Data Science & Machine Learning

Course details

Data science is transforming industries, but many feel overwhelmed by the sheer complexity of algorithms and machine learning models. Without a clear path, even the most motivated learners can struggle to get started. Imagine confidently handling machine learning models, data preparation, and algorithm evaluations using Data Science & Machine Learning techniques. This course bridges the gap between confusion and confidence, providing a structured, step-by-step introduction to Data Science & Machine Learning. Through foundational Python libraries and advanced evaluation methods, you’ll develop the skills to understand and work with complex data sets. With this course, you’ll gain the expertise to tackle data science challenges, positioning yourself for success in a rapidly growing field.

Learning Outcomes

• Understand the fundamentals of Data Science & Machine Learning.
• Learn how to prepare, clean, and manipulate datasets.
• Master key Python libraries such as NumPy, Pandas, and Matplotlib.
• Develop skills in algorithm evaluation and selection.
• Gain expertise in feature selection and data preparation.
• Explore ensemble techniques for performance improvement.

Course Curriculum

• Module 01: Course Overview & Table of Contents
• Module 02: Introduction to Machine Learning - Part 1 - Concepts, Definitions, and Types
• Module 03: Introduction to Machine Learning - Part 2 - Classifications and Applications
• Module 04: System and Environment Preparation - Part 1
• Module 05: System and Environment Preparation - Part 2
• Module 06: Learn Basics of Python - Assignment
• Module 07: Learn Basics of Python - Assignment
• Module 08: Learn Basics of Python - Functions
• Module 09: Learn Basics of Python - Data Structures
• Module 10: Learn Basics of NumPy - NumPy Array
• Module 11: Learn Basics of NumPy - NumPy Data
• Module 12: Learn Basics of NumPy - NumPy Arithmetic
• Module 13: Learn Basics of Matplotlib
• Module 14: Learn Basics of Pandas - Part 1
• Module 15: Learn Basics of Pandas - Part 2
• Module 16: Understanding the CSV Data File
• Module 17: Load and Read CSV Data File Using Python Standard Library
• Module 18: Load and Read CSV Data File Using NumPy
• Module 19: Load and Read CSV Data File Using Pandas
• Module 20: Dataset Summary - Peek, Dimensions, and Data Types
• Module 21: Dataset Summary - Class Distribution and Data Summary
• Module 22: Dataset Summary - Explaining Correlation
• Module 23: Dataset Summary - Explaining Skewness - Gaussian and Normal Curve
• Module 24: Dataset Visualization - Using Histograms
• Module 25: Dataset Visualization - Using Density Plots
• Module 26: Dataset Visualization - Box and Whisker Plots
• Module 27: Multivariate Dataset Visualization - Correlation Plots
• Module 28: Multivariate Dataset Visualization - Scatter Plots
• Module 29: Data Preparation (Pre-Processing) - Introduction
• Module 30: Data Preparation - Re-scaling Data - Part 1
• Module 31: Data Preparation - Re-scaling Data - Part 2
• Module 32: Data Preparation - Standardizing Data - Part 1
• Module 33: Data Preparation - Standardizing Data - Part 2
• Module 34: Data Preparation - Normalizing Data
• Module 35: Data Preparation - Binarizing Data
• Module 36: Feature Selection - Introduction
• Module 37: Feature Selection - Uni-variate Part 1 - Chi-Squared Test
• Module 38: Feature Selection - Uni-variate Part 2 - Chi-Squared Test
• Module 39: Feature Selection - Recursive Feature Elimination

……...And more. Updated on 14 October, 2024