Free download скачать Introduction To Python Data Structure And Linear Regression
Published 8/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 528.97 MB | Duration: 1h 36m
Manipulating and analyzing python data structure in depth and understanding linear regression with associated concepts.

What you'll learn
Like a master chef crafting a signature dish, you'll learn to cook up python data structures from a recipe of brackets and commas.
Delve into the spellbook of linear regression methods, which contain enchantments like `EDA`, `MISSING DATA ANALYSIS `, NORMALIZATION to manipulate data.
Learn the incantations to safely dealing with python data structures and preventing any unintended alterations to your original creations.
Become adept at performing magical operations such as concatenation, multiplication, and comparison to merge, replicate, and linear model in data analytics
Requirements
Python,IDE, Statistics, Machine Learning
Description
Unleash the power of python data structures and in-depth analysis of linear regression with this immersive and exciting video lesson on the Introduction to python data structure and linear regression! The tutorial covers important concepts and functions that are crucial for manipulating and analyzing data efficiently using Python.This Course has been designed for ease and better clarity to online learners starting from how to work with a python data structure and to perform regression operations on a data frame.From importing and exporting data to and transforming data, this course covers that are crucial for manipulating and analyzing data . We will dive into the essentials of python data structures and regression by demystifying complex concepts through clear and engaging examples.You will also learn how to perform advanced data operations such as merging, grouping, and aggregating data with ease. Unravel step-by-step examples that'll make even complex concepts a breeze to grasp, unfolding the wonders of advanced python Data structures and regression Techniques.From dazzling videos to interactive explanations, this lesson is perfect for student, professional, or data enthusiast seeking clarity on python Data structures and linear regression.Dive into the world of python Data structures with this comprehensive online tutorial, perfect for beginners looking to enhance their data analysis skills. You will positively gain a solid foundation for further exploration in the field and learn how to handle and analyze data efficiently, andBy the end of this course, you will be able to confidently work with python data frames and tackle complex data analysis tasks like a pro.So grab your pencil, sharpen your focus, and get ready to unlock the secrets of this course that will equip you with the necessary knowledge to excel in python and data analysis tasks.
Overview
Section 1: Introduction to list
Lecture 1 Introduction
Lecture 2 List Creation
Lecture 3 Python list overview
Lecture 4 List access with tabulation for clarification
Lecture 5 List Access and it's associated operations
Lecture 6 List useful methods and its operations
Lecture 7 List deletion with del keyword and empty list
Lecture 8 List conversion to tuple,set and dictionary
Section 2: Introduction to tuple
Lecture 9 Introduction
Lecture 10 Tuple creation
Lecture 11 Tuple access
Lecture 12 Iterating through a Tuple
Lecture 13 Changing, Reassigning, and Deleting Tuples
Lecture 14 Tuples vs. Lists
Lecture 15 Tuple method with description
Lecture 16 Tuple use case
Lecture 17 Summary
Section 3: Introduction to Set
Lecture 18 Introduction
Lecture 19 Example of set
Lecture 20 Set Operations
Lecture 21 Set Creation
Lecture 22 Set Example
Lecture 23 Frozen Set
Lecture 24 Frozen set with detailed information
Lecture 25 Frozen set usage
Lecture 26 Summary
Section 4: Dictionary
Lecture 27 Introduction
Lecture 28 Dictionary structure and examples
Lecture 29 Accessing Elements
Lecture 30 Accessing elements using get
Lecture 31 Adding and Modifying Entries to a Dictionary
Lecture 32 Removing or Deleting Elements from a Dictionary
Lecture 33 Delete an Dictionary
Lecture 34 Setdefault() method
Lecture 35 Dictionary copy method
Lecture 36 Dictionary fromkeys method
Lecture 37 Dictionary with example and detailed content
Lecture 38 Summary
Section 5: Python libraries for data analysis
Lecture 39 Introduction
Lecture 40 Python libraries widely used for analysis machine learning and statistics
Lecture 41 Importing libraries
Lecture 42 Loading training data
Lecture 43 Loading testing data
Lecture 44 Removing Unnecessary Attributes
Lecture 45 Correlation check
Lecture 46 Multicollinearity
Lecture 47 Outliers detection
Lecture 48 Data Preprocessing
Lecture 49 Delete Unuseful Features
Lecture 50 Fix Datatype
Lecture 51 Check Mising Values
Lecture 52 Imputing missing values
Lecture 53 Feature Engineering
Lecture 54 Numeric Feature Scaling
Lecture 55 Skewing with boxcox_normmax()
Lecture 56 Adding New Features
Lecture 57 Numerical and categorical features
Lecture 58 Encoding Categorical Variables
Lecture 59 One Hot Encoding
Lecture 60 Train and test data set
Lecture 61 Target Variable Analysis
Lecture 62 Target Variable Transformation
Section 6: Modeling
Lecture 63 Cross Validation
Lecture 64 Evaluation Metric
Lecture 65 Linear Models
Whether you're a novice scribe or a seasoned programmer looking to expand your repertoire, this course will have you conjuring lists with the finesse of a Pythonista.
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