About the course
This course provides a concise introduction to the fundamental concepts in machine learning and popular machine learning algorithms. The course is accompanied by hands-on problem-solving exercises in Python.
This course covers the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, k-nearest neighbor, an introduction to Bayesian learning and the naïve Bayes algorithm, support vector machines and kernels, and neural networks with an introduction to Deep Learning. It also covers the basic clustering algorithms. Feature reduction methods are also discussed. This course also introduces the basics of computational learning theory. Also, this course covers various issues related to the application of machine learning algorithms. This course also covers hypothesis space, overfitting, bias and variance, tradeoffs between representational power and learnability, evaluation strategies, and cross-validation.
The course can be taken by:
Students: All students who are pursuing professional graduate/post-graduate courses related to computer science and engineering or data science.
Teachers/Faculties: All computer science and engineering teachers/faculties.
Professionals: All working professionals from the computer science / IT / Data Science domain.
- 24X7 Access: You can view lectures as per your own convenience.
- Online lectures: Online lectures with high-quality videos.
- Updated Quality content: Content is the latest and gets updated regularly to meet the current industry demands.
Why learn Machine Learning?
Machine Learning lays the foundation for Artificial Intelligence. Artificial Intelligence (AI) is indeed moving tremendously. Self-driving cars are AI applications, also, Siri on your iPhone as well as Youtube’s video recommendations are AI applications. Machine Learning is the rave of the moment. Tons of companies are going all out to hire competent engineers, as ML is gradually becoming the brain behind business intelligence. Just as humans learn from experience, ML systems learn from data. So, learning ML would make you more knowledgeable in data science, and thus more attractive in the labor market. Also, there’s a potentially positive demand for ML engineers. So, it’s worth learning having a go at the Machine learning course if you want to be a highly demanded ML professional.
Test & Evaluation
Each lecture will have a quiz containing a set of multiple-choice questions. Apart from that, there will be a final test based on multiple-choice questions.
Your evaluation will include the overall scores achieved in each lecture quiz and the final test.
Topics to be covered
- Linear Regression
This is only a demo course. The full course can be bought here
- Multiple Linear Regression
- Logistic Regression Part 1
- Logistic Regression Part 2
- K Means Method
- Hierarchical Methods
Dimension Reduction Methods:
- Explanatory Factor Analysis
- Principal Component Analysis
Other Popular Machine Learning Methods:
- Association Rules Models With Apriori