Data Science & ML for Python-Python & Data Science Made Easy
Updated on
Course overview
- Provider
- Udemy
- Course type
- Paid course
- Level
- Beginner
- Duration
- 11 hours
- Lessons
- 82 lessons
- Certificate
- Available on completion
- Course author
- Steven Martin
-
- Python & R programming for Structured data/ tables.
- Python in demand packages used by Data Scientist and Machine Learning professionals.
- Basic, Inferential and Advanced Statistics
- Concept of Linear and Logistic Regression implementing with Python code
- Machine Learning (ML) Algorithms concepts with Python code
- ML Algorithms - Support Vector Machine
- Machine Learning Algorithms. - K nearest neighbors
- Practical Application of Data Science and Machine Learning in Healthcare and Real estate Industry
- An approach and outlook a Data Scientist and ML professional should adopt while solving business problems in real life
- Engaging Course with Multiple choice questions for Students towards end of each section for Knowledge tests
- Practical & Comprehensive Assignment with Guidelines explaining challenges faced by DS/ML professional and how to deal with such roadblocks.
Description
This course is for Aspirant Data Scientists, Business/Data Analyst, Machine Learning & AI professionals planning to ignite their career/ enhance Knowledge in niche technologies like Python and R. You will learn with this program:
✓ Basics of Python, marketability and importance
✓ Understanding most of python programming from scratch to handle structured data inclusive of concepts like OOP, Creating python objects like list, tuple, set, dictionary etc; Creating numpy arrays, ,Creating tables/ data frames, wrangling data, creating new columns etc.
✓ Various In demand Python packages are covered like sklearn, sklearn.linear_model etc.; NumPy, pandas, scipy etc.
✓ R packages are discussed to name few of them are dplyr, MASS etc.
✓ Basics of Statistics - Understanding of Measures of Central Tendency, Quartiles, standard deviation, variance etc.
✓ Types of variables
✓ Advanced/ Inferential Statistics - Concept of probability with frequency distribution from scratch, concepts like Normal distribution, Population and sample
✓ Statistical Algorithms to predict price of houses with Linear Regression
✓ Statistical Algorithms to predict patient suffering from Malignant or Benign Cancer with Logistic Regression
✓ Machine learning algorithms like SVM, KNN
✓ Implementation of Machine learning (SVM, KNN) and Statistical Algorithms (Linear/ Logistic Regression) with Python programming code
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