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
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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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