About this course
Course Overview
This course is designed for professionals aspiring to learn the basics of AI using Python and the use of AI technologies to gain insights that will be used to get people throughout the enterprise to run the intelligent solutions that derive a business value.
Course Objectives
- Data Science Project Cycle, its role in the corporate world and understanding the use of python for data science
- Setting up Anaconda open-source distribution, configuring Jupyter Notebook on Local Machine and Adding project to Git and best practices for handling data projects
- Extracting Data from Databases, APIs, and Web Scraping
- Exploring and Processing Data with the concepts of Summary Statistics, Distributions, Crosstabs, Grouping and Pivots using NumPy and Pandas python libraries.
- Data Munging for treating missing value and working with outliers, deriving feature using feature engineering and create and customize compiling visualizations using Matplotlib
- Building and Evaluating Predictive Models, understanding machine learning concepts and using the python scikit-learn library
- Model Tuning with the concepts of underfitting, overfitting, Regularization, Hyperparameter tuning and cross validation
Course Content
- Module 01: Data Science Project Cycle
- Module 02: Running Anaconda and Jupter notebooks
- Module 03: Data Extraction
- Module 04: Exploring and Processing Data
- Module 05:Data Munging
- Module 06: Building and Evaluating Predictive Models
- Module 07: Model Tuning
Course Prerequisites
Before you start proceeding with this course, we assume that you have prior exposure to APIs, database concepts, and python programming language skills.
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