Python, AI and Machine Learning Online Training India

The Functional Benefits That You Will Reap By Learning the Python Language
Python is a type of Machine AI language. Nowadays, its relevance has increased much. Python is one of the basic formats of another machine language that you need to learn before proceeding to learn other machine languages. Companies like Google, IBM, Nokia, Yahoo and many more are the pioneer of such language.
Benefits to look for
These Companies are the dream destination of every IT students out there. That is why we specialize in the comprehensive training of the Pioneer language at our learning portal. We have picked up advantages why you must enroll for a course in Python. Readout:
 The platformlike big data, data analysis, automation and data mining entirely rely on Python. If you are planning to start a career in Big Data, then learning Python will be a boon for you.
 Even the students who have just taken up the programming course or a nonIT guy can learn Python. It is straightforward and easy to determine compared to other Machine Language. For the multiprogrammer, who aspires to work in organizations relying upon team performance, learning Python is near to compulsion.
 Unlike Java and C++, Python provides more flexibility in the coding scenario. It helps the coder to stay more focused on the target that is set before them.
 The machine language is forefront in developing the prototypes as with basic knowledge, and it is easy to read.
Apart from these functional; advantages, it must also be noted that the Python language is high in demand in most of the reputed companies as mentioned earlier. So students, join the Python learning course at pvvtechnologies and rise up to the career ladder.

Introductio of Python
Overview of Python
Why Python ?
When to use Python?
Python types?
Python Installation
Setting Environment Install Python Windows, UNIX, Linux.
Running basic Python commands.
Basic Syntax/ Construct of Python
“Programming(interactive/script),identifiers,Reserved words, line/indentation,multi
line statements.”
Accessing/Parsing command line arguments.
Python Variable Types:
Variables and Naming rules
Builtin Data Types in Python – Numeric: int, float, complex.
Sequence Types: list, tuple, range
Text Sequence: Str (String).
Set Types: Set, Forzenset
Mapping Types: Dictionary
Data type Conversions between builtin types
Constants: False, True, None, NotImplemented, Ellipsis,debug.
Python Basic Operators:
Basic Operators: Arithmetic, Comparison, Assignment, Identity, Logical, Bitwise, Membership.
Python Operators Precedence: highest to lowest
Python Decision Making:
if, else, nested if, range(), break, continue, elif, Single Statement Suites.
Python Loops:
while, for, Iterating by Sequence Index, nested, Loop Control Statements, break.
Python Functions:
Builtin Functions : len(),slice(),zip() ,random()etc
User Defined Functions : How to create / call a function ,Function arguments
“Anonymous Function: Lambda”
Python Strings:
“Accessing Values in Strings, Updating Strings, Escape Characters, String Formatting
Operator, Builtin String Methods.”
Python Lists:
“Accessing Values in Lists, Basic List Operations, Builtin List Functions & Methods.”
List Slicing ,List comprehension ,sorting,deletion
Python Sets & Tuples :
Python Sets and Tuples and its operations.
Python Dictionaries :
Accessing Values in dictionary, Basic dictionary Operations, Builtin dictionary Functions & Methods.
Python Modules & Packages :
Exploring Builtin modules, writing modules
Packages and create your own packages
Python Classes/Objects
Creating Classes, Class Inheritance, Objects, and Instances
Encapsulation of data, Functions vs Methods
Iterators, Generators and its expressions
Python Errors & Exceptions :
Syntax Errors, Exceptions
Handling and Raising an exception, UserDefined Exception
Python Standard Libraries :
Operating System(OS) Interface, Command Line arguments
Regular Expression (String Pattern matching)
Date and Time, Mathematics
Networking: Sending Email, Multithreading, GUI Programming.
Python File Handling
Open a File, Read from a File, Write into a File, File Position, Looping over a file object.
Pickle (Serialize and Deserialize Python Objects).
Shelve (Python Object Persistence)
Machine Learning
NumPy(Mathematical computing with python)
“Arrays and Matrices, NDarray object, Array indexing, Datatypes, Array math
Broadcasting, Std Deviation, Conditional Prob, Covariance, and Correlation.”SciPy(Scientific computing with python)
Builds on top of NumPy, SciPy and its characteristics, subpackages.
