Machine Learning
- Time Duration: 2-3 Months
- Skill Level : Advance Course
- Certificate : Yes
COURSE DESCRIPTION:
Step into one of the most exciting fields in tech with the best Machine Learning course at Unividya. This hands-on course helps you understand how computers learn from data to make predictions and decisions—just like humans. From supervised learning to neural networks, you’ll explore powerful algorithms and real-world applications. By the end, you’ll be able to build, train, and evaluate models using real data and industry-standard tools.
LEARNING OUTCOMES:
Understand supervised, unsupervised, and reinforcement learning techniques
Preprocess data, select features, and optimize model performance
Use libraries like Scikit-learn, TensorFlow, and Keras
Apply algorithms like regression, decision trees, clustering & neural networks on real datasets
Who Can Join?
The Machine Learning course at Unividya is ideal for students, fresh graduates, data enthusiasts, or working professionals eager to break into AI and data science.
If you have a basic understanding of Python and math (like linear algebra and statistics), and a curiosity to solve real-world problems using data—this course is made for you.
What You’ll Learn!
In the Machine Learning course at Unividya, you’ll gain practical skills through real-world projects:
Learn key ML types: supervised, unsupervised & reinforcement learning
Clean, prepare & visualize data for better model performance
Build models using Scikit-learn, TensorFlow & Keras
Apply regression, classification, clustering, and neural networks
Evaluate models and improve them with real datasets
- Time Duration: 2-3 Months
- Skill Level : Advance Course
- Certificate : Yes
COURSE DESCRIPTION
In this course on machine learning, students delve into the fundamentals and applications of algorithms that enable computers to learn from data and make decisions or predictions. The curriculum covers key topics such as supervised and unsupervised learning, reinforcement learning, and neural networks. Students gain hands-on experience with popular tools and frameworks used in the field, allowing them to implement and evaluate machine learning models on real-world datasets. By the end of the course, participants are equipped with the knowledge and skills to tackle complex problems in various domains, from healthcare and finance to marketing and beyond.
LEARNING OUTCOMES
- Understand the core concepts of machine learning, including supervised, unsupervised, and reinforcement learning.
- Gain proficiency in data preprocessing, feature engineering, and model selection to improve algorithm performance.
- Develop skills in using popular machine learning libraries like Scikit-learn, TensorFlow, and Keras for building and evaluating models.
- Learn to apply various algorithms such as linear regression, decision trees, clustering, and neural networks to real-world datasets.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast