Python for Data Science & Machine Learning: Zero to Hero

Python for Data Science & Machine Learning: Zero to Hero, available at $59.99, has an average rating of 4.35, with 187 lectures, based on 517 reviews, and has 60644 subscribers.

You will learn about Gain familiarity with Pandas, a data analysis tool Get a grasp on the theory behind basic and multiple linear regression Tackle regression problems easily Discover the logic behind decision trees Acquaint yourself with the various clustering algorithms This course is ideal for individuals who are Aspiring Machine Learning Professionals or Anyone interested in expanding their skill set with machine learning and Python or Inquisitive technologists interested in seeing Machine Learning in action or Those who are already proficient in programming and want to expand their capabilities by learning about machine learning It is particularly useful for Aspiring Machine Learning Professionals or Anyone interested in expanding their skill set with machine learning and Python or Inquisitive technologists interested in seeing Machine Learning in action or Those who are already proficient in programming and want to expand their capabilities by learning about machine learning.

Enroll now: Python for Data Science & Machine Learning: Zero to Hero

Summary

Title: Python for Data Science & Machine Learning: Zero to Hero

Price: $59.99

Average Rating: 4.35

Number of Lectures: 187

Number of Published Lectures: 187

Number of Curriculum Items: 187

Number of Published Curriculum Objects: 187

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will Learn

Gain familiarity with Pandas, a data analysis tool
Get a grasp on the theory behind basic and multiple linear regression
Tackle regression problems easily
Discover the logic behind decision trees
Acquaint yourself with the various clustering algorithms

Who Should Attend

Aspiring Machine Learning Professionals
Anyone interested in expanding their skill set with machine learning and Python
Inquisitive technologists interested in seeing Machine Learning in action
Those who are already proficient in programming and want to expand their capabilities by learning about machine learning

Target Audiences

Aspiring Machine Learning Professionals
Anyone interested in expanding their skill set with machine learning and Python
Inquisitive technologists interested in seeing Machine Learning in action
Those who are already proficient in programming and want to expand their capabilities by learning about machine learning

This machine learning course will provide you the fundamentals of how companies like Google, Amazon, and even Udemy utilize machine learning and artificial intelligence (AI) to glean meaning and insights from massive data sets. Glassdoor and Indeed both report that the average salary for a data scientist is $120,000. This is the standard, not the exception.

Data scientists are already quite desirable. It’s difficult to keep them on staff in today’s tight labor market. There is a severe shortage of people who possess the rare combination of scientific training, computer expertise, and analytical talents.

Today’s data scientists are held to the same standards as the Wall Street “quants” of the ’80s and ’90s. When the need arose for innovative algorithms and data approaches, physicists and mathematicians flocked to investment banks and hedge funds.

So, it’s no surprise that data science is rising to prominenceas a promising career path in the modern day. It is analytic in focus, driven by code, and performed on a computer. As a result, it shouldn’t be a shock that the demand for data scientists has been growing steadily in the workplace for the past few years.

On the other hand, availability has been low. Obtaining the education and experience necessary to be hired as a data scientist is tough. And that’s why we made this course in the first place!

Each topic is described in plain English, and the course does its best to avoid mathematical notations and jargon. Once you have access to the source code, you can experiment with it and improve upon it. Learning and applying these algorithms in the real world, rather than in a theoretical or academic setting, is the focus of this course.

Each video will leave you with a new perspective that you can implement right away!

If you have no background in statistics, don’t let that stop you from enrolling in this course; we welcome students of all levels.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome to the Python for Data Science & ML bootcamp!

Lecture 2: Python: A Brief Overview

Lecture 3: The Python Installation Procedure

Lecture 4: What Jupyter is?

Lecture 5: Set up Anaconda on Different Operating Systems

Lecture 6: How to integrate Python into Jupyter?

