Fantastic Python: Data Science & Machine Learning

Fantastic Python: Data Science & Machine Learning, available at $59.99, has an average rating of 4.13, with 222 lectures, based on 8 reviews, and has 43 subscribers.

You will learn about Python coding fundamentals Machine Learning Data manipulation with pandas Data visualization with Seaborn and pandas Object-Oriented Programming Applications in Finance, Real-Estate Market, Image Recognition and many more OLS Linear Regressions Logistic Regressions Linear Discriminant Analysis Neural Networks Principal Component Analysis (PCA) Support Vector Machines K-Nearest Neighbors Algorithm K-Means Clustering Decision Tree Random Forest This course is ideal for individuals who are Beginners or Intermediate-level Python coders who wants to level up their Machine Learning skills or All aspiring data scientists, data analysts and data engineers or Professionals/students from other disciplines (business, marketing, finance, accounting, medicine, law, etc) or Anyone who is curious about Python and/or Machine Learning It is particularly useful for Beginners or Intermediate-level Python coders who wants to level up their Machine Learning skills or All aspiring data scientists, data analysts and data engineers or Professionals/students from other disciplines (business, marketing, finance, accounting, medicine, law, etc) or Anyone who is curious about Python and/or Machine Learning.

Enroll now: Fantastic Python: Data Science & Machine Learning

Summary

Title: Fantastic Python: Data Science & Machine Learning

Price: $59.99

Average Rating: 4.13

Number of Lectures: 222

Number of Published Lectures: 222

Number of Curriculum Items: 222

Number of Published Curriculum Objects: 222

Original Price: $124.99

Quality Status: approved

Status: Live

What You Will Learn

Python coding fundamentals
Machine Learning
Data manipulation with pandas
Data visualization with Seaborn and pandas
Object-Oriented Programming
Applications in Finance, Real-Estate Market, Image Recognition and many more
OLS Linear Regressions
Logistic Regressions
Linear Discriminant Analysis
Neural Networks
Principal Component Analysis (PCA)
Support Vector Machines
K-Nearest Neighbors Algorithm
K-Means Clustering
Decision Tree
Random Forest

Who Should Attend

Beginners
Intermediate-level Python coders who wants to level up their Machine Learning skills
All aspiring data scientists, data analysts and data engineers
Professionals/students from other disciplines (business, marketing, finance, accounting, medicine, law, etc)
Anyone who is curious about Python and/or Machine Learning

Target Audiences

Beginners
Intermediate-level Python coders who wants to level up their Machine Learning skills
All aspiring data scientists, data analysts and data engineers
Professionals/students from other disciplines (business, marketing, finance, accounting, medicine, law, etc)
Anyone who is curious about Python and/or Machine Learning

This course in the Fantastic Python Series is a complete guide on Python Coding & Machine Learning for beginners and intermediate level coders. You will learn not only Python, but also how to conduct data analysis, data visualization and Machine Learning (ML) using pandas, numpy, scikit-learn, statsmodels, seaborn and more.

Practical Examples for ML includes: (1) hand-written digits classification; (2) facial recognition; (3) heart-disease prediction; (4) penguins classification; (5) World Happiness Index; and many more.

In particular, this course consists of 3 major parts (“mini-courses”):

Learn Python Coding
- All essential data types and common operations
- Comprehensive string manipulations
- Control flows
- Lists, Tuples and Sets
- Dictionaries
- Object-Oriented Programming
- Inheritance
- Datetime
- Modules and Packages
- Exceptions Handling, etc
Learn Data Analytics and Visualization with pandas and Seaborn
- Series and Data Frames
- Indexing, filtering, sorting, counting, etc
- Merge/Joins
- Aggregation
- Line plots
- Bar plots
- Scatter plots
- Histogram, etc
Learn Machine Learning with Scikit-Learn
- Linear Regressions
- Logistic Regressions
- Linear Discriminant Analysis
- Principal Component Analysis
- K-Means
- K-Nearest Neighbors
- Support Vector Machines
- Neural Networks
- Decision Trees
- Random Forests
- Hyper-parameters Tuning

The course is one of the most comprehensive and detailed course ever on the Pandas package. It highlights the complexity of data wrangling which occupies about 80% of data scientists’ time, and gives you a solid foundation to meet the challenging requirements of handling messy real-world data.

The focus for Machine Learning (ML) is on practical applications and gaining an intuitive understanding of the algorithms rather than diving into the theories and mathematics.

By the end of this course, you will not only become a competent Python programmer, but also a budding data scientist ready to take on real-world challenges.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Introduction

Lecture 2: Why Learning Python?

