Data Science using Python (Module 1/3)
Learn Data science / Machine Learning using Python (Scikit Learn)
Are you completely new to Data science?
Have you been hearing these buzz words like Machine learning, Data Science, Data Scientist, Text analytics, Statistics and don't know what this is?
Do you want to start or switch career to Data Science and analytics?
If yes, then I have a new course for you. In this course, I cover the absolute basics of Data Science and Machine learning. I spend some time walking you through different career areas in the Business Intelligence Stack, where does Data Science fit in, What is Data Science and what are the tools you will need to get started. I will be using Python and Scikit-Learn Package in this course. I am not assuming any prior knowledge in this area. I have given some reading materials, which will help you solidify the concepts that are discussed in this lectures.
This course will the first data science course in a series of courses. Consider this course as a 101 level course, where I don't go too much deep into any particular statistical area, but rather just cover enough to raise your curiosity in the field of Data Science and Analytics.
What will you gain from this course:
1. Thorough understanding of the Business Intelligence stack.
2. Good understanding of Data Science and some Machine Learning algorithms.
3. Using Python (Scikit-Learn) for Data Science algorithms.
4. Build or switch careers in Data Science and Machine Learning.
How Frequently is the course Update:
I will be updating the course every 1-2 weeks.
What are the topics covered:
1. Basics of Machine learning
2. Python and Scikit-Learn installation and usage
3. Supervised and Unsupervised learning
4. The concept of training a machine learning model.
5. Techniques to clean input data and choosing the right machine learning algorithm
6. Pandas, Seaborn and Scikit-learn concepts.
7. Evaluate the best machine learning models for your input data.
8. Next Steps in Machine learning.
What do I need to get started?
The course is free! I use a windows machine in my demo. You can work on a windows or MAC.
I am a coder, manager, educator and a gamer. I love data and analytics. In my day job, I work with database technologies including SQL , Big Data and Tableau. I am passionate about technologies and love coding and managing teams. In my spare time I like to teach Big Data analytics, Databases, Programming etc. I am currently working on certain machine learning and Data Science projects and love to explore more in the Statistics field.
StartWhat is Data Science and Machine Learning? (9:32)
StartGetting Started with Python and Scikit-Learn (5:06)
StartTypes of Machine learning algorithms (8:53)
StartPlaying Around with Anaconda and Jupyter (8:49)
StartPlaying with some Python Code (10:44)
StartFitting a Machine Learning Model (KNN Algorithm) - Part 1 (13:36)
StartFitting a Machine Learning Model (KNN Algorithm) Part 2 (20:27)
StartFitting a Machine Learning Model (Logistic Regression Algorithm) (14:54)
StartValidation using Model Selection (Train and Test) (23:40)
StartFinalizing Your optimum algorithm (K-Fold Cross Validation) (19:53)