Python Developer Masters Program

Read Review
5.0 (5250 satisfied learners)

Pursue Python Developer Masters Program by enrolling in EDTIA'S Python Developer Masters Program Training and upskill your knowledge and technological skill in the field.

Course Description

Python Developer Masters program will help you become a Python developer and open a career possibility in different fields such as Machine Learning, Data Science, Big Data, and Web Development. Python is a premier, adaptable, and influential open-source language that is easy to learn and use and has powerful data manipulation and analysis libraries.

A Python Developer is accountable for the coding, designing, deploying, and debugging projects, generally on the server-side. Python is used in web development, machine learning, AI, scientific computing, and academic research.

Programmers Developers Technical Leads and Data Scientists fresh graduates.

Python is usually used for developing websites and software, task automation, data analysis, and data visualization.

One can do this course after completing their graduation. The candidate should have basic computer knowledge.

What you'll learn

  • In this course, you will learn: Python Programming PySpark Django NLP Machine Learning Techniques and Artificial Intelligence Tokenization Lemmatization Supervised Algorithms, and more.


  • Basic knowledge of Python Basic Knowledge of Computer Operating Graduate in any field


Understand the concepts of the Python Language.

Need for Programming
Advantages of Programming
Overview of Python
Organizations using Python
Python Applications in Various Domains
Python Installation
Operands and Expressions
Conditional Statements
Command Line Arguments
Numbers in Python
Demonstrating Conditional Statements
Demonstrating Loops

Learn various sequence structures their usage, and execute sequence operations.

Way of Receiving User Input and eval Function
Python - Files Input/Output Functions
Lists and Related Operations
Tuples and Related Operations
Strings and Related Operations
Sets and Related Operations
Dictionaries and Related Operations
File Handling
Tuple - Properties, Related Operations
List - Properties, Related Operations
Dictionary - Properties, Related Operations
Set - Properties, Related Operations
String – Properties, Related Operations

understand diverse types of Functions and various Object-Oriented concepts such as Abstraction, Inheritance, Polymorphism, Overloading, Constructor, and so on.

User-Defined Functions
Concept of Return Statement
Concept of __name__=” __main__”
Function Parameters
Different Types of Arguments
Global Variables
Global Keyword
Variable Scope and Returning Values
Lambda Functions
Various Built-In Functions
Introduction to Object-Oriented Concepts
Built-In Class Attributes
Public, Protected and Private Attributes, and Methods
Class Variable and Instance Variable
Constructor and Destructor
Decorator in Python
Core Object-Oriented Principles
Inheritance and Its Types
Method Resolution Order
Getter and Setter Methods
Inheritance-In-Class Case Study
Operations - Syntax, Arguments, Keyword Arguments, and Return Values
Lambda - Features, Syntax, Options
Built-In Functions
Python Object-Oriented Concepts Applications
Python Object-Oriented Core Principles and Its Applications
Inheritance Case Study

Discover how to create generic python scripts, address errors/exceptions in code, and extract/filter content using regex.

Standard Libraries
Packages and Import Statements
Reload Function
Important Modules in Python
Sys Module
Os Module
Math Module
Date-Time Module
Random Module
JSON Module
Regular Expression
Exception Handling
Packages and Modules
Regular Expressions
Errors and Exceptions

Learn the basics of Data Analysis using two essential libraries: NumPy and Pandas.

Basics of Data Analysis
NumPy - Arrays
Operations on Arrays
Indexing Slicing and Iterating
NumPy Array Attributes
Matrix Product
NumPy Functions
Array Manipulation
File Handling Using NumPy
Matrix Product and Aggregate Functions using Numpy
Array Creation and Logic Functions
File Handling Using Numpy

know about analyzing datasets and data manipulation using Pandas.

Introduction to pandas
Data structures in pandas
Data Frames
Importing and Exporting Files in Python
Basic Functionalities of a Data Object
Merging of Data Objects
Concatenation of Data Objects
Types of Joins on Data Objects
Data Cleaning using pandas
Exploring Datasets
Functionality of Series
The Functionality of Data Frame
Combining Data from Dataset
Cleaning Data

learn about Data Visualization using Matplotlib.

Why Data Visualization?
Matplotlib Library
Line Plots
Multiline Plots
Bar Plot
Pie Chart
Scatter Plot
Saving Charts
Customizing Visualizations
Saving Plots
Plotting Different Types of Charts
Customizing Visualizations Using Matplotlib
Customizing Visualizations and Subplots

learn GUI programming using the ipywidgets package.

Ipywidgets Package
Numeric Widgets
Boolean Widgets
Selection Widgets
String Widgets
Date Picker
Color Picker
Container Widgets
Creating a GUI Application
Creating GUI Elements
Creating an application containing GUI elements

learn to design Python Applications.

Folium Library
Pandas Library
Flow Chart of Web Map Application
Developing Web Map Using Folium and Pandas
Reading Data from Titanic Dataset and present through Plots

design Python Applications.

Soup Library
Requests Library
ScrapLoading various kinds of the dataset in Python
All Hyperlinks from a Webpage Utilizing Beautiful Soup and Requests
Plotting Charts Using Bokeh
Plotting Scatterplots Using Bokeh
Image Editing Using OpenCV
Face Detection Using OpenCV
Motion Detection and Capturing Video

you will be introduced to Data Science how it helps analyze large and unstructured data with different tools.

What is Data Science?
What does Data Science involve?
The era of Data Science
Business Intelligence vs. Data Science
The life cycle of Data Science
Tools of Data Science
Introduction to Python

Learn sources available to extract data, arrange the data in a structured form, analyze the data, and represent the data in a graphical format.

