Diploma in Python with Data Science and Machine Learning

Diploma in Python with Data Science and Machine Learning, available at $59.99, has an average rating of 4.15, with 120 lectures, 2 quizzes, based on 486 reviews, and has 46513 subscribers.

You will learn about Advanced Python with Data science & Machine Learning Python Data Types Lists, dictionary Java programming in deep Data science with Java Machine Learning with python Multithreading Current Projects This course is ideal for individuals who are Interested students It is particularly useful for Interested students.

Enroll now: Diploma in Python with Data Science and Machine Learning

Summary

Title: Diploma in Python with Data Science and Machine Learning

Price: $59.99

Average Rating: 4.15

Number of Lectures: 120

Number of Quizzes: 2

Number of Published Lectures: 120

Number of Published Quizzes: 2

Number of Curriculum Items: 122

Number of Published Curriculum Objects: 122

Number of Practice Tests: 2

Number of Published Practice Tests: 2

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

Advanced Python with Data science & Machine Learning

Python Data Types Lists, dictionary

Java programming in deep

Data science with Java

Machine Learning with python

Multithreading

Current Projects

Who Should Attend

Interested students

Target Audiences

Interested students

Are you having an interest in learning a python programming language and searching for a better course for your brighter career? Then explore us and get your career solution right now.

Diploma in python programming course leads the students from the basics of writing and running Python scripts to more advanced features such as file operations, regular expressions, working with binary data. The use of this programming language is to store different types of data into a variable format. Python is one of the absolute, flexible and powerful open source language which can be used in scientific computing, finance, oil and gas, physics and signal processing. This programming language has recently begun to gain an ever-increasing market share. The cross-platform nature of Python programming enables a better use for different tasks on any operating system. This provides an opportunity towards the Python developers to get utilised for different IT related roles. One of the most amazing features of Python is that it is actually one person’s work. Generally, new programming languages are developed and published by large companies employing lots of professionals experts, and due to copyright rules, it is very hard to name any of the persons involved in the project. Python is an exception to all of this as it is very convenient to use by anyone having a knowledge of programming language.

TOPICS COVERED IN THE COURSE WILL BE

1) Introduction to course

1.1 Python Introduction

1.2 History of Python

1.3 Scope of Python

1.4 Applications of Python

1.5 Why Python is everywhere

1.6 How Python is different

1.7 Running Python

2.1 Variables in Python

2.2 Data Types

2.3 Lists in Python

2.4 Demo of List function

2.5 Tuples

2.6 Python Dictionary

3.1 Python Operators

3.2 If Else statement

3.3 Indentation

3.4 Assignment 1

4.1 Iterative Statements

4.2 While Statement

4.3 For Statement

4.4 Touple

4.5 Assignment 2

5.1 Demo 1 For loop

5.2 Demo 2 While loop

5.3 Demo 3 Range function

5.4 Test 1

6.1 Python Bootcamp Introduction

6.2 Data Science & ML introduction

6.3 Python Crash Course Introductory

6.4 Python Crash course lecture 1

6.5 Dictionaries in Python

6.6 Jupiter Python Exercises

6.7 Operators in Python

6.8 Iterative Statements

6.9 Python Functions

7.1 Data Analysis Numpy Part 1

7.2 Data Analysis Numpy Part 2

7.3 Data Analysis Numpy Part 3

7.4 Data Analysis Numpy Part 4

8.1 Data analysis Pandas part 1

8.2 Data analysis Pandas part 2

8.3 Data analysis pandas Part 3

8.4 Data analysis pandas Part 4

8.5 Data Analysis Pandas Part 5

8.6 Data Analysis Pandas Part 6

8.7 Data Analysis Pandas Part 7

9.1 Data Visualization Matplotlib Part 1

9.2 Data Visualization Matplotlib Part 2

10.1 Data Visualization seaborn Part 1

10.2 Data Visualization seaborn Part 2

11.1 Machine Learning part 1

11.2 Machine Learning part 2

12.1 to 12.12 At the end you will get bonus Java lecture series

The use of this programming language is to store different types of data into a variable format. Python is one of the absolute, flexible and powerful open source language which can be used in scientific computing, finance, oil and gas, physics and signal processing. This programming language has recently begun to gain an ever-increasing market share. The cross-platform nature of Python programming enables a better use for different tasks on any operating system. This provides an opportunity towards the Python developers to get utilized for different IT related roles. One of the most amazing features of Python is that it is actually one person’s work. Generally, new programming languages are developed and published by large companies employing lots of professionals experts, and due to copyright rules, it is very hard to name any of the persons involved in the project. Python is an exception to all of this as it is very convenient to use by anyone having a knowledge of programming language

