Complete Python for Data Science & Machine Learning from A-Z

 Complete Python for Data Science & Machine Learning from A-Z

Python with Machine Learning & Data Science, Data Visulation, Numpy & Pandas for Data Analysis, Kaggle projects from A-Z

You can check out this course on Udemy called “Complete Machine Learning & Data Science with Python | A-Z” by Oak Academy Team1. It covers Scikit, NumPy, Pandas, Matplotlib, Seaborn and dives into machine learning A-Z with Python and Data Science. The course has a rating of 4.6 out of 5 from 190 reviews and is 8.5 hours long with 64 lectures1. Another course you might be interested in is “Complete Data Science & Machine Learning A-Z with Python” which covers Kaggle, statistics, r, python data science, deep learning, python programming, django, machine learning a-z, data scientist and python for data science2. This course has a rating of 4.5 out of 5 from 190 reviews and is 21 hours long with 196 lectures2. Finally, there’s “Learn Python for Data Science & Machine Learning from A-Z” by Juan E. Galvan and Ahmed Wael which covers NumPy, Pandas, Machine Learning and more3. This course has a rating of 4.2 out of 5 from 1690 reviews and is 23 hours long with 140 lectures

What you'll learn

• Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks.
• Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames.
• Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
• Pandas Pyhon aims to be the fundamental high-level building block for doing practical, real world data analysis in Python
• Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices.
• NumPy aims to provide an array object that is up to 50x faster than traditional Python lists.
• NumPy brings the computational power of languages like C and Fortran to Python.
• Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries.
• Learn Machine Learning with Hands-On Examples
• What is Machine Learning?
• Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective.
• Python is a general-purpose, object-oriented, high-level programming language.
• Python is a multi-paradigm language, which means that it supports many programming approaches. Along with procedural and functional programming styles
• Python is a computer programming language often used to build websites and software, automate tasks, and conduct data analysis.
• Whether you work in artificial intelligence or finance or are pursuing a career in web development or data science, Python is one of the most important skills
• Its simple syntax and readability makes Python perfect for Flask, Django, data science, and machine learning.
• Installing Anaconda Distribution for Windows
• Installing Anaconda Distribution for MacOs
• Installing Anaconda Distribution for Linux
• Reviewing The Jupyter Notebook
• Reviewing The Jupyter Lab
• Python Introduction
• First Step to Coding
• Using Quotation Marks in Python Coding
• How Should the Coding Form and Style Be (Pep8)
• Introduction to Basic Data Structures in Python
• Performing Assignment to Variables
• Performing Complex Assignment to Variables
• Type Conversion
• Arithmetic Operations in Python
• Examining the Print Function in Depth
• Escape Sequence Operations
• Boolean Logic Expressions
• Order Of Operations In Boolean Operators
• Practice with Python
• Examining Strings Specifically
• Accessing Length Information (Len Method)
• Search Method In Strings Startswith(), Endswith()
• Character Change Method In Strings Replace()
• Spelling Substitution Methods in String
• Character Clipping Methods in String
• Indexing and Slicing Character String
• Complex Indexing and Slicing Operations
• String Formatting with Arithmetic Operations
• String Formatting With % Operator
• String Formatting With String Format Method
• String Formatting With f-string Method
• Creation of List
• Reaching List Elements – Indexing and Slicing
• Adding & Modifying & Deleting Elements of List
• Adding and Deleting by Methods
• Adding and Deleting by Index
• Other List Methods
• Creation of Tuple
• Reaching Tuple Elements Indexing And Slicing
• Creation of Dictionary
• Reaching Dictionary Elements
• Adding & Changing & Deleting Elements in Dictionary
• Dictionary Methods
• Creation of Set
• Adding & Removing Elements Methods in Sets
• Difference Operation Methods In Sets
• Intersection & Union Methods In Sets
• Asking Questions to Sets with Methods
• Comparison Operators
• Structure of “if” Statements
• Structure of “if-else” Statements
• Structure of “if-elif-else” Statements
• Structure of Nested “if-elif-else” Statements
• Coordinated Programming with “IF” and “INPUT”
• Ternary Condition
• For Loop in Python
• For Loop in Python(Reinforcing the Topic)
• Using Conditional Expressions and For Loop Together
• Continue Command
• Break Command
• List Comprehension
• While Loop in Python
• While Loops in Python Reinforcing the Topic
• Getting know to the Functions
• How to Write Function
• Return Expression in Functions
• Writing Functions with Multiple Argument
• Writing Docstring in Functions
• Using Functions and Conditional Expressions Together
• Arguments and Parameters
• High Level Operations with Arguments
• all(), any() Functions
• map() Function
• filter() Function
• zip() Function
• enumerate() Function
• max(), min() Functions
• sum() Function
• round() Function
• Lambda Function
• Local and Global Variables
• Features of Class
• Instantiation of Class
• Attribute of Instantiation
• Write Function in the Class
• Inheritance Structure

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