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Data Science & Machine Learning Study Bootcamp: From A to Z.

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Data Science & Machine Learning Study Bootcamp: From A to Z., available at $54.99, has an average rating of 4.84, with 130 lectures, based on 22 reviews, and has 52 subscribers.

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You will learn about Fundamentals of Data Science: Grasp the core concepts and principles underlying data science. Data Analysis with Python: Utilize Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization. Machine Learning Algorithms: Understand and implement regression, classification, and clustering algorithms using Scikit-learn. Model Building and Evaluation: Learn to build predictive models and assess their accuracy with real-world datasets. Data Preprocessing Techniques: Master essential data cleaning, transformation, and feature engineering methods. Practical Applications: Apply data science skills to real-world projects and case studies. Model Deployment: Gain insights into deploying machine learning models in production environments. Career Preparation: Receive guidance on preparing for technical interviews and building a data science portfolio. Python Programming for Data Science: Solidify your Python skills for data science tasks. Data Visualization: Create informative and visually appealing data visualizations to communicate insights. This course is ideal for individuals who are Beginners: Individuals with little to no experience in data science who want to gain a solid foundation. or Aspiring Data Scientists: Those looking to build a career in data science or machine learning. or Analysts and Professionals: Individuals working with data who want to enhance their skillset. or Students: Students interested in exploring the field of data science. or Python Enthusiasts: Anyone wanting to apply Python programming to data-related tasks. or Career Changers: Professionals from other fields looking to transition into data science. or Decision Makers: Managers and executives seeking to understand the potential of data science. pen_spark or Curious Minds: Individuals interested in learning about the practical applications of machine learning. or Tech Enthusiasts: Anyone fascinated by the advancements in artificial intelligence and data-driven technologies. or Problem Solvers: Individuals who enjoy analyzing data and finding patterns to solve real-world challenges. It is particularly useful for Beginners: Individuals with little to no experience in data science who want to gain a solid foundation. or Aspiring Data Scientists: Those looking to build a career in data science or machine learning. or Analysts and Professionals: Individuals working with data who want to enhance their skillset. or Students: Students interested in exploring the field of data science. or Python Enthusiasts: Anyone wanting to apply Python programming to data-related tasks. or Career Changers: Professionals from other fields looking to transition into data science. or Decision Makers: Managers and executives seeking to understand the potential of data science. pen_spark or Curious Minds: Individuals interested in learning about the practical applications of machine learning. or Tech Enthusiasts: Anyone fascinated by the advancements in artificial intelligence and data-driven technologies. or Problem Solvers: Individuals who enjoy analyzing data and finding patterns to solve real-world challenges.

Enroll now: Data Science & Machine Learning Study Bootcamp: From A to Z.

Summary

Title: Data Science & Machine Learning Study Bootcamp: From A to Z.

Price: $54.99

Average Rating: 4.84

Number of Lectures: 130

Number of Published Lectures: 130

Number of Curriculum Items: 132

Number of Published Curriculum Objects: 132

Original Price: $59.99

Quality Status: approved

Status: Live

What You Will Learn

  • Fundamentals of Data Science: Grasp the core concepts and principles underlying data science.
  • Data Analysis with Python: Utilize Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization.
  • Machine Learning Algorithms: Understand and implement regression, classification, and clustering algorithms using Scikit-learn.
  • Model Building and Evaluation: Learn to build predictive models and assess their accuracy with real-world datasets.
  • Data Preprocessing Techniques: Master essential data cleaning, transformation, and feature engineering methods.
  • Practical Applications: Apply data science skills to real-world projects and case studies.
  • Model Deployment: Gain insights into deploying machine learning models in production environments.
  • Career Preparation: Receive guidance on preparing for technical interviews and building a data science portfolio.
  • Python Programming for Data Science: Solidify your Python skills for data science tasks.
  • Data Visualization: Create informative and visually appealing data visualizations to communicate insights.

Who Should Attend

  • Beginners: Individuals with little to no experience in data science who want to gain a solid foundation.
  • Aspiring Data Scientists: Those looking to build a career in data science or machine learning.
  • Analysts and Professionals: Individuals working with data who want to enhance their skillset.
  • Students: Students interested in exploring the field of data science.
  • Python Enthusiasts: Anyone wanting to apply Python programming to data-related tasks.
  • Career Changers: Professionals from other fields looking to transition into data science.
  • Decision Makers: Managers and executives seeking to understand the potential of data science. pen_spark
  • Curious Minds: Individuals interested in learning about the practical applications of machine learning.
  • Tech Enthusiasts: Anyone fascinated by the advancements in artificial intelligence and data-driven technologies.
  • Problem Solvers: Individuals who enjoy analyzing data and finding patterns to solve real-world challenges.

