Machine Learning Zero to Hero

Machine Learning Zero to Hero


Please register to enroll for this course.

18% GST Extra

SKU: cid_89063 Categories: , Tag:
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.

Learning Outcomes

After completing this course, you will be able to:

  • Understand the fundamentals of Machine Learning
  • Choose the correct model for your data
  • Use different clustering models
  • Use an association rules model with Apriori
  • Boost your hireability through innovative and independent learning.
  • Get a certificate on successful completion of the course.
Target Audience

The course can be taken by:

Students: All students who are pursuing professional graduate/post-graduate courses related to computer science or Information Technology.

Teachers/Faculties: All computer science and engineering teachers/faculties.

Professionals: All IT professionals, who wish to acquire new skills or improve their existing skills.

Course Features
  • 24X7 Access: You can view lectures at 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 to have a go at the Machine learning course if you want to be a highly demanded ML professional.

Test & Evaluation

1. During the program, the participants will have to take all the assignments given to them for better learning.

2. At the end of the program, a final assessment will be conducted.


1. All successful participants will be provided with a certificate of completion.

2. Students who do not complete the course / leave it midway will not be awarded any certificate.

No prerequisites

Topics to be covered

Regression Models:

  1. Linear Regression
  2. Multiple Linear Regression
  3. Logistic Regression Part 1
  4. Logistic Regression Part 2

Clustering Models:

  1. K Means Method
  2. Hierarchical Methods

Dimension Reduction Methods:

  1. Explanatory Factor Analysis
  2. Principal Component Analysis

Other Popular Machine Learning Methods:

  1. Association Rules Models With Apriori