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Python for Data Science & Machine Learning: Zero to Hero

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4.6

Python for Data Science & Machine Learning: Zero to Hero

Master Data Science & Machine Learning in Python: Numpy, Pandas, Matplotlib, Scikit-Learn, Machine Learning, and more!

updated on icon Updated on Jul, 2024

language icon Language - English

person icon Meta Brains

English [CC]

category icon Development,Data Science

Lectures -187

Resources -13

Duration -5.5 hours

4.6

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Course Description

This machine learning course will provide you the fundamentals of how companies like Google, and Amazon, and even 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 prominence as 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.

Who this course is for:

  • 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

Goals

  • 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

Prerequisites

  • The ability to do simple math

  • No programming experience needed

  • No prior data science knowledge required

  • Readiness, flexibility, and passion for learning

Python for Data Science & Machine Learning: Zero to Hero

Curriculum

Check out the detailed breakdown of what’s inside the course

Introduction
21 Lectures
  • play icon Welcome to the Python for Data Science & ML bootcamp! 00:36 00:36
  • play icon Python: A Brief Overview 00:52 00:52
  • play icon The Python Installation Procedure 02:24 02:24
  • play icon What Jupyter is? 00:59 00:59
  • play icon Set up Anaconda on Different Operating Systems 04:15 04:15
  • play icon How to integrate Python into Jupyter? 00:44 00:44
  • play icon Handling Directories in Jupyter Notebook 02:48 02:48
  • play icon Input & Output 01:44 01:44
  • play icon Working with different datatypes 01:05 01:05
  • play icon Variables 01:50 01:50
  • play icon Arithmetic Operators 01:48 01:48
  • play icon Comparison Operators 00:43 00:43
  • play icon Logical Operators 03:05 03:05
  • play icon Conditional statements 02:20 02:20
  • play icon Loops 04:30 04:30
  • play icon Sequences Part 1: Lists 03:18 03:18
  • play icon Sequences Part 2: Dictionaries 02:48 02:48
  • play icon Sequences Part 3: Tuples 01:07 01:07
  • play icon Functions Part 1: Built-in Functions 00:26 00:26
  • play icon Functions Part 2: User-defined Functions 03:14 03:14
  • play icon Resources
The Must-Have Python Data Science Libraries
7 Lectures
Tutorialspoint
NumPy Mastery: Everything you need to know about NumPy
20 Lectures
Tutorialspoint
DataFrames and Series in Python's Pandas
22 Lectures
Tutorialspoint
Data Cleaning Techniques for Better Data
10 Lectures
Tutorialspoint
Exploratory Data Analysis in Python
10 Lectures
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Python for Time-Series Analysis: A Primer
5 Lectures
Tutorialspoint
Python for Data Visualization: Library Resources, and Sample Graphs
12 Lectures
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The Basics of Machine Learning
6 Lectures
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Simple Linear Regression with Python
6 Lectures
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Multiple Linear Regression with Python
8 Lectures
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Classification Algorithms: K-Nearest Neighbors
10 Lectures
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Classification Algorithms: Decision Tree
8 Lectures
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Classification Algorithms: Logistic regression
8 Lectures
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Clustering
15 Lectures
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Recommender System
17 Lectures
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Conclusion
2 Lectures
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Instructor Details

Meta Brains

Meta Brains


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