Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
3 Hour(s) 28 Minute(s)
Language
English
Taught by
Aalekh Rai
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
Course Overview
Getting started and building a strong foundation for AI and ML using Python. Go from Hello world to Complete ML projects
[ 90% of the Course has been updated and rest will be updated soon. Enroll Now!!! ]
Artificial Intelligence and Machine Learning doesn't have to be hard and complex if we approach it in right way. Sometimes, we need to have a complete working model to understand the basic concept. And this course is going to deliver you the same.
Course objective:
The sole objective of this course is to get you introduced with AI (Artificial Intelligence) and ML (Machine Learning). All the programs and projects that we are going to develop, are using Python programming language. So, You need Python knowledge.
If you are not familiar with Python programming language, You can take our FREE course on Python. [This free course of Python is also getting updated.]
Learning outcomes:
After completing this course and assignments given to you, you will have:
Multiple programs developed for Machine Learning
Multiple complete projects developed for Machine Learning
A complete ML model developed
For concept seeker in you, you shall be able to answer these questions comfortably:
What is AI and What is ML?
How AI and ML are different but related?
What are DL, NLP, ANN, DNN etc.?
What is Anaconda, Spyder, Jupyter etc. and how and why do we use them for Machine Learning?
What are classifiers and models in ML?
How to develop programs and projects of Machine Learning?
For the developer in you, you shall be comfortable with:
Machine learning development environment with Python.
Course Content
- 11 section(s)
- 53 lecture(s)
- Section 1 Basics of Artificial Intelligence
- Section 2 Basics of Machine Learning
- Section 3 AI applications - A look
- Section 4 Basics of Python3 (Bonus section)
- Section 5 Python libraries for ML
- Section 6 Anaconda development environment
- Section 7 Your first ML Model
- Section 8 Core ML concepts
- Section 9 Model Evaluation Techniques
- Section 10 Your first complete ML project
- Section 11 Knowledge Bytes
What You’ll Learn
- Develop an ML Model using Python, Perform Error Analysis and Make Predictions.
- Develop your first ML Model - 'Hello World' for AI and ML.
- Develop your first 'complete ML project'. Understand basics of Machine Learning.
- Classifiers and Models in ML.
- Learn Supervised, Unsupervised, Regression, Classification, Clustering in ML.
- Learn RMSE method, Confusion matrix, Classification report in ML.
- Top Python libraries for Machine Learning.
- SK-learn library for ML with Python.
- Project 1: Complete ML project of IRIS flower dataset.
- Project 2: Complete ML project of Digit recognition system.
Skills covered in this course
Reviews
-
MMehakpreet Kaur
Experience is very nice
-
MManoj Shashikant Nevase
great learning experience....
-
DDev Chadha
It was very concise and to the point.Very informative.
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MMurali
I believe, in the course Clustering evaluation was not included. Please include clustering evaluation topic as well.
