[Udemy] Python for Data Science and Machine Learning Bootcamp
Python for Data Science and Machine Learning Bootcamp

Python for Data Science and Machine Learning Bootcamp

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Course Description

Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science! This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science an

d machine learning on Udemy! We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning: Programming with Python NumPy with Python Using pandas Data Frames to solve complex tasks Use pandas to handle Excel Files Web scraping with python Connect Python to SQL Use matplotlib and seaborn for data visualizations Use plotly for interactive visualizations Machine Learning with SciKit Learn, including: Linear Regression K Nearest Neighbors K Means Clustering Decision Trees Random Forests Natural Language Processing Neural Nets and Deep Learning Support Vector Machines and much, much more! Enroll in the course and become a data scientist today!

What you will learn

Use Python for Data Science and Machine Learning Use Spark for Big Data Analysis Implement Machine Learning Algorithms Learn to use NumPy for Numerical Data Learn to use Pandas for Data Analysis Learn to use Matplotlib for Python Plotting Learn to use Seaborn for statistical plots Use Plotly for interactive dynamic visualizations Use SciKit-Learn for Machine Learning Tasks K-Means Clustering Logistic Regression Linear Regression Random Forest and Decision Trees Natural Language Processing and Spam Filters Neural Networks Support Vector Machines

Curriculum

Section 1: Course Introduction

Section 2: Environment Set-Up

Section 3: Jupyter Overview

Section 4: Python Crash Course

Section 5: Python for Data Analysis - NumPy

Section 6: Python for Data Analysis - Pandas

Section 7: Python for Data Analysis - Pandas Exercises

Section 8: Python for Data Visualization - Matplotlib

Section 9: Python for Data Visualization - Seaborn

Section 10: Python for Data Visualization - Pandas Built-in Data Visualization

Section 11: Python for Data Visualization - Plotly and Cufflinks

Section 12: Python for Data Visualization - Geographical Plotting

Section 13: Data Capstone Project

Section 14: Introduction to Machine Learning

Section 15: Linear Regression

Section 16: Cross Validation and Bias-Variance Trade-Off

Section 17: Logistic Regression

Section 18: K Nearest Neighbors

Section 19: Decision Trees and Random Forests

Section 20: Support Vector Machines

Section 21: K Means Clustering

Section 22: Principal Component Analysis

Section 23: Recommender Systems

Section 24: Natural Language Processing

Section 25: Big Data and Spark with Python

Section 26: Neural Nets and Deep Learning