Lecture 7: Handling Directories in Jupyter Notebook

Lecture 8: Input & Output

Lecture 9: Working with different datatypes

Lecture 10: Variables

Lecture 11: Arithmetic Operators

Lecture 12: Comparison Operators

Lecture 13: Logical Operators

Lecture 14: Conditional statements

Lecture 15: Loops

Lecture 16: Sequences Part 1: Lists

Lecture 17: Sequences Part 2: Dictionaries

Lecture 18: Sequences Part 3: Tuples

Lecture 19: Functions Part 1: Built-in Functions

Lecture 20: Functions Part 2: User-defined Functions

Lecture 21: Course Materials

Chapter 2: The Must-Have Python Data Science Libraries

Lecture 1: Completing Library Setup

Lecture 2: Library Importing

Lecture 3: Pandas: A Data Science Library

Lecture 4: NumPy: A Data Science Library

Lecture 5: NumPy vs. Pandas

Lecture 6: Matplotlib Library for Data Science

Lecture 7: Seaborn Library for Data Science

Chapter 3: NumPy Mastery: Everything you need to know about NumPy

Lecture 1: Intro to NumPy arrays

Lecture 2: Creating NumPy arrays

Lecture 3: Indexing NumPy arrays

Lecture 4: Array shape

Lecture 5: Iterating Over NumPy Arrays

Lecture 6: Basic NumPy arrays: zeros()

Lecture 7: Basic NumPy arrays: ones()

Lecture 8: Basic NumPy arrays: full()

Lecture 9: Adding a scalar

Lecture 10: Subtracting a scalar

Lecture 11: Multiplying by a scalar

Lecture 12: Dividing by a scalar

Lecture 13: Raise to a power

Lecture 14: Transpose

Lecture 15: Element-wise addition

Lecture 16: Element-wise subtraction

Lecture 17: Element-wise multiplication

Lecture 18: Element-wise division

Lecture 19: Matrix multiplication

Lecture 20: Statistics

Chapter 4: DataFrames and Series in Python's Pandas

Lecture 1: What is a Python Pandas DataFrame?

Lecture 2: What is a Python Pandas Series?

Lecture 3: DataFrame vs Series

Lecture 4: Creating a DataFrame using lists

Lecture 5: Creating a DataFrame using a dictionary

Lecture 6: Loading CSV data into python

Lecture 7: Changing the Index Column

Lecture 8: Inplace

Lecture 9: Examining the DataFrame: Head & Tail

Lecture 10: Statistical summary of the DataFrame

Lecture 11: Slicing rows using bracket operators

Lecture 12: Indexing columns using bracket operators

Lecture 13: Boolean list

Lecture 14: Filtering Rows

Lecture 15: Filtering rows using & and | operators

Lecture 16: Filtering data using loc()

Lecture 17: Filtering data using iloc()

Lecture 18: Adding and deleting rows and columns

Lecture 19: Sorting Values

Lecture 20: Exporting and saving pandas DataFrames

Lecture 21: Concatenating DataFrames

Lecture 22: groupby()

Chapter 5: Data Cleaning Techniques for Better Data

Lecture 1: Introduction to Data Cleaning

Lecture 2: Quality of Data

Lecture 3: Examples of Anomalies

Lecture 4: Median-based Anomaly Detection

Lecture 5: Mean-based anomaly detection

Lecture 6: Z-score-based Anomaly Detection

Lecture 7: Interquartile Range for Anomaly Detection

Lecture 8: Dealing with missing values

Lecture 9: Regular Expressions

Lecture 10: Feature Scaling

Chapter 6: Exploratory Data Analysis in Python

Lecture 1: Introduction

Lecture 2: What is Exploratory Data Analysis?

Lecture 3: Univariate Analysis

Lecture 4: Univariate Analysis: Continuous Data

Lecture 5: Univariate Analysis: Categorical Data

Lecture 6: Bivariate analysis: Continuous & Continuous

Lecture 7: Bivariate analysis: Categorical & Categorical

Lecture 8: Bivariate analysis: Continuous & Categorical

Lecture 9: Detecting Outliers

Lecture 10: Categorical Variable Transformation

Chapter 7: Python for Time-Series Analysis: A Primer

Lecture 1: Introduction to Time Series

Lecture 2: Getting stock data using yfinance

Lecture 3: Converting a Dataset into Time Series

Instructors

Meta Brains
Let's code & build the metaverse together!

Rating Distribution

1 stars: 4 votes
2 stars: 15 votes
3 stars: 59 votes
4 stars: 205 votes
5 stars: 234 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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