Welcome to this in-depth video on Data Science and Machine Learning. If you’re curious about how these technologies are shaping the future, transforming industries, and opening up high-paying career paths, then this video is for you. We’ll explore the fundamentals of data science, break down the different types of machine learning, and explain how these concepts are applied in real-world scenarios such as healthcare, finance, and marketing. You’ll also learn about essential skills and tools like Python, Pandas, TensorFlow, and more that are crucial for becoming a successful data scientist or machine learning engineer. Whether you’re a complete beginner or someone looking to shift into the tech industry, this video will give you a clear direction and help you take the first steps toward a rewarding career. Don’t forget to subscribe and turn on the notification bell to stay updated with our latest tech content.
Course Content
Data Science And Machine Learning
Class 1: What is data science
00:00Class 2: Machine learning overview
00:00Class 3: Data science and machine learning marketplace
00:00Class 4: Dsand ml job opportunities
00:00Class 5: Data science job roles
00:00Class 6: Getting started with r
00:00Class 7: R basics
00:00Class 8: R files
00:00Class 9: R tidyverse
00:00Class 10: R Studio
00:00Class 11: R Resources
00:00Class 12: Data types and structures section intro
00:00Class 13: Basic types
00:00Class 14: Vectors part one
00:00Class 15: Vectors part two Copy
00:00Class 16: Vectors missing values
00:00Class 17: Vectors coercion
00:00Class 18: Vectors naming
00:00Class 19: Vectors misc
00:00Class 20: Creating matrices Copy
00:00Class 21: Working with lists
00:00Class 22: Introduction to data frames
00:00Class 23: Creating data frames
00:00Class 24: Data frames helper functions
00:00Class 25: Data frames tibbles Copy
00:00Class 26: Intermediate r section intro
00:00Class 27: Relational operators
00:00Class 28: Logical operators
00:00Class 29: Conditional statements
00:00Class 30: Loops
00:00Class 31: Functions
00:00Class 32: Packages Copy
00:00Class 33: Factors
00:00Class 34: Dates and times
00:00Class 35: Functional programming
00:00Class 36: Data importexport
00:00Class 37: Databases
00:00Class 38: Data manipulation in r section intro
00:00Class 39: Tidy data
00:00Class 40: The pipe operator
00:00Class 41: The filter verb
00:00Class 42: The select verb
00:00Class 43: The mutate verb
00:00Class 44: The arrange verb
00:00Class 45: The summarize verb
00:00Class 46: Data pivoting
00:00Class 47: String manipulation
00:00Class 48: Web scraping
00:00Class 49: Json parsing
00:00Class 50: Data visualization in r intro
00:00Class 51: Aesthetics mappings
00:00Class 52: Single variable plots
00:00Class 52: Single variable plots
00:00Class 53: Two variable plots
00:00Class 54: Fcets layering and coordinate systems
00:00Class 55: Styling and saving
00:00Class 56: Intro to r markdown
00:00Class 57: Intro to r shiny
00:00Class 58: A basic webapp
00:00Class 59: Other webapp examples
00:00Class 60: Intro to machine learning Part 1
00:00Class 61: Intro to machine learning Part 2
00:00Class 62: Data preprocessing Intro
00:00Class 63: Data preprocessing
00:00Class 64: Linear regression a simple model Intro
00:00Class 65: Linear regression a simple model
00:00Class 66: Exploratory data analysis intro
00:00Class 67: Hands on exploratory data analysis
00:00Class 68: Linear regression a real model intro
00:00Class 69: Llinear regression a real model
00:00Class 70: Logistic regression intro
00:00Class 71: Logistic regression in r
00:00Class 72: Starting a data science career
00:00Class 73: Creating a data science resume
00:00Class 74: Getting started with freelancing
00:00Data Science And Machine Learning
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