Learning Predictive Analytics with Python

Learning Predictive Analytics with Python, available at $27.99, has an average rating of 4.5, with 31 lectures, based on 3 reviews, and has 14 subscribers.

You will learn about Predictive Analytics Theory and Practical Application Python with Object Oriented Programming Decision Tree and Random Forest Algorithm Introduction to libraries like sklearn, pandas and numpy This course is ideal for individuals who are Beginner Python developers curios about Machine Learning and Data Analytics or Those who want to have good basic understanding of how ML is done with Python. It is particularly useful for Beginner Python developers curios about Machine Learning and Data Analytics or Those who want to have good basic understanding of how ML is done with Python.

Enroll now: Learning Predictive Analytics with Python

Summary

Title: Learning Predictive Analytics with Python

Price: $27.99

Average Rating: 4.5

Number of Lectures: 31

Number of Published Lectures: 31

Number of Curriculum Items: 31

Number of Published Curriculum Objects: 31

Original Price: £29.99

Quality Status: approved

Status: Live

What You Will Learn

Predictive Analytics Theory and Practical Application
Python with Object Oriented Programming
Decision Tree and Random Forest Algorithm
Introduction to libraries like sklearn, pandas and numpy

Who Should Attend

Beginner Python developers curios about Machine Learning and Data Analytics
Those who want to have good basic understanding of how ML is done with Python.

Target Audiences

Beginner Python developers curios about Machine Learning and Data Analytics
Those who want to have good basic understanding of how ML is done with Python.

Hi all,

Predictive analytics is a branch of advanced analytics that uses statistical techniques, machine learning algorithms, and data mining to analyze historical data and make predictions about future events or trends. It helps organizations to identify patterns, understand trends, and anticipate outcomes, which can lead to better decision-making and more efficient operations. Predictive analytics is used in a variety of industries, including finance, healthcare, marketing, and manufacturing. By using predictive analytics, organizations can gain insights into customer behaviour, optimize marketing campaigns, detect fraud, and improve supply chain management, among other benefits.

In this course, we will be covering all the important basics of Predictive analytics and then we will start with all the important concepts about Python and Object Oriented Programming.

Python is a general-purpose language used in major areas like data science, machine learning and web development.

This course covers almost all the essential concepts of Python. As a beginner, it is essential to have a basic clear and thorough understanding of the concepts. This course focuses on these points and we have tried our best to deliver it in a way which will help you to get the basics right as well as concepts clear.

The key concepts covered in this course are:

1. Built-in data types like list, dictionary, tuple, set, string with their functionalities.

2. List comprehension, lambda function and decorators in Python

3. Object-oriented programming in Python

4. Creating an application using all the learned concepts

5. Predictive Analytics

6. A predictive model using Random Forest Algorithm to predict a winner between two given pokemon.

Does this course cover all the concepts of Data Science and Web development?

I will be honest with you, the course does not cover all the concepts of Web Development (although we have different courses for that we will discuss it some other day), however, it does cover about basics of Data Science and Machine Learning.

Course Curriculum

Chapter 1: Theoretical Introduction of Predictive Analytics.

Lecture 1: Introduction

Lecture 2: Predictive Analytics in brief

Lecture 3: Key terms of Predictive Analytics

Lecture 4: Decision Tree Algorithm With Example

Lecture 5: Random Forest Algorithm

Chapter 2: Python Tutorial

Lecture 1: Introduction

Lecture 2: Installing Python and Visual Studio Code.

Lecture 3: Variables and Operators

Lecture 4: Bitwise, Logical and Comparison Operators

Lecture 5: Data Types

Lecture 6: Functions, Lambda Functions, Arbitrary and Keyword Arguments

Lecture 7: List

Lecture 8: Tuple

Lecture 9: Set

Lecture 10: Dictionary

Lecture 11: String Operations

Lecture 12: if else loop

Lecture 13: while loop

Lecture 14: for loop and list comprehension

Lecture 15: Object Oriented Programming (OOP) Introduction

Lecture 16: OOP at glance

Lecture 17: Class and Object

Lecture 18: Inheritance, Abstraction, Encapsulation and Polymorphism

Lecture 19: Decorators

Lecture 20: Creating a command line app

Lecture 21: Creating a Book class

Lecture 22: File Hanling

Lecture 23: Create Read Update Delete (CRUD) operations Book Class

Lecture 24: Create Author, Reader and Person Class

Lecture 25: Writing the main script to run our app

Chapter 3: Creating a Predictive Analytics App using Python

Lecture 1: App to predict winner between two given pokemons

Instructors

adirani Digital Solutions
Digital skills development startup
Aditya S Kadam
Entrepreneur

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4 stars: 2 votes
5 stars: 1 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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