Course description

The Fundamentals of Machine Learning course is designed for beginners and professionals who want to understand the core concepts of ML, train models, and apply AI solutions to real-world problems.

Machine Learning is transforming industries like healthcare, finance, e-commerce, and robotics, making it one of the most in-demand skills today. This course provides hands-on training in data preprocessing, model selection, training, and evaluation using Python and popular ML libraries (Scikit-Learn, TensorFlow, and PyTorch).

What You’ll Learn:

???? Introduction to Machine Learning – Understand what ML is, its applications, and real-world use cases.
???? Data Preprocessing & Feature Engineering – Learn data cleaning, handling missing values, and feature selection techniques.
???? Supervised Learning – Master linear regression, logistic regression, decision trees, SVMs, and ensemble methods.
???? Unsupervised Learning – Explore clustering algorithms (K-Means, DBSCAN), dimensionality reduction (PCA, t-SNE).
???? Introduction to Neural Networks – Understand the basics of deep learning and artificial neural networks.
???? Model Training & Evaluation – Learn train-test split, cross-validation, confusion matrix, and accuracy metrics.
???? Hands-on ML Projects – Work on real-world datasets to build predictive models.

By the end of this course, you'll have a solid understanding of ML principles and be ready to explore advanced AI techniques.

What will i learn?

• Understand the core concepts of Machine Learning.
• Preprocess data and engineer features for ML models.
• Train and evaluate supervised & unsupervised models.
• Work with Python and ML libraries to build AI applications.
• Kickstart a career in AI, ML, or Data Science.

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