• Introduction to Machine
• What is Machine Learning
• Types of Machine Learning
• Data Pre-processing: Understanding the data
• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• Support Vector Machines (SVM)
• K-Nearest Neighbors (KNN)
• Naive Bayes
• Clustering
• Dimensionality Reduction
• Neural
• Deep Learning
• Model Selection and Ensemble

You will work on a variety of practical projects throughout the course to put the concepts you have studied into practice. Additionally, you’ll have access to a helpful network of professors and classmates who can provide you advice and answer your concerns.

This course gives you a thorough introduction to the topic of machine learning and gives you the knowledge and abilities you need to seek a fulfilling career in this fascinating and quickly expanding industry. This course gives you the groundwork you need to succeed whether you want to launch a new profession or develop your present one.

1. Data Scientist: Use machine learning techniques to analyse complex data sets and extract insights to support business decisions.
2. Machine Learning Engineer: Design, develop, and deploy machine learning models and algorithms to solve real-world problems.
3. Artificial Intelligence Engineer:
4. business decisions, develop predictive models, and implement machine learning algorithms.
5. Business Intelligence Analyst
6. Research Scientist:
7. Computer Vision Engineer:
8. Natural Language Processing Engineer.
9. Recommender Systems Engineer: