Published 5/2023
Created by Zaviir Berry
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 49 Lectures ( 3h 4m ) | Size: 1 GB
Learn the basics of python, how to manipulate and visualize data, and how to train and evaluate machine learning models
What you’ll learn
The Basics of Python Programming
How to Work with Datasets
How to Visualize Data
Machine Learning and Statistical Modeling
Data Preprocessing and Feature Engineering
Training and Evaluating Machine Learning Models
Requirements
No programming experience needed. You’ll learn everything you need to know in this course
Description
Welcome to Python Data Analysis: Real World Applications. I am Zaviir Berry, your instructor for this comprehensive course. I hold a degree in Electrical and Computer Engineering from Rochester Institute of Technology where I specialized in artificial intelligence and its applications in analyzing live brain wave data to classify human motor functions. Since graduating in 2021, I have been working as a Software Engineer at a Fortune 100 company.Throughout this course, you will: gain a solid understanding of the basics of Python programminglearn how to work with datasetsvisualize dataperform machine learning and statistical modeling techniquesWe will delve into the essential components of model development, including: data preprocessingfeature engineeringmodel trainingevaluationUpon completion of this course, participants will have acquired the skills necessary to effectively forecast insurance claim amounts and predict financial market trends using advanced machine learning techniques. They will be able to utilize patient characteristics, such as age, gender, Body Mass Index (BMI), and blood pressure, to make accurate predictions of insurance claim amounts. Additionally, they will be able to predict the closing price of the S&P 500 for the next day with a high degree of accuracy. The course also includes a comprehensive data preprocessing component, which enables participants to effectively prepare data for use in various machine learning techniques, including Linear and Logistic Regression. Furthermore, participants will be able to interpret the results of their models through the application of various evaluation metrics, such as accuracy, precision, and recall, which will allow them to make informed decisions based on their predictions.
Who this course is for
Beginner Python developers who are curious about data analysis and machine learning
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