Data Science & Machine Learning, Python
In this hands-on Udemy course, Data Science and Machine Learning with Python, you will learn how to become a data scientist in the current technology industry. This will be achieved through studying data mining and machine learning course with the popular programs of Python and Apache Spark. If you’re a programmer, software developer, or a data analyst interested in moving to the tech industry, then this course might be worth to consider!
While certainly all skill levels are welcome to participate, students should have some previous coding or scripting experience, and at the minimum high school math skills. Those without prior coding experience should not initially take this course, but take a basic Python one first. A desktop computer compatible with Enthought Canopy 1.6.2 or newer is also required.
The entirety of the course is 9 hours long with 71 lectures. Each section is taught through engaging videos that challenge you to understand the tech industry, and learn techniques that will land you a career as a data scientist.
There are 10 sections in the course broken down into:
- An overview of the course and what you’ll need to succeed.
- Downloading and running Python
2. Statistics & Probability Refresher, Python Practice
- Types of data and distribution including: continuous, discrete numerical, categorical, and ordinal data
- Mean, median, mode, variation, and standard deviation in Python
- Introduction to probability and density mass functions
- Percentiles, correlation, and covariance
- Bayes’ Theorem with applicable usage
3. Predictive Models
- How both Linear and Polynomial regression work and applying example material to Python
- Multivariate regression to predict value and building it in Python
- Pandas library in Python
4. Machine Learning with Python
- Applying the Bayes’ concept to problem solving scenarios
- Supervised and unsupervised machine learning
- Recognizing similar items with K-Means and clustering
- Measuring disorder with entropy
- Using decision trees
5. Recommender Systems
- Recommended using user and item based collaborative filtering
6. Data Mining and Machine Learning Techniques
- K-Nearest-Neighbors (KNN) learning machine
- Using KNN and applying it to real problems
7. Dealing with Real-World Data
- Understanding errors from bias and variance tradeoff
- Data Cleaning and normalization
- Inputting data
8. Apache Spark: Machine Learning on Big Data
- Introducing, installing, and using Apache Spark
9. Experimental Design
- Creating controlled experiments with the A/B, T-Tests, and P-values
- Understanding when and how long to run an experiment
10. You made it!
- Other material to explore
- Bonus video lecture for further courses
Data Science and Machine Learning with Python – Summary, Coupon
After finishing each section you will participate in an exercise to apply your gained knowledge. Activities are also implemented throughout the course, so that you can directly use the learned material in a real-life scenario.
Upon completion of this course you will be well versed in data science and machine learning. You will understand the fundamentals of Data Science and Apache Spark, so that you can effectively deepen and use the knowledge.
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