Data science & machine Learning Using Python

[Pages:8]Data Science & Machine Learning Using Python

Python

Introduction To Python

l Why Python l Application areas of python l Python implementations

l Cpython l Jython l Ironpython l Pypy l Python versions l Installing python l Python interpreter architecture l Python byte code compiler l Python virtual machine(pvm)

Writing and Executing First Python Program

l Using interactive mode l Using script mode

l General text editor and command window l Idle editor and idle shell l Understanding print() function l How to compile python program explicitly

Python Language Fundamentals

l Character set l Keywords l Comments l Variables l Literals l Operators l Reading input from console l Parsing string to int, float Python Conditional Statements

l If statement l If else statement l If elif statement l If elif else statement l Nested if statement Looping Statements

l While loop l For loop l Nested loops l Pass, break and continue keywords Standard Data Types

l Int, float, complex, bool, nonetype l Str, list, tuple, range l Dict, set, frozenset

l

Duration: 4.5 Months

String Handling

l What is string l String representations l Unicode string l String functions, methods l String indexing and slicing l String formatting

Python List

l Creating and accessing lists l Indexing and slicing lists l List methods l Nested lists l List comprehension

Python Tuple

l Creating tuple l Accessing tuple l Immutability of tuple

Python Set

l How to create a set l Iteration over sets l Python set methods l Python frozenset

Python Dictionary

l Creating a dictionary l Dictionary methods l Accessing values from dictionary l Updating dictionary l Iterating dictionary l Dictionary comprehension

Python Functions

l Defining a function l Calling a function l Types of functions l Function arguments

l Positional arguments, keyword arguments l Default arguments, non-default arguments l Arbitrary arguments, keyword arbitrary arguments l Function return statement l Nested function l Function as argument l Function as return statement l Decorator function l Closure l Map(), filter(), reduce(), any() functions l Anonymous or lambda function Modules & Packages

l Why modules l Script v/s module l Importing module l Standard v/s third party modules l Why packages l Understanding pip utility

File I/O

l Introduction to file handling l File modes l Functions and methods related to file handling l Understanding with block

Object Oriented Programming

l Procedural v/s object oriented programming l OOP principles l Defining a class & object creation l Object attributes l Inheritance l Encapsulation l Polymorphism

Exception Handling

l Difference between syntax errors and exceptions l Keywords used in exception handling

l try, except, finally, raise, assert l Types of except blocks

Regular Expressions(Regex)

l Need of regular expressions l Re module l Functions /methods related to regex l Meta characters & special sequences

GUI Programming

l Introduction to tkinter programming l Tkinter widgets

l Tk, label, Entry, Textbox, Button l Frame, messagebox, filedialog etc l Layout managers l Event handling l Displaying image

Multi-Threading Programming

l Multi-processing v/s Multi-threading l Need of threads l Creating child threads l Functions /methods related to threads l Thread synchronization and locking

SQL

Introduction to Database

l Database Concepts l What is Database Package? l Understanding Data Storage l Relational Database (RDBMS) Concept

SQL (Structured Query Language)

l SQL basics l DML, DDL & DQL l DDL: create, alter, drop l SQL constraints:

l Not null, unique, l Primary & foreign key, composite key l Check, default l DML: insert, update, delete and merge l DQL : select l Select distinct l SQL where l SQL operators l SQL like l SQL order by l SQL aliases l SQL views l SQL joins l Inner join l Left (outer) join l Right (outer) join l Full (outer) join

l Mysql functions l String functions l Char_length l Concat l Lower l Reverse l Upper l Numeric functions l Max, min, sum l Avg, count, abs l Date functions l Curdate l Curtime l Now

Statistics, Probability & Analytics:

Introduction to Statistics l Sample or population l Measures of central tendency

l Arithmetic mean l Harmonic mean l Geometric mean l Mode l Quartile

l First quartile l Second quartile(median) l Third quartile l Standard deviation

Probability Distributions l Introduction to probability l Conditional probability l Normal distribution l Uniform distribution l Exponential distribution l Right & left skewed distribution l Random distribution l Cenltral limit theorem

l

Hypothesis Testing l

l Normality test l Mean test

l T-test l Z-test l ANOVA test l Chi square test l Correlation and covariance

l

Numpy Package l

l Difference between list and numpy array l Vector and matrix operations l Array indexing and slicing

l

Pandas Package l l

Introduction to pandas

l

l Labeled and structured data l Series and dataframe objects

l

How to load datasets l From excel l From csv l From html table Accessing data from Data Frame l at & iat l loc & iloc l head() & tail()

