Curriculum
- 3 Sections
- 35 Lessons
- 1 Week
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- MACHINE LEARNING FOR DATA SCIENCE6
- 1.0what is machine Learning and its implementation
- 1.1Why use Pandas for Machine Learning?
- 1.2Exploring Data with Series in PYTHON
- 1.3Explore properties of Series in Pandas Python for Machine Learning
- 1.4Practical Use of Properties in Series in Pandas
- 1.5Mastering Pandas Series: Essential Functions for Data Analysis
- Introduction to DataFrame29
- 2.0Introduction to Dataframe in Pandas for Machine Learning
- 2.1Data Analysis with Pandas: Slicing Columns and Basic DataFrame Commands
- 2.2Data Analysis with Pandas: Creating DataFrames from CSV and Excel Files
- 2.3How to Create Clipboard in dataframe Pandas for Machine learning
- 2.4Sorting in dataframe in Pandas for Machine Learning
- 2.5How to Use Duplicate in Pandas for Machine Learning | Data Cleaning with Pandas”
- 2.6Data Frame Slicing in Pandas on CSV or Excel File | Data Analytics with Python”
- 2.7Mastering WHERE Clause in Pandas (Python) – Filter, Select, and Manipulate Data with Precision!
- 2.8Mastering Group By in Pandas for Data Analysis
- 2.9Concatenate Two Sheets in Excel Using Pandas DataFrame for Data Analysis in Python
- 2.10Mastering Join in Pandas Python: Unlocking Advanced Techniques
- 2.11Efficient Data Manipulation in Pandas: Insert, Update, and Delete Rows in Python
- 2.12Effortless Data Analysis: Pandas DataFrame Column Updates, Inserts, and Deletions
- 2.13Pandas : A Deep Dive into Data Enrichment and Cleaning
- 2.14Unleashing the Power of Data Analytics: Mastering Pandas and Pivot Tables
- 2.15Top and Bottom Analysis: Using Pandas to Find the Highest and Lowest Values in Your Data
- 2.16Pandas Tutorial: DataFrame में Date से Month, Year, और Weekday निकालना सीखें | Hindi
- 2.17Pandas Quiz50 Minutes50 Questions
- 2.18Python Full Course for Data Analytics | Beginner to Advanced | Complete Tutorial in Hindi
- 2.19Machine Learning Tutorial 1: What It Is and Why It Matters
- 2.20Machine Learning Tutorial 2: Supervised Learning vs Unsupervised Learning: Key Differences
- 2.21Machine Learning Tutorial 3 : Understanding Train Data and Test Data
- 2.22Machine Learning Tutorial 4: :Beginner’s Guide to Predictive Modelling: Linear Regression i
- 2.23Machine Learning Tutorial 5: Using Multiple Variables in Machine Learning for Linear Regression
- 2.24Machine Learning Tutorial 6 : -Polynomial Regression Explained with Python
- 2.25Machine Learning Tutorial 7: Master Logistic Regression in Machine Learning 📈
- 2.26Machine Learning Tutorial 8: Logistic Regression for Multi-Class Classification Easy Explanation
- 2.27🔍 Machine Learning Tutorial 9: Understanding Decision Trees| Classification Made Simple 🌳
- 2.28Machine Learning Tutorial 10 : Random Forest Classification Explained |
- End to End Project in Pandas1
Instructor
