AI Reseacher and Engineer
What would you create if you could build machines which had a mind of their own? Learn and develop computer programs that can access data and can learn by themselves, with this course.
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. In this course, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.
What you will Learn
- Core technical concepts behind Machine Learning
- Popular packages and tools
- Environment set-up and Git
- ML Algorithms and Models
- ML Engineering, Tools and Projects
- Careers in Machine Learning
About the Course
Machine Learning and Artificial Intelligence is everywhere today;
If you've got some programming or scripting experience, this course will is designed to train you with the techniques used by real data scientists and machine learning practitioners in the tech industry and prepare you for an easier move into this career path. Each topic is explained and demonstrated using Python code which you can experiment with and build upon.
Even if you're new to Python, the course starts with the basic elements of Python. If you've done some programming before, you should be able to pick it up quickly.
The topics in this course cover the Machine learning, AI, and data mining techniques real employers are looking for, including:
- Numpy, Scipy
- Matplotlib and Panda Tutorials
- Model representation
- Cost function
- Gradient descent
- Features and polynomial regression
- Normal equation
- Normal equation non invertibility
- Hypothesis representation
- Decision boundary
- Logical regression
- Model selection and Train:Validation:Test sets
- Diagnosing Bias vs Variance
- Learning curves
We will also jump into and practice few popular algorithms in Machine Learning including -
- Linear regression
- Decision trees
- K Nearest neighbours
- Random Forests
- Building an RF Classifier
- Support Vector Machine
- Implementing SVM in Python
The course has 2 real-time projects - Mini and Major projects, on completion of which you will receive a Certificate. Each project has been designed to be of use for your industrial or final year projects in college and also for your placements or job interviews. With this course you'll get 24/7 support, so if you have any questions you can post them in the Discussions dashboard, and we'll respond to you immediately. Unlike other courses, this course doesn't just teach Machine Learning but also motivates the student and creates a sense of interest for the subject too.
About The Coach
Sai Krishna Rohith
An undergraduate student pursuing Computer Science and Engineering, Rohith is a Teaching Assistant for Data Science and Artificial Intelligence at a reputed University. He is also an AI Researcher at Next Tech lab. Rohith’s technical interests lie in Artificial Intelligence and Machine Learning, which makes him enthusiastic about working on cutting edge technologies and projects on Internet of Things and Block chain technology.
My experience with Unschool has been superb and I learnt a lot from my courses. It was wonderfully taught from the basics. I hope to be a part of more courses at Unschool. Whenever I had a query or was confused, they come to your aid immediately Wish to see Unschool impacting many more students.
Amity University, Jaipur
The content provided by Unshool is quite helpful because the video shows practically along with theoretical information. Also, it is self-paced which helps us complete the training at our own convenience. The topics covered are also quite helpful and tell us about the real-world applications of a particular subject.
Cummins College Of Engineering for Women