Data Science - Machine Learning

This course provides an introductory overview of data science, its significance in today's data-driven world, and its diverse applications. Students will gain a fundamental understanding of data collection, storage, and analysis.

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Data Science - Machine Learning

Course description

Machine Learning (ML) is revolutionizing the way we make predictions, automate decision-making, and extract valuable insights from data. This compr...

Machine Learning (ML) is revolutionizing the way we make predictions, automate decision-making, and extract valuable insights from data. This comprehensive "Machine Learning Fundamentals" course is designed to provide you with a deep understanding of the core principles, algorithms, and applications of machine learning.

What you’ll learn

This course is suitable for beginners with a basic understanding of programming and mathematics. Whether you are a data enthusiast, a software developer, a business analyst, or anyone interested in leveraging the power of machine learning, this course will equip you with the knowledge and skills to excel in this dynamic field.

Curriculum

Supervised Learning (2 lectures)
1. Linear Regression

2. Logistic regression

Unsupervised Learning (3 lectures)
1. K-Means

2. K-Means ++

3. Hierarchical Clustering

SVM (4 lectures)
1. Support Vectors

2. Hyperplanes

3. 2-D Case

4. Linear Hyperplane

SVM Kernal (3 lectures)
1. Linear

2. Radial

3. polynomial

Other Machine Learning Algorithms (5 lectures)
1. K – Nearest Neighbour

2. Naïve Bayes Classifier

3. Decision Tree – CART

4. Decision Tree – C50

5. Random Forest

Frequently Asked Questions

Frequently Asked Questions

1. What programming language is used in this course??

Information about the programming language used, such as Python, and its importance in machine learning.

2. What is Machine Learning, and how can I start using Python for it??

Introduction to machine learning, its applications, and Python's role in building and evaluating machine learning models.

3. What is the process of training and evaluating a machine learning model in Python??

A step-by-step explanation of the machine learning workflow, including data splitting, model training, hyperparameter tuning, and performance evaluation.

4. What is overfitting in machine learning, and how can it be prevented??

Overfitting occurs when a model learns the training data too well and performs poorly on new data. It can be prevented by using techniques like cross-validation and regularization.

5. What is the role of feature engineering in machine learning??

Feature engineering involves creating new features or modifying existing ones to improve a model's performance and understanding of the data.

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