Data Analysis Pipeline
What is Data Extraction
Types of Data
Raw and Processed Data
Data Wrangling
Exploratory Data Analysis
Visualization of Data
Loading diverse datasets in Python
Arranging the data
Plotting the graphs

understands the concept of Machine Learning with Python and its types.

What is Machine Learning?
Machine Learning Use-Cases
Machine Learning Process Flow
Machine Learning Categories
Linear regression
Gradient descent

learn Supervised Learning Techniques and their implementation.

What are Classification and its use cases?
What is a Decision Tree?
Algorithm for Decision Tree Induction
Creating a Perfect Decision Tree
Confusion Matrix
What is Random Forest?

learn about the effect of dimensions within data.

Introduction to Dimensionality
Why Dimensionality Reduction
Factor Analysis
Scaling dimensional Model

learn Supervised Learning Techniques and their implementation.

Naïve Bayes?
How Naïve Bayes works?
Implementing Naïve Bayes Classifier
Support Vector Machine
Illustrate how Support Vector Machine works?
Hyperparameter optimization
Grid Search vs. Random Search
Performance of Support Vector Machine for Classification

knows about Unsupervised Learning, and the various types of clustering used to analyze the data.

Clustering & its Use Cases?
K-means Clustering
How does the K-means algorithm works?
How to do optimal clustering
What is C-means Clustering?
What is Hierarchical Clustering?
How does Hierarchical Clustering work?

learn Association rules and their extension.

What are Association Rules?
Association Rule Parameters
Calculating Association Rule Parameters
Recommendation Engines
How do Recommendation Engines work?
Collaborative Filtering
Content-Based Filtering

learn about developing an intelligent learning algorithm.

What is Reinforcement Learning
Why Reinforcement Learning
Elements of Reinforcement Learning
Exploration vs. Exploitation dilemma
Epsilon Greedy Algorithm
Markov Decision Process (MDP)
Q values and V values
Q – Learning
α values
Calculating Reward
Discounted Reward
Calculating Optimal quantities
Implementing Q Learning
Setting up an Optimal Action

know about Time Series Analysis to forecast dependent variables based on time.

What is Time Series Analysis?
Importance of TSA
Components of TSA
White Noise
AR model
MA model
ARMA model
ARIMA model
Checking Stationarity
Converting non-stationary data to stationary
Implementing Dickey-Fuller Test
Plot ACF and PACF
Generating the ARIMA plot
TSA Forecasting

understand selecting one Model over another, Boosting and its importance in Machine Learning.

What is Model Selection?
Need of Model Selection
What is Boosting?
How do Boosting Algorithms work?
Types of Boosting Algorithms
Adaptive Boosting

implement a Project end to end, and the Expert will share his insights from the Industry to help you with your career in this profession. After that, we will have a Q&A and doubt clearing session.

How to approach a project
Hands-On project implementation
Industry insights for the Machine Learning domain
QA and Doubt Clearing Session

Learn text mining and the ways of extracting and reading data from some common file types, including NLTK corpora

Overview of Text Mining
Need of Text Mining
Natural Language Processing (NLP) in Text Mining
Applications of Text Mining
OS Module
Reading, Writing to text and word files
Setting the NLTK Environment
Accessing the NLTK Corpora

Learn some ways of text extraction and cleaning using NLTK.

Frequency Distribution
Different Types of Tokenizers
Bigrams, Trigrams & Ngrams
POS Tagging
Named Entity Recognition
Tokenization: Regex, Word, Blank line, Sentence Tokenizers
Bigrams, Trigrams & Ngrams
Stopword Removal
POS Tagging

comprehend to study a sentence structure using a word group to construct phrases and sentences using NLP and English grammar rules.

Syntax Trees
Context-Free Grammars (CFG)
Automating Text Paraphrasing
Parsing Syntax Trees
Automate Text Paraphrasing using CFG's

review text classification, vectorization techniques, and processing using scikit-learn.

Machine Learning: Brush Up
Bag of Words
Count Vectorizer
Term Frequency (TF)
Inverse Document Frequency (IDF)
Demonstrate Bag of Words Approach
Working with CountVectorizer
Using TF & IDF

learn to create a Machine Learning classifier for text classification

Converting Text to features and labels
Multinomial Naive Bayes Classifier
Leveraging Confusion Matrix
Converting Text to features and labels
Demonstrate text classification using Multinomial NB Classifier
Leveraging Confusion Matrix

understand Sentiment Classification on Movie Rating Dataset

Sentiment Analysis

Learn about Big Data, Hadoop, and Spark.

What is Big Data?
Big Data Customer Scenarios
Constraints and Resolutions of Existing Data Analytics Architecture with Uber Use Case
How Hadoop Solves the Big Data Problem?
What is Hadoop?
Hadoop's Key Characteristics
Hadoop Ecosystem and HDFS
Hadoop Core Components
Rack Awareness and Block Replication
YARN and its Advantage
Hadoop Cluster and its Architecture
Hadoop: Different Cluster Modes
Big Data Analytics with Batch & Real-Time Processing
Why is Spark Needed?
What is Spark?
How Spark Differs from its Competitors?
Spark at eBay
Spark's Place in Hadoop Ecosystem

Know Python programming basics and learn different types of sequence structures, related operations, and their usage.

Overview of Python
Different Applications where Python is Used
Values, Types, Variables
Operands and Expressions
Conditional Statements
Command Line Arguments
Writing to the Screen
Python files I/O Functions
Strings and related operations
Tuples and related operations
Lists and related operations
Dictionaries and related operations
Sets and related operations
Creating "Hello World" code
Demonstrating Conditional Statements
Demonstrating Loops
Tuple - properties, associated functions, compared with the list
List - properties, related operations
Dictionary - properties, related operations
Set - properties, related operations

Discover How to create generic python scripts, how to address errors/exceptions in code, and how to extract/filter content using regex.

Function Parameters