Machine Learning Complete Guide: Master Algorithms, AI, and Data Modeling

Dive into the world of machine learning with this comprehensive guide. Learn to build intelligent systems, master algorithms, and create predictive models using real-world data.

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4.6

★★★★★ 4.6/5

610 students

What you'll learn

About Course

This course offers a complete journey into Machine Learning, designed to equip you with the skills to build intelligent, data-driven applications. Whether you’re just starting out or aiming to sharpen your expertise, you’ll gain hands-on experience in everything from fundamental algorithms to advanced models and real-world deployment techniques.

This course includes:

Course Curriculum

1. Introduction & Installation Python Background, Need & Importance

2. Programming Elements Keywords & Variables

3. Conditions & Loops Indentation & Scopes

4. Data Structures

5. Functions & Exception Handling

6. Add On Topics

7. Model Evaluation and Hyperparameter Tuning

Material Includes

Introduction to Machine Learning Algorithms - A comprehensive overview of essential algorithms like linear regression, decision trees, k-nearest neighbors (KNN), and support vector machines (SVM).
Hands-on Projects and Case Studies - Practical exercises using real-world datasets to apply machine learning techniques for solving problems in areas like healthcare, finance, and marketing.
Supervised and Unsupervised Learning - Detailed exploration of both supervised learning techniques (classification, regression) and unsupervised learning (clustering, dimensionality reduction).
Model Evaluation and Optimization - Training on how to assess model performance using metrics like accuracy, precision, recall, and F1 score, and fine-tuning models for better results.
Introduction to Neural Networks - Learn the fundamentals of neural networks and deep learning, with practical examples using libraries such as TensorFlow and Keras.