Python for Machine Learning & Data Science Masterclass
4.7 /5- Offered byUDEMY
Python for Machine Learning & Data Science Masterclass at UDEMY Overview
Duration | 44 hours |
Total fee | ₹380 |
Mode of learning | Online |
Credential | Certificate |
Python for Machine Learning & Data Science Masterclass at UDEMY Highlights
- Earn a Certificate of completion from Udemy
- Get a 30 days money back guarantee on the course
- Get full lifetime access of the course material
- Learn from 33 downloadable resource and 6 articles
Python for Machine Learning & Data Science Masterclass at UDEMY Course details
- For Beginner Python developers curious about Machine Learning and Data Science with Python
- Master critical data science skills
- Understand Machine Learning from top to bottom
- Replicate real-world situations and data reports
- Learn NumPy for numerical processing with Python
- Conduct feature engineering on real world case studies
- Learn Pandas for data manipulation with Python
- Create supervised machine learning algorithms to predict classes
- Learn Matplotlib to create fully customized data visualizations with Python
- This is the most complete course online for learning about Python, Data Science, and Machine Learning
- This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Data Science and Machine Learning
- Cover everything you need to know for the full data science and machine learning tech stack required at the world's top companies
- This course is balanced between practical real world case studies and mathematical theory behind the machine learning algorithms
Python for Machine Learning & Data Science Masterclass at UDEMY Curriculum
Introduction to the course
Anaconda Python and Jupyter Install and Setup
Environment Setup
Python Crash Course
Python Crash Course - Part One
Python Crash Course - Part Two
Python Crash Course - Part Three
Python Crash Course - Exercise Questions
Python Crash Course - Exercise Solutions
Machine learning pathway overview
Machine learning pathway
NumPy
Introduction to NumPy
NumPy Arrays
NumPy Indexing and Selection
NumPy Operations
NumPy Exercises
Numpy Exercises - Solutions
Pandas
Introduction to Pandas
Series - Part One
Series - Part Two
DataFrames - Part One - Creating a DataFrame
DataFrames - Part Two - Basic Properties
DataFrames - Part Three - Working with Columns
DataFrames - Part Four - Working with Rows
Pandas - Conditional Filtering
Pandas - Useful Methods - Apply on Single Column
Pandas - Useful Methods - Apply on Multiple Columns
Pandas - Useful Methods - Statistical Information and Sorting
Missing Data - Overview
Missing Data - Pandas Operations
GroupBy Operations - Part One
GroupBy Operations - Part Two - MultiIndex
Combining DataFrames - Concatenation
Combining DataFrames - Inner Merge
Combining DataFrames - Left and Right Merge
Combining DataFrames - Outer Merge
Pandas - Text Methods for String Data
Pandas - Time Methods for Date and Time Data
Pandas Input and Output - CSV Files
Pandas Input and Output - HTML Tables
Pandas Input and Output - Excel Files
Pandas Input and Output - SQL Databases
Pandas Pivot Tables
Pandas Project Exercise Overview
Pandas Project Exercise Solutions
Matplotlib
Introduction to Matplotlib
Matplotlib Basics
Matplotlib - Understanding the Figure Object
Matplotlib - Implementing Figures and Axes
Matplotlib - Figure Parameters
Matplotlib Styling - Legends
Matplotlib Styling - Colors and Styles
Advanced Matplotlib Commands (Optional)
Matplotlib Exercise Questions Overview
Matplotlib Exercise Questions - Solutions
Seaborn data visualizations
Introduction to Seaborn
Scatterplots with Seaborn
Distribution Plots - Part One - Understanding Plot Types
Distribution Plots - Part Two - Coding with Seaborn
Categorical Plots - Statistics within Categories - Understanding Plot Types
Categorical Plots - Statistics within Categories - Coding with Seaborn
Categorical Plots - Distributions within Categories - Understanding Plot Types
Categorical Plots - Distributions within Categories - Coding with Seaborn
Seaborn - Comparison Plots - Understanding the Plot Types
Seaborn - Comparison Plots - Coding with Seaborn
Seaborn Grid Plots
Seaborn - Matrix Plots
Seaborn Plot Exercises Overview
Seaborn Plot Exercises Solutions
Data analysis and visualization capstone project exercise
Capstone Project Overview
Capstone Project Solutions - Part One
Capstone Project Solutions - Part Two
Capstone Project Solutions - Part Three
Machine learning concepts overview
Introduction to Machine Learning Overview Section
Why Machine Learning?
Types of Machine Learning Algorithms
Supervised Machine Learning Process
Companion Book - Introduction to Statistical Learning
Linear regression
Introduction to Linear Regression Section
Linear Regression - Algorithm History
Linear Regression - Understanding Ordinary Least Squares
Linear Regression - Cost Functions
Linear Regression - Gradient Descent
Python coding Simple Linear Regression
Overview of Scikit-Learn and Python
Linear Regression - Scikit-Learn Train Test Split
Linear Regression - Scikit-Learn Performance Evaluation - Regression
Linear Regression - Residual Plots
Linear Regression - Model Deployment and Coefficient Interpretation
Polynomial Regression - Theory and Motivation
Polynomial Regression - Creating Polynomial Features
Polynomial Regression - Training and Evaluation
Bias Variance Trade-Off
Polynomial Regression - Choosing Degree of Polynomial
Polynomial Regression - Model Deployment
Regularization Overview
Feature Scaling
Introduction to Cross Validation
Regularization Data Setup
L2 Regularization - Ridge Regression Theory
L2 Regularization - Ridge Regression - Python Implementation
L1 Regularization - Lasso Regression - Background and Implementation
L1 and L2 Regularization - Elastic Net
Linear Regression Project - Data Overview
Python for Machine Learning & Data Science Masterclass at UDEMY Faculty details
Python for Machine Learning & Data Science Masterclass at UDEMY Entry Requirements
- Prerequisite: Basic Python Knowledge (capable of functions)
- Not mentioned
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