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Master Data Science and Machine Learning with Python: A Comprehensive Bootcamp

Unleash the full potential of your data science and machine learning with python skills as you explore the applications of NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, TensorFlow, and various other powerful tools, techniques, and frameworks.

Created by- Jose Portilla

What you’ll learn

  • Python: Data Science and Machine Learning
  • Spark: Big Data Analysis
  • Machine Learning Algorithms
  • NumPy: Numerical Data
  • Pandas: Data Analysis
  • Matplotlib: Python Plotting
  • Seaborn: Statistical Plots
  • Plotly: Dynamic Visualizations
  • SciKit-Learn: Machine Learning Tasks
  • K-Means Clustering
  • Logistic Regression
  • Linear Regression
  • Random Forest & Decision Trees
  • NLP & Spam Filters
  • Neural Networks
  • Support Vector Machines
Data Science and Machine Learning with Python

Course Content

  • Course Introduction: Get an overview of the course and what to expect.
  • Environment Set-Up: Learn how to set up your programming environment.
  • Jupyter Overview: Understand the basics of Jupyter, a popular tool for interactive programming.
  • Python Crash Course: Dive into a quick crash course on Python programming.
  • Python for Data Analysis – NumPy: Explore the use of NumPy for efficient numerical data manipulation.
  • Python for Data Analysis – Pandas: Learn how to use Pandas for powerful data analysis and manipulation.
  • Python for Data Analysis – Pandas Exercises: Practice your data analysis skills with hands-on Pandas exercises.
  • Python for Data Visualization – Matplotlib: Discover the capabilities of Matplotlib for creating visualizations in Python.
  • Python for Data Visualization – Seaborn: Explore Seaborn for advanced statistical data visualization.
  • Python for Data Visualization – Pandas Built-in Data Visualization: Utilize Pandas’ built-in visualization capabilities.
  • Python for Data Visualization – Plotly and Cufflinks: Learn how to create interactive visualizations with Plotly and Cufflinks.
  • Python for Data Visualization – Geographical Plotting: Dive into geographical plotting using Python.
  • Data Capstone Project: Apply your newly acquired skills to a real-world data analysis project.
  • Introduction to Machine Learning: Gain an understanding of the fundamentals of machine learning.
  • Linear Regression: Learn how to implement linear regression for predictive modeling.
  • Cross Validation and Bias-Variance Trade-Off: Explore cross-validation techniques and the bias-variance trade-off in machine learning.
  • Logistic Regression: Understand logistic regression and its applications in classification problems.
  • K Nearest Neighbors: Discover the K Nearest Neighbors algorithm for pattern recognition and classification.
  • Decision Trees and Random Forests: Learn about decision trees and random forests for complex decision-making.
  • Support Vector Machines: Understand the principles and applications of Support Vector Machines in machine learning.
  • K Means Clustering: Dive into K Means Clustering for unsupervised data clustering.
  • Principal Component Analysis: Explore Principal Component Analysis for dimensionality reduction and data visualization.
  • Recommender Systems: Learn how to build recommendation systems using collaborative filtering techniques.
  • Natural Language Processing: Discover the basics of Natural Language Processing and its applications.
  • Neural Nets and Deep Learning: Understand the fundamentals of neural networks and deep learning.
  • Big Data and Spark with Python: Explore big data analysis using Spark and Python.
  • BONUS SECTION: THANK YOU!: Conclude the course with a bonus section expressing gratitude and final thoughts.

Requirements of Data Science and Machine Learning with Python

  • Prior Programming Experience: Master Data Science and Machine Learning with Python course is suitable for individuals with some background in programming.
  • Experience Requirement: It is recommended to have a basic understanding of programming concepts in this Data Science and Machine Learning with Python course and familiarity with at least one programming language.
  • Admin Permissions: To fully benefit from Data Science and Machine Learning with Python course, you will need administrative permissions on your computer to download necessary files and tools.
  • Downloading Files: Access to download files is required to set up the programming environment and utilize various resources throughout the Master Data Science and Machine Learning with Python course.

Description

Embark on Your Journey to Become a Data Scientist!

Join Master Data Science and Machine Learning with Python comprehensive course and unlock the power of Python for data analysis, stunning visualizations, and advanced machine learning algorithms.

According to Glassdoor, Data Scientist has been ranked as the number one job, offering exciting challenges and a promising career path. With an average salary of over $120,000 in the United States (Indeed), data science opens doors to solving intriguing global problems.

Unlock Your Data Science Potential: Beginner or Experienced Developer, This Course is for You!

Whether you’re a beginner with some programming experience or an experienced developer aiming to transition into Data Science, Data Science and Machine Learning with Python comprehensive course caters to your learning needs.

Comparable to high-priced Data Science bootcamps, Master Data Science and Machine Learning with Python course offers the same comprehensive curriculum at a fraction of the cost. With over 100 HD video lectures and detailed code notebooks accompanying each lecture, it is one of the most comprehensive resources for Data Science and Machine Learning on Udemy.

In Master Data Science and Machine Learning with Python course, we will equip you with the skills to program in Python, create captivating data visualizations, and leverage Machine Learning with Python. Here are some of the exciting topics we’ll cover:

  • Programming with Python
  • Harnessing the power of NumPy in Python
  • Solving complex tasks with pandas Data Frames
  • Excel file handling using pandas
  • Web scraping with Python
  • Connecting Python to SQL databases
  • Data visualizations with matplotlib and seaborn
  • Interactive visualizations with plotly
  • 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!

This course is suitable for

  • Master Data Science and Machine Learning with Python course is tailored for individuals with a foundational understanding of programming.

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Frequently Asked Questions (FAQs)

Q: What topics are covered in the course?

A: Python programming, data analysis, data visualization, machine learning algorithms, and more.

Q: What are the requirements to take the course?

A: Prior programming experience, basic understanding of programming concepts, administrative permissions to download files.

Q: Who is the course suitable for?

A: Individuals with some programming experience and those interested in data science and machine learning.

Q: What is the course content?

A: The course covers various topics including Python programming, data analysis with NumPy and Pandas, data visualization with Matplotlib and Seaborn, machine learning algorithms, and more.

Q: What is the course’s description?

A: The course provides a comprehensive learning experience in data science and machine learning with Python, covering programming, data visualization, and machine learning concepts.

Q: What is the course suitable for?

A: The course is suitable for individuals with a foundational understanding of programming.

Q: Who is the course creator?

A: The course was created by Jose Portilla.

Q: What will students learn in the course?

A: Students will learn Python programming, data analysis with NumPy and Pandas, data visualization with Matplotlib and Seaborn, machine learning algorithms, and more.

Q: What are some of the topics covered in the course?

A: Some topics covered include linear regression, logistic regression, decision trees, support vector machines, natural language processing, neural networks, and big data analysis with Spark.

Q: Is there any bonus section in the course?

A: Yes, the course concludes with a bonus section expressing gratitude and final thoughts.

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