Exploratory Data Analysis (EDA) l describe() l groupby() l crosstab() l boolean slicing / query() Data Manipulation & Cleaning l Map(), apply() l Combining data frames l Adding/removing rows & columns l Sorting data l Handling missing values l Handling duplicacy l Handling data error Handling Date and Time

Data Visualization using matplotlib and seaborn packages l Scatter plot, lineplot, bar plot l Histogram, pie chart, l Jointplot, pairplot, heatmap l Outlier detection using boxplot

Machine Learning:

Introduction To Machine Learning l Traditional v/s Machine Learning Programming l Real life examples based on ML l Steps of ML Programming l Data Preprocessing revised l Terminology related to ML

Supervised Learning l Classification l Regression Unsupervised Learning l clustering

KNN Classification l Math behind KNN l KNN implementation l Understanding hyper parameters Performance metrics l Math behind KNN l KNN implementation l Understanding hyper parameters

Regression

l Math behind regression l Simple linear regression l Multiple linear regression l Polynomial regression l Boston price prediction l Cost or loss functions

l Mean absolute error l Mean squared error l Root mean squared error l Least square error l Regularization

Logistic Regression for classification

l Theory of logistic regression l Binary and multiclass classification l Implementing titanic dataset l Implementing iris dataset l Sigmoid and softmax functions

Support Vector Machines

l Theory of SVM l SVM Implementation l kernel, gamma, alpha Decision Tree Classification

l Theory of decision tree l Node splitting l Implementation with iris dataset l Visualizing tree Ensemble Learning

l Random forest l Bagging and boosting l Voting classifier

Model Selection Techniques

l Cross validation l Grid and random search for hyper parameter tuning

Recommendation System

l Content based technique l Collaborative filtering technique l Evaluating similarity based on correlation l Classification-based recommendations

Clustering

l K-means clustering l Hierarchical clustering l Elbow technique l Silhouette coefficient l Dendogram Text Analysis

l Install nltk l Tokenize words l Tokenizing sentences l Stop words customization l Stemming and lemmatization l Feature extraction l Sentiment analysis l Count vectorizer l Tfidfvectorizer l Naive bayes algorithms

Dimensionality Reduction l Principal component analysis(pca)

Open CV l Reading images l Understanding gray scale image l Resizing image l Understanding haar classifiers l Face, eyes classification l How to use webcam in open cv l Building image data set l Capturing video l Face classification in video l Creating model for gender prediction

Tableau

Tableau - Home l Tableau - overview l Tableau - environment setup l Tableau - get started l Tableau - navigation l Tableau - design flow l Tableau - file types l Tableau - data types l Tableau - show me l Tableau - data terminology

Tableau - Data Sources l Tableau - custom data view l Tableau - data sources l Tableau - extracting data l Tableau - fields operations l Tableau - editing metadata l Tableau - data joining l Tableau - data blending

Tableau ? Work Sheet l Tableau - add worksheets l Tableau - rename worksheet l Tableau - save & delete worksheet l Tableau - reorder worksheet l Tableau - paged workbook

Tableau ? Calculation l Tableau - operators l Tableau - functions l Tableau - numeric calculations l Tableau - string calculations l Tableau - date calculations l Tableau - table calculations l Tableau - lod expressions

Tableau ? Sorting & Filter l Tableau - basic sorting l Tableau - basic filters l Tableau - quick filters l Tableau - context filters l Tableau - condition filters l Tableau - top filters l Tableau - filter operations

Tableau - Charts

l Tableau - bar chart l Tableau - line chart l Tableau - pie chart l Tableau - crosstab l Tableau - scatter plot l Tableau - bubble chart l Tableau - bullet graph l Tableau - box plot l Tableau - tree map l Tableau - bump chart l Tableau - gantt chart l Tableau - histogram l Tableau - motion charts l Tableau - waterfall charts l Tableau - dashboard

Projects

l One project using python & sql l One project using python & ml l One dashboard using tableau

Partners :

Java

ducateducation

NOIDA

GHAZIABAD

PITAMPURA (DELHI)

A-43 & A-52, Sector-16, Noida - 201301, (U.P.) INDIA

70-70-90-50-90 +91 99-9999-3213

1, Anand Industrial Estate,

Plot No. 366, 2nd Floor,

Near ITS College, Mohan Nagar, Kohat Enclave, Pitampura,

Ghaziabad (U.P.)

( Near- Kohat Metro Station)

70-70-90-50-90

Above Allahabad Bank, New Delhi- 110034.

70-70-90-50-90

GURGAON

1808/2, 2nd floor old DLF, Near Honda Showroom, Sec.-14, Gurgaon (Haryana)

70-70-90-50-90

l

SOUTH EXTENSION (DELHI)

D-27, South Extension-1 New Delhi-110049

70-70-90-50-90 +91 98-1161-2707

 ................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download