Information Technology

Data Science Projects with Python

Instructor-Led 6 Agenda

Description

This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you’ll be able to understand the working and output of these algorithms and gain insight into not only th

Course Overview

Course Objective

By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data.

Who Should Attend

If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful.

Data Exploration and Cleaning Lesson Id 1

Data Exploration and Cleaning

Introduction to Scikit-Learn and Model Evaluation Lesson Id 2

Introduction to Scikit-Learn and Model Evaluation

Details of Logistic Regression and Feature Exploration Lesson Id 3

Details of Logistic Regression and Feature Exploration

The Bias-Variance Trade-off Lesson Id 4

The Bias-Variance Trade-off

Decision Trees and Random Forests Lesson Id 5

Decision Trees and Random Forests

Imputation of Missing Data, Financial Analysis, and Delivery to Client Lesson Id 6

Imputation of Missing Data, Financial Analysis, and Delivery to Client

Lesson Id	Title	Description
1	Data Exploration and Cleaning	Python and the Anaconda Package Management System<br />Different Types of Data Science Problems<br />Loading the Case Study Data with Jupyter and pandas<br />Data Quality Assurance and Exploration<br />Exploring the Financial History Features in the Dataset<br />Activity 1: Exploring Remaining Financial Features in the Dataset
2	Introduction to Scikit-Learn and Model Evaluation	Introduction<br />Model Performance Metrics for Binary Classification<br />Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve
3	Details of Logistic Regression and Feature Exploration	Introduction<br />Examining the Relationships between Features and the Response<br />Univariate Feature Selection: What It Does and Doesn't Do<br />Building Cloud-Native Applications<br />Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients
4	The Bias-Variance Trade-off	Introduction<br />Estimating the Coefficients and Intercepts of Logistic Regression<br />Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters<br />Activity 4: Cross-Validation and Feature Engineering with the Case Study Data
5	Decision Trees and Random Forests	Introduction<br />Decision trees<br />Random Forests: Ensembles of Decision Trees<br />Activity 5: Cross-Validation Grid Search with Random Forest
6	Imputation of Missing Data, Financial Analysis, and Delivery to Client	Introduction<br />Review of Modeling Results<br />Dealing with Missing Data: Imputation Strategies<br />Activity 6: Deriving Financial Insights<br />Final Thoughts on Delivering the Predictive Model to the Client