Keep learning your way forward | Get courses from ₹ 700 now


Machine Learning Course Content

File size: 114 kb

course overview:

Machine Learning course is curated and developed by leading faculty and industry leaders with customized specializations. The course will nurture and transform you into a highly-skilled professional with an in-depth knowledge of various algorithms and techniques, such as regression, classification, supervised and unsupervised learning, Natural Language Processing, etc.

Overall, a machine learning course should cover the basics of supervised and unsupervised learning, deep learning, and various techniques used to evaluate models and reduce dimensionality. Additionally, a comprehensive course should also cover the ethical considerations involved in machine learning.

What are the prerequisites for learning machine learning ?

  • Anybody can take up this online training course and get trained in machine learning.
  • ML Basics would be the ideal

About Techehost 

Techehost is designed to provide high-quality, flexible and accessible learning opportunities to help you develop the skills you need to advance your career or pursue your personal interests. We offer a range of courses, taught by industry experts, that cover a wide variety of topics and are designed to fit your schedule and learning style. Our online learning environment is user-friendly and allows you to interact with instructors and other learners in real-time, enabling you to get the support you need to succeed. Whether you’re looking to learn a new skill, enhance your knowledge or take your career to the next level, our online training website is the perfect place to start.



Introduction to Machine Learning

This section provides an overview of what machine learning is, its importance, and various applications of machine learning.

Linear Regression

Linear regression is a simple but powerful machine learning algorithm used for predicting numerical outcomes. This section covers the basic concepts of linear regression, how to build a model, and how to interpret the results.


Classification is a supervised learning technique that is used to classify data into different categories. This section covers popular algorithms such as logistic regression, decision trees, and support vector machines.


Clustering is an unsupervised learning technique used to group data points into clusters based on similarity. This section covers popular algorithms such as k-means and hierarchical clustering.

Neural Networks

Neural networks are a powerful machine learning technique used for complex tasks such as image recognition, natural language processing, and speech recognition. This section covers the basics of neural networks, different types of neural networks such as convolutional neural networks and recurrent neural networks, and how to train them.

Model Evaluation and Validation

This section covers different techniques used to evaluate machine learning models such as cross-validation, precision, recall, and F1 score.

Dimensionality Reduction

Dimensionality reduction is a technique used to reduce the number of features in a dataset while retaining its essence. This section covers popular techniques such as Principal Component Analysis (PCA) and t-SNE

Reinforcement Learning

Reinforcement learning is a type of machine learning where an agent learns to take actions in an environment to maximize a reward. This section covers the basic concepts of reinforcement learning, popular algorithms such as Q-learning and SARSA, and how to implement them.

Ethics in Machine Learning

This section covers the ethical considerations involved in machine learning such as privacy, bias, and fairness.

Be the first to add a review.

Please, login to leave a review
Get course
Enrolled: 0 students
Duration: 25 hrs
Lectures: 9
Video: 25
Level: Advanced
₹20,000 ₹15,000

Shopping Cart

Cart is empty!