...

Python Developer Masters Program

Categories
Programming Languages and Methodologies
Read Review
5.0 (5250 satisfied learners)
Python Developer Masters Program

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

Course Description for Python Developer Masters Program

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 Python Developer Masters Program

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

Requirements of Python Developer Masters Program

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

Curriculam of Python Developer Masters Program

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
Variables
Operands and Expressions
Conditional Statements
Loops
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
Overloading
Overriding
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
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
Series
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
Histogram
Pie Chart
Scatter Plot
Boxplot
Saving Charts
Customizing Visualizations
Saving Plots
Grids
Subplots
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
PCA
Factor Analysis
Scaling dimensional Model
LDA

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?