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Mastering Python – Machine Learning with Data Science

Mastering Python – Machine Learning with Data Science

Python is a popular data science programming language because of its simple syntax and intuitive features. This also makes it the perfect choice for beginner programmers. It offers a host of robust tools and libraries that make it easy to process data and produce business intelligence. The students will learn to use various standard libraries in the Python ecosystem such as Pandas, NumPy, Matplotlib, Scikit- to tackle different stages of a data science project such as extracting data, cleaning and processing data, building and evaluating machine learning models.

Key Learnings

Python Fundamentals for Data Science: Begin with a strong grasp of Python's syntax, data structures, and libraries essential for data science, such as NumPy, Pandas, and Matplotlib.

Exploratory Data Analysis (EDA): Learn to explore and visualize data to uncover hidden patterns and insights, a critical first step in any data science project.

Statistical Foundations: Understand the statistical concepts that underpin machine learning, including probability distributions, hypothesis testing, and regression analysis.

Machine Learning Algorithms::  Dive into a wide range of machine learning algorithms, from simple linear regression to complex neural networks, and learn how to implement them using Python's powerful libraries like scikit-learn and TensorFlow.

Career Path:

Data Scientist:

Machine Learning Engineer:

AI Specialist/Consultant:

Data Analyst:

Python Developer 


 

99.99 Rs.
99.99 PKR 99.99 Rs.
99.99 Rs.
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Last Update 08/20/2024
Completion Time 20 hours
Members 1
Python Data Science Machine Learning Machine Learning with Data Science
    • The “with” Contect Manager
  • Module1: Python Fundamentals
    8Lessons · 3 hr 40 min
    • Introduction to Python,
    • Variables, Strings, Numbers
    • Math Operators, Built-in functions
    • Lists, List Indexing and Slicing, List Methods
    • Tuples, Dictionaries
    • User Input, Conditions
    • Custom Functions
    • Working with Files, Processing the content of a file
  • Module2: Working with Loops and Packages
    9Lessons · 3 hr 45 min
    • For Loop, While Loop
    • String Formatting
    • Syntax Errors
    • Modules, Libraries and Packages
    • Runtime Errors
    • Installing Python Packages
    • Working with Date and Time objects
    • Exception Handling in Python
    • Getting Started with Pandas
  • Module3: Dealing with Programming Errors
    4Lessons · 3 hr
    • Loading Data in Python from CSV, Excel, TXT and JSON Files
    • Indexing and Slicing Dataframes
    • Dropping Dataframe Columns and Rows
    • Updating and Adding new Columns and Rows
  • Module4: Python – Machine Learning & Data Science – I
    5Lessons · 2 hr 45 min
    • NumPy with Python
    • Using pandas Data Frames to solve complex tasks
    • Use pandas to handle Excel Files
    • Web scraping with python
    • Connect Python to MongoDB
  • Module 5: Python – Machine Learning & Data Science – II
    2Lessons · 1 hr 40 min
    • Use matplotlib and seaborn for data visualizations
    • Use plotly for interactive visualizations
  • Module6:Python – Machine Learning & Data Science – III
    1Lessons · 1 hr 40 min
    • Python Machine Learning with SciKit Learn, including Linear Regression
  • Module7: Python – Machine Learning & Data Science -IV
    5Lessons · 3 hr 30 min
    • Spot key features and advantages of NoSQL database MongoDB over RDBMS databases
    • Installing MongoDB
    • Design MongoDB database from start to finish
    • Spot key features and advantages of NoSQL database MongoDB over RDBMS databases
    • Differentiate between RDBMS and NoSQL databases