Ementorhub

What you'll learn

Job-ready foundational machine learning skills in Python in just 6 weeks, including how to utilizeScikit-learn to build, test, and evaluate models.

How to apply data preparation techniques and manage bias-variance tradeoffs to optimize model performance.

How to implement core machine learning algorithms, including linear regression, decision trees, and SVM, for classification and regression tasks.

How to evaluate model performance using metrics, cross-validation, and hyperparameter tuning to ensure accuracy and reliability.

Skills you'll gain

Regression Analysis

NumPy

Supervised Learning

Predictive Modeling

Classification And Regression Tree (CART)

Scikit Learn (Machine Learning Library)

Dimensionality Reduction

Machine Learning

Jupyter

Python Programming

Statistical Modeling

Matplotlib

Feature Engineering

Unsupervised Learning

There are 6 modules in this course

Throughout the course, you’ll dive into core ML concepts and learn about the iterative nature of model development. With Python libraries like Scikit-learn, you’ll gain hands-on experience with tools used for real-world applications. Plus, you’ll build a foundation in statistical methods like linear and logistic regression. You’ll explore supervised learning techniques with libraries such as Matplotlib and Pandas, as well as classification methods like decision trees, KNN, and SVM, covering key concepts like the bias-variance tradeoff. The course also covers unsupervised learning, including clustering and dimensionality reduction. With guidance on model evaluation, tuning techniques, and practical projects in Jupyter Notebooks, you’ll gain the Python skills that power your ML journey. ENROLL TODAY to enhance your resume with in-demand expertise!

Linear and Logistic Regression

Building Supervised Learning Models

Building Unsupervised Learning Models

Evaluating and Validating Machine Learning Models

Final Project and Exam