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Diploma in Python with Data Science and Machine Learning

Name: Diploma in Python with Data Science and Machine Learning
Rating: 4.1 (495 reviews)

Python Bootcamp with Data Science & Machine Learning with bonus Java series

Created byGlobal Education Foundation

Last updated 3/2023

English

What you'll 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

Course content

19 sections • 120 lectures • 31h 23m total length

2.1 Variables in Python4:39
Learn how to declare and assign variables in Python, understand memory locations, use comments, redeclare values, and print results.
2.2 Data Types3:34
Study python data types such as integers, floats, strings, booleans, and dictionaries, with examples of positive and negative numbers, sequences, and scientific notation.
2.3 Lists in Python4:50
Discover how lists in Python use square brackets to store multiple values of data types, with indexing from zero, duplicates allowed, items appended at the end, and length by len.
2.4 Demo of List function17:27
Explore Python list operations through a hands-on demo, covering list creation, indexing (including negative), slicing, printing, and modify operations like insert, extend, and delete, plus membership checks with in.
2.5 Tuples7:33
Explore Python tuples and lists: understand mutability differences, indexing, length with len, duplicates, and order; learn to unpack, concatenate, repeat, and convert between lists and tuples.
2.6 Python Dictionary4:06

3.1 Python Operators6:18
The lecture explains Python operators, including arithmetic, assignment, exponentiation, floor division, and modulo, plus comparison and logical operators, with practical examples and syntax demonstrations.
3.2 If Else statement3:50
Learn how if-else statements control program flow in Python, using conditional checks, nesting, and proper indentation to execute blocks or raise errors when misaligned.
3.3 Indentation5:32
3.4 Assignment 10:48
Practice hands-on coding on your computer to see how it works, emphasizing practical implementation over mere theory. Enhance your skills by tackling the assignment questions through regular practice at home.

4.1 Iterative Statements1:14
Explore iterative statements in Python, including for and while loops, which repeatedly execute a section of code as long as a given condition remains true.
4.2 While Statement2:41
Demonstrate how a while statement uses a condition to repeat printing a message, using the hello example, driven by user input, a counter, and consistent indentation.
4.3 For Statement1:30
4.4 Touple1:16
4.5 Assignment 21:15

5.1 Demo 1 For loop3:21
5.2 Demo 2 While loop10:40
Demonstrates using while and for loops to compute the sum of a list by initializing a sum to zero and updating it as the loop iterates, and prints the result.
5.3 Demo 3 Range function11:09
Explore how the Python range function iterates numbers in loops, starting from zero, ending before the end, and optionally stepping by a defined increment to produce even numbers.
Test 1
Test 2

6.1 Python Bootcamp Introduction1:55
This bootcamp introduces notebook usage, python basics (data types, operators, conditionals, functions), data analysis and Seabourne visualization, and an introduction to supervised learning and logistic regression.
6.2 Data Science & ML introduction4:08
6.3 Python Crash Course Introductory1:57
6.4 Python Crash course lecture 115:41
Explore Python data types from integers and floats to strings, lists, tuples, and dictionaries. Learn mutability, indexing, converting between types, and appending and deleting items.
6.5 Dictionaries in Python6:19
Explore dictionaries in Python by learning how key-value pairs work, how to create them with curly braces, and how to access, update, and delete items using keys.
6.6 Jupiter Python Exercises2:16
Tackle python list exercises by retrieving items, printing, appending numbers, removing elements, and sorting. Reinforce these with conditional statements or functions for practical data science projects.
6.7 Operators in Python13:59
Explore Python operators, including arithmetic, comparison, and logical operators, and learn how to apply them within if statements to build conditional logic.
6.8 Iterative Statements9:49
6.9 Python Functions6:54
Learn Python functions, including defining functions with parameters, blocks, and calls, and explore anonymous lambda functions for single-line expressions in data science and machine learning projects.

8.1 Data analysis Pandas part 14:55
8.2 Data analysis Pandas part 29:37
8.3 Data analysis pandas Part 39:11
Explore data analysis with pandas by building and indexing data structures, creating customized tables, and accessing values via dictionaries and keys, csv concepts and indexing tricks.
8.4 Data analysis pandas Part 44:24
8.5 Data Analysis Pandas Part 59:53
Explore importing data with pandas, inspecting data frames, and cleaning data by handling file paths, removing duplicates and non-values, and computing mean and median for analysis.
8.6 Data Analysis Pandas Part 611:45
Master Pandas basics to replace missing values with mean, median, or mode, edit column data, handle duplicates, and perform quick data analysis on a dataframe.
8.7 Data Analysis Pandas Part 76:09
Explore how to quantify relationships between dataset columns using correlation, focusing on numeric data and ignoring non-numeric values while visualizing with seaborn scatterplots and histograms for predictive insight.

10.1 Data Visualization seaborn Part 16:25
Explore seaborn data visualization by importing seaborn, inspecting a flight data dataset, and creating styled plots, including line and joint plots, color palettes, and grid layouts.
10.2 Data Visualization seaborn Part 29:39
Import seaborn to load datasets and explore its high-level, interactive visualization capabilities, including diverse plot types and line plots.

Requirements

Should be Interested

Description

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

Who this course is for:

Interested students

Diploma in Python with Data Science and Machine Learning

What you'll learn

Explore related topics

Course content

2. Coding in Python6 lectures • 42min

3. Core of Python in depth4 lectures • 16min

4. Multithreading in Python5 lectures • 8min

5. Demonstration3 lectures • 25min

6 Python Bootcamp9 lectures • 1hr 3min

7. Data Analysis Numpy series4 lectures • 58min

8. Data Analysis Pandas7 lectures • 56min

9. Data Visualization Matplotlib2 lectures • 24min

10. Data Visualization seaborn2 lectures • 16min

11. Machine Learning2 lectures • 19min

Requirements

Description

Who this course is for: