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Applying Data Science Techniques using Machine Learning with Python (DSP07)


Description
This course equips learners with the knowledge and skills necessary to effectively apply Data Science techniques and Machine Learning (ML) algorithms using Python.

This course provides a concise introduction to Machine Learning fundamentals, covering its applications and types of algorithms. From supervised and unsupervised learning to reinforcement learning and model evaluation, you will gain a solid understanding of ML concepts and their practical use. It also provides hands-on labs to reinforce your learning and apply the skills learned in real-world problem-solving.

This course will teach you to apply the following techniques using ML and Python:
• Data Preprocessing
• Classification
• Regression
• Anomaly Detection
• Association Rules
• Clustering
• Time Series

Prerequisites:
Basic knowledge of any programming language, basic math skills, problem-solving and logical thinking skills, and a basic understanding of SQL

Must have completed the following o9 Academy training:
• Statistics Essentials
• Python Fundamentals
• Data Science Essentials
• Machine Learning and Artificial Intelligence: Essentials
• Mastering Python Programming
• Data Science with Python Programming

Target Audience:
Application Developers, Programmers, Data Scientists, Big Data Professionals, and Anyone Interested in Machine Learning

Assessment:
This course is divided into modules and upon completing each module, you will have the opportunity to demonstrate your understanding and proficiency through an assessment. This assessment serves as a culmination of your learning journey and allows you to apply the knowledge and skills you have acquired throughout the course. Please note, that this is a multiple-choice question assessment with two attempts to pass within a stipulated timeline.

Course Duration: 9 hr 15 mins

Content
  • Program Overview [02:19 mins]
  • Program Overview [02:25 mins]
  • Getting Started [33:05 mins]
  • Machine Learning with Python Part 1 [16:47 mins]
  • Machine Learning with Python Part 2 [06:45 mins]
  • Machine Learning with Python - Assessment [10:00 mins]
  • Data Pre-processing [74:55 mins]
  • Data Pre-processing Part 1 [10:10 mins]
  • Data Pre-processing Part 2 [15:19 mins]
  • Tutorial - Data Preprocessing in Python [40:00 mins]
  • Data Preprocessing - Assessment [10:00 mins]
  • Classification [82:19 mins]
  • Classification Part 1 [14:59 mins]
  • Classification Part 2 [17:20 mins]
  • Tutorial - Classification in Machine Learning [35:00 mins]
  • Classification - Assessment [10:00 mins]
  • Regression [85:44 mins]
  • Regression Part 1 [15:16 mins]
  • Regression Part 2 [10:30 mins]
  • Tutorial - Regression in Machine Learning [45:00 mins]
  • Regression - Assessment [10:00 mins]
  • Implementing Anomaly Detection [77:37 mins]
  • Implementing Anomaly Detection [12:37 mins]
  • Tutorial - Anomaly Detection in Machine Learning [50:00 mins]
  • Anomaly Detection - Assessment [10:00 mins]
  • Association Rules [61:20 mins]
  • Association Rules [11:20 mins]
  • Tutorial - Association Rules in Machine Learning [40:00 mins]
  • Association Rules - Assessment [10:00 mins]
  • Clustering [70:47 mins]
  • Clustering [20:49 mins]
  • Tutorial - Clustering in Machine Learning [35:00 mins]
  • Clustering - Assessment [10:00 mins]
  • Time Series [62:08 mins]
  • Time Series [12:08 mins]
  • Tutorial - Time Series in Machine Learning [35:00 mins]
  • Time Series - Assessment [10:00 mins]
  • Feedback
  • Applying Data Science Techniques using Machine Learning with Python - Feedback
Completion rules
  • All units must be completed
  • Leads to a certificate with a duration: Forever
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