Target Audiences

  • Beginners: Individuals with little to no experience in data science who want to gain a solid foundation.
  • Aspiring Data Scientists: Those looking to build a career in data science or machine learning.
  • Analysts and Professionals: Individuals working with data who want to enhance their skillset.
  • Students: Students interested in exploring the field of data science.
  • Python Enthusiasts: Anyone wanting to apply Python programming to data-related tasks.
  • Career Changers: Professionals from other fields looking to transition into data science.
  • Decision Makers: Managers and executives seeking to understand the potential of data science. pen_spark
  • Curious Minds: Individuals interested in learning about the practical applications of machine learning.
  • Tech Enthusiasts: Anyone fascinated by the advancements in artificial intelligence and data-driven technologies.
  • Problem Solvers: Individuals who enjoy analyzing data and finding patterns to solve real-world challenges.

Welcome to the most in-depth and engaging Machine Learning & Data Science Bootcamp designed to equip you with practical skills and knowledge for a successful career in the AI field. This comprehensive course is tailor-made for beginners and aspiring professionals alike, guiding you from the fundamentals to advanced topics, with a strong emphasis on Python programming and real-world applications.

Become a master of Machine Learning, Deep Learning, and Data Science with Python in this comprehensive bootcamp. This course is designed to take you from beginner to expert, equipping you with the skills to build powerful AI models, solve real-world problems, and land your dream job in 2024.

Master the fundamentals of Data Science:

  • Learn how to work with data effectively, from collection and cleaning to analysis and visualization.

  • Master essential Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and exploration.

  • Discover the power of data preprocessing techniques to enhance your model’s performance.

Unlock the potential of Machine Learning with Python:

  • Dive into the core concepts of machine learning algorithms, including regression, classification, and clustering.

  • Implement popular ML algorithms using Scikit-learn, the go-to library for ML in Python.

  • Build your own predictive models and evaluate their accuracy with real-world datasets.

Launch your career in Data Science and Machine Learning:

  • Gain practical experience by working on real-world projects and case studies.

  • Learn how to deploy your models in production environments to create real-world impact.

  • Prepare for technical interviews and land your dream job with career guidance and tips.

Why choose this course:

  • Comprehensive curriculum covering all essential aspects of Data Science, ML, and Deep Learning with Python.

  • Hands-on approach with practical exercises, projects, and quizzes to reinforce your learning.

  • Expert instruction from experienced professionals in the field.

  • Lifetime access to course materials, so you can learn at your own pace and revisit concepts as needed.

  • Active community support to connect with fellow learners and get your questions answered.

Whether you’re a complete beginner or have some prior experience, this bootcamp will provide you with the knowledge and skills to excel in the exciting world of Data Science and Machine Learning. Enroll today and start your journey towards a rewarding career in AI!

What you’ll learn:

  • Python for Data Science & ML: Master Python, the language of choice for data professionals, and essential libraries (NumPy, Pandas, Matplotlib) for manipulating, analyzing, and visualizing data effectively.

  • Machine Learning Fundamentals: Gain a deep understanding of ML algorithms (Linear Regression, Logistic Regression, Decision Trees, Random Forests), model evaluation, and deployment.

  • Data Science Essentials: Learn to work with data, perform exploratory data analysis (EDA), feature engineering, and extract meaningful insights to drive decision-making.

  • Real-World Projects: Apply your learning to practical projects, building a portfolio showcasing your skills to potential employers.

  • Career Preparation: Get expert guidance on building a strong resume, acing technical interviews, and navigating the job market.

Why choose this course:

  • 2024 Edition: Fully updated with the latest ML & DS techniques, libraries, and industry best practices.

  • Hands-On Learning: Immerse yourself in practical exercises, real-world projects, and quizzes to reinforce your understanding.

  • Expert Instruction: Learn from experienced data scientists and ML engineers passionate about sharing their knowledge.

  • Lifetime Access: Learn at your own pace, anytime, anywhere, and revisit the material whenever you need a refresher.

  • Supportive Community: Connect with fellow learners, get help when you need it, and collaborate on projects.

No prior experience is required. Whether you’re a complete beginner or looking to enhance your existing skills, this course will empower you to become a proficient ML & DS practitioner, ready to tackle the challenges of the AI-driven world.

Enroll now and unlock your potential in the exciting fields of Machine Learning and Data Science!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Understanding Data Types and Structures in Python

Lecture 3: Understanding Python Data Structure Wrap up

Chapter 2: Python Refresher

Lecture 1: String Functions in Python Part 1

Lecture 2: String Functions in Python Part 2

Lecture 3: String Functions in Python Part 3

Lecture 4: String Functions in Python Part 4

Lecture 5: String Functions in Python Part 5

Lecture 6: Lists.

Lecture 7: Tuples.

Lecture 8: Sets.

Lecture 9: Dictionaries

Lecture 10: Control Flow IF

Lecture 11: For Loop Part 1.

