Ementorhub

What you'll learn

Run Python programs for tasks using numeric operations, control structures, and functions.

Analyze data with NumPy and Pandas for comprehensive data insights.

Evaluate the performance of linear regression and KNN classification models.

Develop optimized machine learning models using gradient descent.

Skills you'll gain

Seaborn

Matplotlib

NumPy

Python Programming

Machine Learning

Data Structures

Regression Analysis

Data Visualization

Classification And Regression Tree (CART)

Linear Algebra

Jupyter

Data Analysis

Supervised Learning

Data Manipulation

Scikit Learn (Machine Learning Library)

Pandas (Python Package)

There are 10 modules in this course

Building on Python fundamentals, you will explore data analysis with NumPy and Pandas. You will learn about array operations in NumPy, manipulating and analyzing data using Pandas, including working with DataFrames, performing data operations, indexing, and merging datasets. These modules are designed to provide you with a strong foundation in data manipulation and analysis, critical for any data science role. The course culminates with an introduction to basic machine learning concepts. You will delve into linear regression, understanding its mathematical foundations and practical applications. Furthermore, you will explore gradient descent, a crucial optimization technique, and KNN classification, one of the simplest machine learning algorithms. Each topic is reinforced with case studies, ensuring you can apply theoretical knowledge to real-world scenarios. This course is ideal for beginners in programming and data science. No prior experience in Python or data analysis is required, but a basic understanding of mathematics will be beneficial.

NumPy

Pandas

Some Fun with Math

Data Visualization

Simple Linear Regression

Gradient Descent

Classification: KNN

Logistic Regression

Advanced Machine Learning Algorithms