Lecture 3: Python as a Programming Language

Lecture 4: The Python Interpreter

Lecture 5: Python is Dynamically Typed

Lecture 6: Course Structure & Content

Chapter 2: Installation of Python and Jupyter Notebook

Lecture 1: Installations on Windows PC

Lecture 2: Python Package Manager "pip" on Windows PC

Lecture 3: Installations on Mac

Lecture 4: Python Package Manager "pip" on Mac

Chapter 3: Numeric Data and Computations

Lecture 1: Introduction to Python Data Types

Lecture 2: 3 Numeric Data Types

Lecture 3: Basic Computations in Python

Lecture 4: How to Calculate Exponentiation/Power?

Lecture 5: How to Do Integer Divisions and the Remainder Easily

Lecture 6: Complex Numbers for smart Mathematicians and Physicists

Chapter 4: The Magic of Python Strings

Lecture 1: Strings in Python

Lecture 2: The length of a string

Lecture 3: The Power of String indexing

Lecture 4: Find the location of Matching Substrings

Lecture 5: Change to Lower and/or Upper Cases

Lecture 6: How to Replace Parts of a String

Lecture 7: How to Strip Whitespaces

Chapter 5: Boolean and Logical Operators

Lecture 1: The Boolean Data Type

Lecture 2: Logical Operator: AND

Lecture 3: Logical Operator: OR

Lecture 4: Logical Operator: NOT

Lecture 5: Compound/Complex Logical Operations

Chapter 6: None Type and Data Type Conversions

Lecture 1: The None Type

Lecture 2: Converting to-and-from Numeric Data Types

Lecture 3: Converting to-and-from Strings

Lecture 4: Converting to-and-from Booleans

Lecture 5: Converting to-and-from None

Chapter 7: Variables Names, Creation and Destruction

Lecture 1: How to Create, Name and Destroy Variables

Lecture 2: Handy Tricks in Creating Variables

Lecture 3: More Handy Tricks in Creating Variables

Chapter 8: More Complex Data Structures – Lists and Tuples

Lecture 1: What are Lists and Tuples?

Lecture 2: Indexing Lists

Lecture 3: How to Add and Delete Items in a List?

Lecture 4: How to Manipulate Lists?

Lecture 5: The "Weird" Copying Behaviors

Lecture 6: A Challenge for You!

Lecture 7: Tuples

Lecture 8: Unpacking Lists & Tuples Easily

Chapter 9: Sets

Lecture 1: What are Sets?

Lecture 2: Basic Syntax for Sets

Lecture 3: Modifying Sets and Other Set Operations

Chapter 10: Python Fun: Shakepeare Sonnets Challenge

Lecture 1: The Challenge

Lecture 2: Solutions Part 1: Data Cleaning

Lecture 3: Solutions Part 2: Text Analysis

Chapter 11: Control Flows 1: if-else and switch-case

Lecture 1: if-else statement

Lecture 2: A little more complexity

Lecture 3: The switch-case statement for multiple options

Chapter 12: Control Flows 2: "for" loop and "while" loop

Lecture 1: The Classic For-Loop

Lecture 2: For Loops with a Range

Lecture 3: Looping over Strings

Lecture 4: Fabonacci Series and the "break" keyword in for loops

Lecture 5: For Loops with Twists (Skip & Continue)

Lecture 6: Demystifying List Comprehension

Lecture 7: The "While" Loop

Lecture 8: While Loop with twists (break & continue)

Chapter 13: Python Fun: The Lincoln's Gettyburg Address Challenge!

Lecture 1: The Challenge

Lecture 2: Solutions 1: Data Cleaning

Lecture 3: Solutions 2: Answering First Two Questions

Lecture 4: Solutions 3: Answering the Rest

Chapter 14: Functions in Python

Lecture 1: What are Functions?

Lecture 2: Function Arguments: Positional vs. Named

Lecture 3: Default vs Compulsory Arguments

Lecture 4: The Order of Arguments Matter and Common Mistakes

Lecture 5: Python Fun: The Harry Potter Challenge

Lecture 6: Python Standard Library – Math

Lecture 7: Python Standard Library – Random

Lecture 8: The Mystery of *args

Lecture 9: * Recursive Functions (Horror!) – Advanced/Optional

Lecture 10: Functions are Objects Too!

Chapter 15: Python Fun: Coin Toss Simulation

Lecture 1: The Basic Idea and Setup

Lecture 2: Coding the Complex Simulations

Lecture 3: Visualizing the Result – the Central Limit Theorem

Chapter 16: Dictionary as a Powerful Data Structure

Lecture 1: What is a Dictionary?

Lecture 2: Creating Dictionaries via Zip

Lecture 3: The Basic Calories Counter App

Lecture 4: An Interactive Calories Counter App

Lecture 5: How to Loop through a Dictionary?

Lecture 6: Dictionary Manipulations

Instructors

Richard Wang
Professor and Entrepreneur

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 2 votes
4 stars: 2 votes
5 stars: 3 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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