python course in Bangalore,top python and iot training in Bangalore,best python and django course in Bangalore,best machine learning training institute in bangalore, artificial intelligence course bangalore, python institute,django training,python with web technologies training institute in Bangalore, machine learning training and placement in Bangalore,python training and placement institutes in Bangalore,python with cloud training in Bangalore,iot training,rtos course,best IOT training in Bangalore,django training course in Bangalore,data analytics training institutes in Bangalore, best machine learning training Bangalore,top python institutes in Bangalore,Python classes in bangalore, machine learning training, fullstack development course in bangalore, machine learning course bangalore, best Data Science training institute in bangalore, best AI training in bangalore, artificial intelligence course, python institute in bangalore, Machine Learning training institute in bangalore, Data Science course in bangalore, iot course in bangalore, testing automation training in bangalore, best python training institute in bangalore, top python training course in bangalore,selenium training in bangalore

Python Data Science/AI Course:

Objective:
>Understand Python Programming environment and coding.
>Working on Machine Learning, Data Analytics,SQL, Java,etc.
>Deep Understanding of Python Applications.
Who can attend?
>Freshers seeking career in Python Programming.
>Working/experienced who want to explore Python Data Science/Data Analytics.

Python Data Science Syllabus:
MODULE 1: PYTHON BASICS
Introduction to Python
Variables, keywords and Data types
Operators 
Control Statements
Lists
Tuple
Sets
Dictionary
Functions
Strings
Arrays
 

MODULE 2: PYTHON ADVANCED
Modules and Packages
Exception Handling
File Handling
Object Oriented Programming
Multi-Threading
Regular Expressions
GUI Programming-tkinter

MODULE 3:SQL
Introduction to Database
SQL Architecture
SQL Commands
Storage Classes
Data Types
Create Table
Operators
SQL Clauses
Adding Constraints
Joining Tables
Table Relations
Sub Queries

MODULE 4: DATA ANALYTICS
CH1: Learning NumPy
NumPy basics
Shape manipulation
Copies and Views
Broadcasting rules
Indexing with array of indices
Indexing with boolean array
Indexing with strings
Linear algebra operations
Numpy benefits with matplotlib
CH2: Pandas
Series and Dataframes
Creating dataframes from csv
Plotting csv data
Adding/deleting coulmns with index
Stack/Unstack/Transpose functions
Filtering & Sorting
Grouping
Ways to calculate outliers
Exporting data to txt/csv/excel
Visualization with matplotlib
CH3: Matplotlib & seaborn
Basics of graph plotting
Line plot
Scatter plot
Bar graph
Histogram
Contour plot
Pie chart
Grids
Text plot
Multi plot

MODULE 5: MACHINE LEARNING ALGORITHMS 
CH1: Supervised Learning
Linear Regression
Polynomial Regression
Logistic Regression
KNN (k-Nearest Neighbour)
Decision tree
Naive Bayes

CH2: Unsupervised Learning
K-means clustering
Agglomerative Clustering Algorithm