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!
Lectures -187
Resources -13
Duration -5.5 hours
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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
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Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
21 Lectures
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Welcome to the Python for Data Science & ML bootcamp! 00:36 00:36
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Python: A Brief Overview 00:52 00:52
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The Python Installation Procedure 02:24 02:24
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What Jupyter is? 00:59 00:59
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Set up Anaconda on Different Operating Systems 04:15 04:15
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How to integrate Python into Jupyter? 00:44 00:44
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Handling Directories in Jupyter Notebook 02:48 02:48
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Input & Output 01:44 01:44
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Working with different datatypes 01:05 01:05
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Variables 01:50 01:50
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Arithmetic Operators 01:48 01:48
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Comparison Operators 00:43 00:43
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Logical Operators 03:05 03:05
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Conditional statements 02:20 02:20
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Loops 04:30 04:30
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Sequences Part 1: Lists 03:18 03:18
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Sequences Part 2: Dictionaries 02:48 02:48
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Sequences Part 3: Tuples 01:07 01:07
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Functions Part 1: Built-in Functions 00:26 00:26
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Functions Part 2: User-defined Functions 03:14 03:14
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Resources
The Must-Have Python Data Science Libraries
7 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
NumPy Mastery: Everything you need to know about NumPy
20 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
DataFrames and Series in Python's Pandas
22 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Data Cleaning Techniques for Better Data
10 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Exploratory Data Analysis in Python
10 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Python for Time-Series Analysis: A Primer
5 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Python for Data Visualization: Library Resources, and Sample Graphs
12 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
The Basics of Machine Learning
6 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Simple Linear Regression with Python
6 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Multiple Linear Regression with Python
8 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Classification Algorithms: K-Nearest Neighbors
10 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Classification Algorithms: Decision Tree
8 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Classification Algorithms: Logistic regression
8 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Clustering
15 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Recommender System
17 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Conclusion
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Instructor Details
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Meta Brains
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