Lecture 12: For Loop Part 2.

Lecture 13: While Loop Part 1.

Lecture 14: While Loop Part 2.

Lecture 15: While Loop Best Practices.

Lecture 16: Introduction to Functions in Python.

Lecture 17: Functions in Python and Arguments.

Lecture 18: Function Tips & Tricks: Recursion.

Lecture 19: Function Tips & Tricks: Functions Decorators and Higher Order Functions.

Lecture 20: Functions Tips & Tricks: Lambda Functions.

Lecture 21: Function Tips & Tricks: Functions Caching & Memoization.

Lecture 22: Error Handling in Python.

Lecture 23: Files and Modules in Python.

Lecture 24: Creating Simple Class.

Lecture 25: Overviewing Constructor.

Lecture 26: Learning How to creating Dunder Methods?

Lecture 27: Learning about Inheritance.

Lecture 28: Knowing What is the Encapsulation?

Lecture 29: Learning also about Multiple Inheritance.

Lecture 30: Knowing What is the Overriding?

Lecture 31: Learning about Decorators.

Lecture 32: Learning How to use Build-in Decorators?

Chapter 3: Python Numpy Library

Lecture 1: Numpy Intro

Lecture 2: Numpy.shape & Numpy.size

Lecture 3: Creating Numpy nd arrays using Numpy functions

Lecture 4: Numpy.unique( ) & Array slicing

Lecture 5: Numpy Calculations and Operators.

Lecture 6: Numpy Aggregations

Lecture 7: Numpy Reshape and Transposing

Lecture 8: Comparing Numpy Arrays.

Lecture 9: Numpy Arrays Images Processing.

Chapter 4: Python Pandas Library

Lecture 1: Installing Jupyter Lab & Pandas

Lecture 2: SQL PostgreSQL Down and install

Lecture 3: Database Creation

Lecture 4: Database Restore

Lecture 5: Using Python Pandas Package to load PostgreSQL the Data Output file

Lecture 6: Fetchmany and Fetchall

Lecture 7: Querying Using Python Panadas

Lecture 8: Pandas methods and functions

Lecture 9: Visualizing Data

Lecture 10: Pandas Data Analysis

Lecture 11: Sampling Error

Lecture 12: How to Scrape a website in Python?

Lecture 13: Scrape a Table inside a Webpage using Pandas and LXML Python Modules!

Lecture 14: Data Visualization of the Scraped Data.

Lecture 15: Save The Scraped Data to a Database.

Chapter 5: Project 1: Using Pandas + Automation to Manage a Business Email List

Lecture 1: Part 1

Lecture 2: Part 2

Lecture 3: Part 3

Chapter 6: Accessing, Manipulating & Filtering DataFrames.

Lecture 1: Data manipulation using DataFrames.

Lecture 2: Accessing Data Using DataFrames.

Lecture 3: Data aggregation and summarization.

Lecture 4: Create New Columns, Drop Unnecessary Ones, and Perform Various Data Manipulation

Lecture 5: Essential Techniques for Peeking at & Describing our Data in Python.

Lecture 6: Filtering Data.

Chapter 7: Data Visualization in Python.

Lecture 1: Introduction to Data Visualization in Python.

Lecture 2: Histograms – a Powerful Tool for Visualizing the Distribution of Data.

Lecture 3: Visualizing Trends using a Real-World Financial Data.

Lecture 4: Determining and Choosing the Appropriate Plot Type.

Chapter 8: Time Series Data Analysis using Python.

Lecture 1: Datasets used in this Section.

Lecture 2: Introduction to Time Series Analysis.

Lecture 3: Creating, Converting Datetimes from Strings & Manipulating Datetime Data.

Lecture 4: Accessing Datetime Attributes, Comparing Datetimes, & Making Relative Datetime.

Lecture 5: Understanding Time Series Growth Rates & Comparing Stock Prices with a Benchmark

Lecture 6: Changing Time Series Frequency By Up-Sampling & Interpolation.

Lecture 7: Changing Time Series Frequency By Down-Sampling.

Lecture 8: Window Functions in Time Series Analysis.

Lecture 9: Stocks Prices Series Analysis with Lags.

Chapter 9: Project 2: Google App Data Analysis.

Lecture 1: Visual Exploring of Google App Store Data.

Lecture 2: Data Cleaning and Preprocessing of Google App Store Data.

Lecture 3: 3. Data Visualization Techniques.

Lecture 4: Statistical Analysis and Hypothesis Testing.

Lecture 5: Data Storytelling.

Lecture 6: Conclusion.

Chapter 10: Introduction to ML & Supervised Learning.

Lecture 1: Introduction to Machine learning with scikit-learn.

Lecture 2: Downloading All The Datasets Used in This Course From Here.

Instructors

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  • 4 stars: 2 votes
  • 5 stars: 19 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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