Master Machine Learning: Complete 2024 Video Course
Focused View
9:24:15
001. Chapter 1. Introduction Delivering machine learning projects is hard; lets do it better.mp4
09:10
002. Chapter 1. Why is ML important.mp4
08:38
003. Chapter 1. Other machine learning methodologies.mp4
09:00
004. Chapter 1. Understanding this book.mp4
04:13
005. Chapter 1. Case study The Bike Shop.mp4
01:48
006. Chapter 1. Summary.mp4
01:03
007. Chapter 2. Pre-project From opportunity to requirements.mp4
05:50
008. Chapter 2. Project management infrastructure.mp4
04:07
009. Chapter 2. Project requirements.mp4
18:47
010. Chapter 2. Data.mp4
09:09
011. Chapter 2. Security and privacy.mp4
03:24
012. Chapter 2. Corporate responsibility, regulation, and ethical considerations.mp4
12:14
013. Chapter 2. Development architecture and process.mp4
09:46
014. Chapter 2. Summary.mp4
01:10
015. Chapter 3. Pre-project From requirements to proposal.mp4
11:43
016. Chapter 3. Create an estimate.mp4
23:56
017. Chapter 3. Pre-salespre-project administration.mp4
02:12
018. Chapter 3. Pre-projectpre-sales checklist.mp4
01:33
019. Chapter 3. The Bike Shop pre-sales.mp4
19:20
020. Chapter 3. Pre-project postscript.mp4
03:02
021. Chapter 3. Summary.mp4
00:45
022. Chapter 4. Getting started.mp4
02:48
023. Chapter 4. Finalize team design and resourcing.mp4
02:22
025. Chapter 4. Infrastructure plan.mp4
04:49
026. Chapter 4. The data story.mp4
17:25
027. Chapter 4. Privacy, security, and an ethics plan.mp4
02:25
028. Chapter 4. Project roadmap.mp4
03:42
029. Chapter 4. Sprint 0 checklist.mp4
00:35
030. Chapter 4. Bike Shop project setup.mp4
17:02
031. Chapter 4. Summary.mp4
00:58
032. Chapter 5. Diving into the problem.mp4
01:51
033. Chapter 5. Understanding the data.mp4
23:20
034. Chapter 5. Business problem refinement, UX, and application design.mp4
10:23
035. Chapter 5. Building data pipelines.mp4
22:15
036. Chapter 5. Model repository and model versioning.mp4
09:03
037. Chapter 5. Summary.mp4
01:18
038. Chapter 6. EDA, ethics, and baseline evaluations.mp4
25:17
039. Chapter 6. Ethics checkpoint.mp4
03:29
040. Chapter 6. Baseline models and performance.mp4
02:22
041. Chapter 6. What if there are problems.mp4
04:53
042. Chapter 6. Pre-modeling checklist.mp4
00:34
043. Chapter 6. The Bike Shop Pre-modelling.mp4
18:46
045. Chapter 7. Making useful models with ML.mp4
03:24
046. Chapter 7. Feature engineering and data augmentation.mp4
12:27
048. Chapter 7. Making models with ML.mp4
16:16
049. Chapter 7. Stinky, dirty, no good, smelly models.mp4
04:16
050. Chapter 7. Summary.mp4
01:00
051. Chapter 8. Testing and selection.mp4
05:51
052. Chapter 8. Testing processes.mp4
29:04
053. Chapter 8. Model selection.mp4
16:53
054. Chapter 8. Post modelling checklist.mp4
01:36
055. Chapter 8. The Bike Shop sprint 2.mp4
14:43
056. Chapter 8. Summary.mp4
02:26
057. Chapter 9. Sprint 3 system building and production.mp4
04:31
058. Chapter 9. Types of ML implementations.mp4
20:26
059. Chapter 9. Nonfunctional review.mp4
01:42
060. Chapter 9. Implementing the production system.mp4
20:38
061. Chapter 9. Logging, monitoring, management, feedback, and documentation.mp4
13:48
062. Chapter 9. Pre-release testing.mp4
03:41
063. Chapter 9. Ethics review.mp4
02:23
064. Chapter 9. Promotion to production.mp4
03:00
065. Chapter 9. You arent done yet.mp4
01:22
066. Chapter 9. The Bike Shop sprint 3.mp4
10:19
067. Chapter 9. Summary.mp4
01:33
068. Chapter 10. Post project.mp4
00:56
069. Chapter 10. Off your hands and into production.mp4
20:16
070. Chapter 10. Team post-project review.mp4
04:38
071. Chapter 10. Improving practice.mp4
04:15
072. Chapter 10. New technology adoption.mp4
02:06
073. Chapter 10. Case study.mp4
01:21
074. Chapter 10. Goodbye and good luck.mp4
01:22
075. Chapter 10. Summary.mp4
01:35
More details
Course Overview
Dive into the world of machine learning with this comprehensive video course designed to take you from beginner to proficient practitioner. With over 33,855 seconds of expert-led content, you'll gain hands-on experience with real-world projects.
What You'll Learn
- Fundamentals of machine learning algorithms
- Practical implementation of ML projects
- Best practices for model training and evaluation
Who This Is For
- Aspiring data scientists
- Software developers expanding their skillset
- Tech professionals seeking ML expertise
Key Benefits
- Project-based learning approach
- 9+ hours of comprehensive video content
- Real-world applicable skills
Curriculum Highlights
- Introduction to Machine Learning Concepts
- Building Your First ML Model
- Advanced Project Implementation
Focused display
Category
- language english
- Training sessions 72
- duration 9:24:15
- Release Date 2025/05/26
