Description

Duration

2 Days

12 CPD hours

This course is intended for

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.

Overview

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.

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 the predictive capabilities of the models but also their reasons for making these predictions.

Data Exploration and Cleaning

Python and the Anaconda Package Management System

Different Types of Data Science Problems

Loading the Case Study Data with Jupyter and pandas

Data Quality Assurance and Exploration

Exploring the Financial History Features in the Dataset

Activity 1: Exploring Remaining Financial Features in the Dataset

Introduction to Scikit-Learn and Model Evaluation

Introduction

Model Performance Metrics for Binary Classification

Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve

Details of Logistic Regression and Feature Exploration

Introduction

Examining the Relationships between Features and the Response

Univariate Feature Selection: What It Does and Doesn't Do

Building Cloud-Native Applications

Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients

The Bias-Variance Trade-off

Introduction

Estimating the Coefficients and Intercepts of Logistic Regression

Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters

Activity 4: Cross-Validation and Feature Engineering with the Case Study Data

Decision Trees and Random Forests

Introduction

Decision trees

Random Forests: Ensembles of Decision Trees

Activity 5: Cross-Validation Grid Search with Random Forest

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

Introduction

Review of Modeling Results

Dealing with Missing Data: Imputation Strategies

Activity 6: Deriving Financial Insights

Final Thoughts on Delivering the Predictive Model to the Client

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Data Science Projects with Python

By Nexus Human

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