Course Curriculum

Chapter 1: 2. Coding in Python

Lecture 1: 2.1 Variables in Python

Lecture 2: 2.2 Data Types

Lecture 3: 2.3 Lists in Python

Lecture 4: 2.4 Demo of List function

Lecture 5: 2.5 Tuples

Lecture 6: 2.6 Python Dictionary

Chapter 2: 3. Core of Python in depth

Lecture 1: 3.1 Python Operators

Lecture 2: 3.2 If Else statement

Lecture 3: 3.3 Indentation

Lecture 4: 3.4 Assignment 1

Chapter 3: 4. Multithreading in Python

Lecture 1: 4.1 Iterative Statements

Lecture 2: 4.2 While Statement

Lecture 3: 4.3 For Statement

Lecture 4: 4.4 Touple

Lecture 5: 4.5 Assignment 2

Chapter 4: 5. Demonstration

Lecture 1: 5.1 Demo 1 For loop

Lecture 2: 5.2 Demo 2 While loop

Lecture 3: 5.3 Demo 3 Range function

Chapter 5: 6 Python Bootcamp

Lecture 1: 6.1 Python Bootcamp Introduction

Lecture 2: 6.2 Data Science & ML introduction

Lecture 3: 6.3 Python Crash Course Introductory

Lecture 4: 6.4 Python Crash course lecture 1

Lecture 5: 6.5 Dictionaries in Python

Lecture 6: 6.6 Jupiter Python Exercises

Lecture 7: 6.7 Operators in Python

Lecture 8: 6.8 Iterative Statements

Lecture 9: 6.9 Python Functions

Chapter 6: 7. Data Analysis Numpy series

Lecture 1: 7.1 Data Analysis Numpy Part 1

Lecture 2: 7.2 Data Analysis Numpy Part 2

Lecture 3: 7.3 Data Analysis Numpy Part 3

Lecture 4: 7.4 Data Analysis Numpy Part 4

Chapter 7: 8. Data Analysis Pandas

Lecture 1: 8.1 Data analysis Pandas part 1

Lecture 2: 8.2 Data analysis Pandas part 2

Lecture 3: 8.3 Data analysis pandas Part 3

Lecture 4: 8.4 Data analysis pandas Part 4

Lecture 5: 8.5 Data Analysis Pandas Part 5

Lecture 6: 8.6 Data Analysis Pandas Part 6

Lecture 7: 8.7 Data Analysis Pandas Part 7

Chapter 8: 9. Data Visualization Matplotlib

Lecture 1: 9.1 Data Visualization Matplotlib Part 1

Lecture 2: 9.2 Data Visualization Matplotlib Part 2

Chapter 9: 10. Data Visualization seaborn

Lecture 1: 10.1 Data Visualization seaborn Part 1

Lecture 2: 10.2 Data Visualization seaborn Part 2

Chapter 10: 11. Machine Learning

Lecture 1: 11.1 Machine Learning part 1

Lecture 2: 11.2 Machine Learning part 2

Chapter 11: 13. (BONUS SERIES) Java Programming

Lecture 1: 1. Java introduction

Lecture 2: 2. Variables in Java

Lecture 3: 3. Identifiers in Java

Lecture 4: 4. Reserved Keywords in Java

Lecture 5: 5. Literals in Java

Lecture 6: 6. Arrays in Java

Lecture 7: 7. Strings in Java part 1

Lecture 8: 8. Strings in Java part 2

Lecture 9: 09. Operators in Java

Lecture 10: 10. Type Casting in Java

Lecture 11: 11. Control statement If else in Java

Lecture 12: 12. Switch Statement in Java

Chapter 12: 12. Advanced Machine Learning

Lecture 1: 01. MachineLearning introduction

Lecture 2: 02. Types of Machine Learning

Lecture 3: 03. Unsupervised Learning

Lecture 4: 04. Supervised Learning

Lecture 5: 05. Case study

Lecture 6: 06. Bayes Theorem

Lecture 7: 07. Support Vector Machine Algorithm

Lecture 8: 08. KNN Algorithm

Lecture 9: 09. Decision Tree

Lecture 10: 10. Hierrarchical Clustering

Lecture 11: 11. K Means Algorithm

Chapter 13: Speech Recognition by Python ( Mini Project )

Lecture 1: Speech recognition course

Lecture 2: Computer vision lecture 1

Lecture 3: Computer vision lecture 2

Lecture 4: Computer vision lecture 3

Lecture 5: Computer vision lecture 4

Lecture 6: Computer vision lecture 5

Chapter 14: Project Based Learning and Mini Project

Lecture 1: PBL1

Lecture 2: PBL2

Lecture 3: PBL3

Lecture 4: PBL4

Lecture 5: PBL5

Lecture 6: PBL6

Lecture 7: PBL7

Chapter 15: Web Development Bootcamp

Lecture 1: 1. Web development 1

Lecture 2: 2. Web development 2

Lecture 3: 3. Web development 3

Instructors

Global Education Foundation
Simplified Education should reach to all corners of World

Rating Distribution

1 stars: 24 votes

2 stars: 30 votes

3 stars: 68 votes

4 stars: 140 votes

5 stars: 224 votes

Frequently Asked Questions

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

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