Cluster, ﬀtpack, linalg, signal, integrate, optimize, stats; Bayes Theorem using SciPy.Data Visualization (Matplotlib)
Plotting Graphs and Charts (Line, Pie, Bar, Scatter, Histogram, 3D).
Subplots.
The Matplotlib API.
Data Analysis and Data Manipulation with Python (Pandas)
Data frames, NumPy array to a data frame.
Import Data (csv, json, excel, sql database).
“Data operations: View, Select, Filter, Sort, Group, Cleaning, Join/Combine,
Handling Missing Values.”
Machine Learning
“Introduction to Machine Learning(ML): Definition, Concepts and Terminology, Lifecycle ”
Problem categories of ML : Classification ,Clustering,Regression,Optimization
Learning SubFields: Supervised, Unsupervised, SemiSupervised, Reinforcement, Deep learning
“Basic Performance measures: MSE, MAE, NMSE, ROC / AUC, Confusion Matrix
Accuracy, Precision / Recall etc.”
Installation of Python Packages for ML and Setting up Environment ( Anaconda Distribution)
Supervised Algorithms: Linear, Logistic, CART, Naïve Bayes, KNN, Decision Tree, Random Forest, SVM and with the case study(for all)
Unsupervised Algorithms: KMeans, PCA and with the case study(for all)
Recommender Systems
Dimensionality Reduction
“Natural Language Processing, Machine
Learning (ScikitLearn)”
Introduction to Natural Language Processing (NLP) ~ NLTK package
Text Processing: Tokenization, Stemming, Lemmatization, Stop word removal
Text Feature Engineering: Syntactical Parsing, Entity Parsing, Statistical Features
NLP Applications
Text Mining with Python ( web scraping)
Text Analysis
Sentiment Analysis with the Twitter case study
Advanced Topics :
Deep Learning ( Tensorflow ) Overview
Computer Vision Overview
Chatbot overview
Important Scientific Research Papers and IBM Certification 
NumPy(Mathematical computing with python)
“Arrays and Matrices, NDarray object, Array indexing, Datatypes, Array math
Broadcasting, Std Deviation, Conditional Prob, Covariance, and Correlation.”SciPy(Scientific computing with python)
Builds on top of NumPy, SciPy and its characteristics, sub packages.
Cluster, ﬀtpack, linalg, signal, integrate, optimize, stats; Bayes Theorem using SciPy.Data Visualization (Matplotlib)
Plotting Graphs and Charts (Line, Pie, Bar, Scatter, Histogram, 3D).
Subplots.
The Matplotlib API.
Data Analysis and Data Manipulation with Python (Pandas)
Data frames, NumPy array to a data frame.
Import Data (csv, json, excel, sql database).
“Data operations: View, Select, Filter, Sort, Group, Cleaning, Join/Combine,
Handling Missing Values.”
Machine Learning
“Introduction to Machine Learning(ML): Definition, Concepts, and Terminology, Lifecycle”
Problem categories of ML : Classification ,Clustering,Regression,Optimization
Learning SubFields: Supervised, Unsupervised, SemiSupervised, Reinforcement, Deep learning
“Basic Performance measures: MSE, MAE, NMSE, ROC / AUC, Confusion Matrix
Accuracy, Precision / Recall etc.”
Installation of Python Packages for ML and Setting up Environment ( Anaconda Distribution)
Supervised Algorithms: Linear, Logistic, CART, Naïve Bayes, KNN, Decision Tree, Random Forest, SVM and with a case study(for all)
Unsupervised Algorithms: KMeans, PCA and with a case study(for all)
Recommender Systems
Dimensionality Reduction
“Natural Language Processing, Machine
Learning (ScikitLearn)”
Introduction to Natural Language Processing (NLP) ~ NLTK package
Text Processing: Tokenization, Stemming, Lemmatization, Stop word removal
Text Feature Engineering: Syntactical Parsing, Entity Parsing, Statistical Features
NLP Applications
Text Mining with Python ( web scraping)
Text Analysis
Sentiment Analysis with a Twitter case study
Advanced Topics :
Deep Learning ( Tensorflow ) Overview
Computer Vision Overview
Chatbot overview
Important Scientific Research Papers and IBM Certification.
 Well versed of PYTHON programming