Where to use

Beginning Python Basics

  • The print statement
  • Comments
  • Python Data Structures & Data Types
  • String Operations in Python
  • Simple Input & Output
  • Simple Output Formatting

Python Program Flow

  • Indentation
  • The If statement and its' related statement
  • An example with if and it's related statement
  • The while loop
  • The for loop
  • The range statement
  • Break & Continue
  • Assert
  • Examples for looping

Functions & Modules

  • Create your own functions
  • Functions Parameters
  • Variable Arguments
  • Scope of a Function
  • Function Documentation/Docstrings
  • Lambda Functions & map
  • An Exercise with functions
  • Create a Module
  • Standard Modules

Exceptions

  • Errors
  • Exception Handling with try
  • Handling Multiple Exceptions
  • Writing your own Exceptions

File Handling

  • File Handling Modes
  • Reading Files
  • Writing & Appending to Files
  • Handling File Exceptions
  • The with statement

Classes In Python

  • New Style Classes
  • Variable Type
  • Static Variable in class
  • Creating Classes
  • Instance Methods
  • Inheritance
  • Polymorphism
  • Encapsulation
  • Scope and Visibility of Variables
  • Exception Classes & Custom Exceptions

Regular Expressions

  • Simple Character Matches
  • Special Characters
  • Character Classes
  • Quantifiers
  • The Dot Character
  • Greedy Matches
  • Grouping
  • Matching at Beginning or End
  • Match Objects
  • Substituting
  • Splitting a String
  • Compiling Regular Expressions
  • Flags

Data Structures

  • List Comprehensions
  • Nested List Comprehensions
  • Dictionary Comprehensions
  • Functions
  • Default Parameters
  • Variable Arguments
  • Specialized Sorts
  • Iterators
  • Generators
  • The Functions any and all
  • The with Statement
  • Data Compression
  • Closer
  • Decorator

Writing GUIs in Python

  • Introduction
  • Components and Events
  • An Example GUI
  • The root Component
  • Adding a Button
  • Entry Widgets
  • Text Widgets
  • Checkbuttons
  • Radiobuttons
  • Listboxes
  • Frames
  • Menus
  • Binding Events to Widgets

Thread In Python

  • Thread life Cycle
  • Thread Definition
  • Thread Implementation

Network Programming

  • Introduction
  • A Daytime Server
  • Clients and Servers
  • The Client Program
  • The Server Program
  • Client and Server Architecture
  • Threaded Server

Python & MySQL Database Connection

  • Introduction
  • Installation
  • DB Connection
  • Creating DB Table
  • Insert, Read,Update, Delete operations
  • Commit & Rollback operation
  • Handling Errors
  • Mini project on Python with TKinter

Data Science & Machine Learning

Course Overview

  • Overview of Data science
  • What is Data Science
  • Different Sectors Using Data Science

Mathematical Computing with python(NumPy)

  • Introduction to Numpy
  • Activity-Sequence
  • Creating and Printing an ndarray
  • Class and Attributes of ndarray
  • Basic Operations
  • Activity – Slicing
  • Copy and Views
  • Mathematical Functions of Numpy
  • Advance Slicing
  • Transpose and arance
  • Searching

Data Manipulation with Pandas

  • Introduction of Pandas
  • Data Types in Pandas
  • Understanding Series
  • Understanding DataFrame
  • View and Select Data Demo
  • Missing Values
  • Data Operations
  • File Read and Write Support
  • Pandas Sql Operation

Python for Data Visualization-Matplotlib

  • Introduction to Matplotlib
  • Matplotlib Part 1 Set up
  • Matplotlib Part 2 Plot
  • Matplotlib Part 3 Next steps
  • Matplotlib Exercises Overview
  • Matplotlib Exercises – Solutions

Introduction to Machine Learning

  • Introduction to Machine Learning
  • Machine Learning with Python

Linear Regression

  • Linear Regression Theory
  • Model selection Updates for SciKit Learn
  • Linear Regression with Python
  • Linear Regression Project Overview and Project Solution

Logistic Regression

  • Logistic Regression Theory – Introduction
  • Logistic Regression with Python – Part 1 – Logistics
  • Logistic Regression with Python – Part 2 – Regression
  • Logistic Regression with Python – Part 3 – Conclusion
  • Logistic Regression Project Overview and Project Solutions

K Nearest Neighbours

  • KNN Theory
  • KNN with Python
  • KNN Project Overview and Project Solutions

Decision Trees and Random Forests

  • Introduction to Tree Methods
  • Decision Trees and Random Forest with Python
  • Decision Trees and Random Forest Project Overview
  • Decision Trees and Random Forest Solutions Part 1
  • Decision Trees and Random Forest Solutions Part 2

Support Vector Machines

  • SVM Theory
  • Support Vector Machines with Python
  • SVM Project Overview
  • SVM Project Solutions

K Means Clustering

  • K Means Algorithm Theory
  • K Means with Python
  • K Means Project Overview
  • K Means Project Solutions

Machine Learning Basics

  • Converting business problems to data problems
  • Understanding supervised and unsupervised learning with examples
  • Understanding biases associated with any machine learning algorithm
  • Ways of reducing bias and increasing generalisation capabilites
  • Drivers of machine learning algorithms
  • Cost functions
  • Brief introduction to gradient descent
  • Importance of model validation
  • Methods of model validation
  • Cross validation & average error

Generalised Linear Models in Python

  • Linear Regression
  • Regularisation of Generalised Linear Models
  • Ridge and Lasso Regression
  • Logistic Regression
  • Methods of threshold determination and performance measures for classification score models

Tree Models using Python

  • Introduction to decision trees
  • Tuning tree size with cross validation
  • Introduction to bagging algorithm
  • Random Forests
  • Grid search and randomized grid search
  • ExtraTrees (Extremely Randomised Trees)
  • Partial dependence plots

Boosting Algorithms using Python

  • Concept of weak learners
  • Introduction to boosting algorithms
  • Adaptive Boosting
  • Extreme Gradient Boosting (XGBoost)

Support Vector Machines (SVM) & kNN in Python

  • Introduction to idea of observation based learning
  • Distances and similarities
  • k Nearest Neighbours (kNN) for classification
  • Brief mathematical background on SVM/li>
  • Regression with kNN & SVM

Unsupervised learning in Python

  • Need for dimensionality reduction
  • Principal Component Analysis (PCA)
  • Difference between PCAs and Latent Factors
  • Factor Analysis
  • Hierarchical, K-means & DBSCAN Clustering

Text Mining in Python

  • Gathering text data using web scraping with urllib
  • Processing raw web data with BeautifulSoup
  • Interacting with Google search using urllib with custom user agent
  • Collecting twitter data with Twitter API
  • Naive Bayes Algorithm
  • Feature Engineering with text data
  • Sentiment analysis

OpenCV

  • Basic of Computer Vision & OpenCV
  • Images Manipulations
  • Image Segmentation
  • Object Detection
  • Face, People and Car Detection
  • Face Analysis and Fulters
  • Machine Learning in Computer Vision
  • Motion Analysis & Object Tracking
  • Project on OpenCV
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