Introduction: of Data Science & Machine Learning Course

If you are starting out in programming the best thing would be begin with Python, As per all the recent employment forecasts it is predicted Data Sciences and Machine Learning will create most lucrative career options in coming years so it will be wise to give a head-start to your career with disciplined learning in Data Sciences along with Machine learning for a bright and diversifying future.

Objective: of Data Science & Machine Learning Classes

Course covers the necessary tools and concepts used in the data science field which includes machine learning, statistical inference, working with data at scale and etc.

Student will begin with entire process for data science projects and the different roles and skills that are needed, Obtaining data through a variety of sources, including web APIs and page scraping. Using tools like Python, Pandas, Numpy, Seaborns, matplotlib, and numerous algorithm to explore and manipulate data.

Course Schedule: of Data science Course

Duration: 5- 6 months

Schedule:

2 Hours 3 Days a week (Weekdays)

2 Hours 2 Days a week (Weekends)

Course Structure:

Part-1 -Core Python

Module 1: Getting started with Python programming Overview

Introductory Remarks about Python

A Brief History of Python

How python is differ from other languages

Python Versions

Installing Python and Environment Setup

IDLE

Getting Help

How to execute Python program

Writing your first Python program

How to work on different Popular IDE’s [Pycharm, Jupyter Notebook, Spyder etc.] Module 2: Variables, Keywords and Operators Variables

Memory mapping of variables

Keywords in Python

Comments in python

Operators Arithmetic Operators Assignment Operators Comparison Operators Logical Operators Membership Operators Identity Operators Bitwise Operators

Basics I/O and Type casting

Getting user input Module 3: Data types in Python Numbers

Strings

Lists

Tuples

Dictionary

Sets Module 4: Numbers and Strings Introduction to Python ‘Number’ & ‘string’ data types

Properties of a string

String built-in functions

Programming with strings

String formatting Module 5: Lists and Tuples Introduction to Python ‘list’ data type

Properties of a list

List built-in functions

Programming with lists

List comprehension

Introduction to Python ‘tuple’ data type

Tuples as Read only lists Module 6: Dictionary and Sets Introduction to Python ‘dictionary’ data type

Creating a dictionary

Dictionary built-in functions

Introduction to Python ‘set’ data type

Set and set properties

Set built-in functions Module 7: Decision making & Loops Introduction of Decision Making

Control Flow and Syntax

The if Statement

The if…else Statement

The if…elif…else Statement

Nested if…else Statement

The while Loop

break and continue Statement

The for Loop

Pass statement

Exercise

Module 8: User defined Functions Introduction of functions

Function definition and return

Function call and reuse

Function parameters

Function recipe and docstring

Scope of variables

Recursive functions

Lambda Functions / Anonymous Functions

Map , Filter & Reduce functions Module 9: Module s and Packages Module s

Importing Module

Standard Module – sys

Standard Module – OS

The dir Function

Packages

Exercise Module 10: Regular expression Pattern matching

Meta characters for making patterns

re flags

Use of match() , sub() , findall(), search(), split() methods Part-2 – Data Analysis Module 1: GETTING STARTED WITH PYTHON LIBRARIES What is data analysis?

Why python for data analysis?

Essential Python Libraries Installation and setup

Ipython

Jupyter Notebook Module 2: NUMPY ARRAYS Introduction to Numpy

Numpy Arrays

Numpy Data types

Numpy Array Indexing

Numpy Mathematical Operations

Indexing and slicing

Manipulating array shapes

Stacking arrays

Sorting arrays

Creating array views and copies

I/O with NumPy

Numpy Exercises Module 3: WORKING WITH PANDAS Introduction to Pandas

Data structure of pandas

Pandas Series

Pandas dataframes

Data aggregation with Pandas

DataFrames Concatenating and appending

DataFrames Joining

DataFrames Handling missing data

Data Indexing and Selection

Operating on data in pandas

loc and iloc

map,apply,apply_map

group_by

string methods

Querying data in pandas

Dealing with dates

Reading and Writing to CSV files with pandas

Reading and Writing to Excel with pandas

Reading and Writing to SQL with pandas

Reading and Writing to HTML files with pandas

Pandas Exercises Part-3 – Data Visualization Module 1: Matplotlib Introduction of Matplotlib

Basic matplotlib plots

Line Plots

Bar Plots

Pie Plots

Scatter plots

Histogram Plots

Saving plots to file

Plotting functions in matplotlib

Matplotlib Exercises Module 2: Seaborn Introduction of Seaborn

Distribution Plots

Categorical Plots

Matrix Plots

Bar Plots

Box Plots

Strip Plots

Violin Plots

Clustermap Plots

Heatmaps Plots

KDE Plots

Regression Plots

Style and Color

Seaborn Exercise Module 3: Plotly and Cufflinks Introduction to Plotly and Cufflinks

Plotly and Cufflinks Module 4: Geographical Plotting Introduction to Geographical Plotting

Choropleth Maps – Part 1

Choropleth Maps – Part 2

Choropleth Exercises

Projects using Analysis and Visualisation Part-4 – Machine Learning Module 1: Introduction to Machine Learning Course in Delhi What is Machine learing?

Overview about scikit-learn package

Types of ML

Basic steps of ML

ML algorithms

Machine learning examples Module 2: Data Preprocessing Dealing with missing data

Identifying missing values

Imputing missing values

Drop samples with missing values

Handling with categorical data

Nominal and Ordinal features

Encoding class labels

One hot encoding

Split data into training and testing sets

Feature scaling Module 3: Machine Learning Classifiers K-Nearest Neighbors (KNN)

Decision tree

Random forest

Support vector machines (SVM)

Naive Bayes

Logistic Regression

Email Spam Filtering Project Module 4: Regression Based Learning Simple Regression

Multiple Regression

Predicting house prices with Regression Module 5 Clustering Based Learning Definition

Types of clustering

The k-means clustering algorithm Module 6 Natural Language Processing Install nltk

Tokenize words

Tokenizing sentences

Stop words with NLTK

Stemming words with NLTK

Twitter Sentiment analysis Project Module 7 Working with OpenCV Installing opencv

Reading and writing images

Applying image filters

Writing text on images

Image Manipulations

Face detection Project

Speech Recognition Project

Training Partner

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