CBTNuggets – Introduction to Machine Learning


ดาวน์โหลดคอร์สเรียน CBTNuggets – Introduction to Machine Learning ฟรี

หมวดหมู่ (Category) : Developer

ข้อมูลไฟล์ (File Info) :

Released 3/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 420 Lessons ( 48h ) | Size: 29 GB

คำอธิบาย (Description) :

This entry-level training in machine learning and artificial intelligence prepares learners to convert vast datasets into not only meaningful information but also actionable insights, predictions, and forward-looking trends.

The impact of machine learning on today’s technological landscape is simply immeasurable. This course serves as an introduction to the groundbreaking power of machine learning, and aims to illuminate the exciting possibilities of solving real-world problems with machine learning. It’s up to you to harness these insights and skills to solve specific problems in your organization or professional work.

Fortunately, this course goes beyond the concepts of machine learning by offering hands-on opportunities to build models with scikit-learn, PyTorch, TensorFlow, and even a crash course in LLM development with OpenAI, LangChain, and HuggingFace.

Once you complete this Introduction to Machine Learning training, you’ll be adept at employing algorithms to uncover hidden insights, leverage statistical analysis, and generate data-driven predictive outcomes – all by using machine learning.

For leaders of IT teams, this machine learning course offers an amazing transformative value: ideal for new junior data scientists transitioning into machine learning, integrating personalized training sessions, or simply a comprehensive reference for data science, machine learning, and artificial intelligence (AI) concepts and best practices.

Introduction to Machine Learning: What You Need to Know
This machine learning training features videos that cover essential data science, machine learning, and AI topics including

Exploring machine learning fundamentals and the latest best practices
Making sense of algorithms such as gradient descent and backpropagation
Implementing classification and regression models to uncover patterns in data
Diving into the perceptron and neural networks with powerful AI modeling concepts
Hands-on introduction to PyTorch, and TensorFlow model building
Distilling Large Language Models (LLMs) with ChatGPT, LangChain, and HuggingFace

Who Should Take Introductory Machine Learning Training?
The introduction to machine learning training is presented as associate-level data science training, which means it was designed for junior data scientists and aspiring machine learning engineers. This machine learning skills course offers significant value to both emerging IT professionals with at least a year of experience and seasoned data scientists looking to validate their data science skills in an ever advancing field.

New or aspiring junior data scientists. If you’re a brand new data scientist, you don’t want to start your first job without a familiarity with machine learning. Whether you’re looking for your first job or you’re still a student, take this introduction to machine learning and bring all the capabilities and opportunities of machine learning with you to your first job from day one.

Experienced junior data scientists. If you’ve navigated working as a data scientist for several years without delving into machine learning, congrats on your achievement! This introductory machine learning course will further broaden your wheelhouse of skills, empowering you to work with precision, efficiency, and alignment to the latest best practices and tools. Not to mention staying at the forefront of data science but also opening up profitable opportunities and advancement in your career.

เนื้อหาหลักสูตร (Overview) :

1. Explore How AI Agents Navigate Driving Directions
2. Apply Probability to Real-World AI Problems
3. Define What is Machine Learning
4. Setup a Machine Learning Development Environment
5. Explore How AI Agents Navigate Driving Directions
6. Apply Probability to Real-World AI Problems
7. Define What is Machine Learning
8. Setup a Machine Learning Development Environment
9. Explore Data Pipelines and Linear Regression
10. Apply Regression Concepts for Supervised Learning
11. Examine Cost Functions and Parameter Tuning
12. Implement Gradient Descent for Linear Regression
13. Vectorize Operations for Multiple Regression
14. Explore Feature Engineering and Data Preparation
15. Identify Key Classification Algorithms
16. Implement Logistic Regression with Python
17. Build a Python Decision Tree Classification Model
18. Build a Python Random Forest Classification Model
19. Apply Regularization to Overcome Overfitting
20. Build a Support Vector Machine Classifier
21. Build a K-Nearest Neighbors Classifier
22. Explore Neural Network Basics With The Perceptron
23. Implement a Perceptron for Classification
24. Explore PyTorch Fundamentals for Machine Learning
25. Leverage PyTorch Tensor Attributes and Operators
26. Explore Fundamental PyTorch Tensor Operations
27. Apply PyTorch Tensor Manipulation and Indexing
28. Explore Gradient Descent & Back Propagation
29. Predict Ice Cream Sales with PyTorch Regression
30. Implement a Logistic Regression Model with PyTorch
31. Explore Neural Network Classification with PyTorch
32. Build a PyTorch Classifier with Non-Linearity
33. Explore Multi-class Classification with PyTorch
34. Tune Hyperparameters and Analyze Fit with PyTorch
35. Discover What’s New with PyTorch 2.0
36. Explore TensorFlow Machine Learning Foundations
37. Explore TensorFlow Aggregation and Manipulation
38. Implement Matrix Multiplication with TensorFlow
39. Reshape, Transpose, and Alter TensorFlow Tensors
40. Squeeze, Encode, and Optimize TensorFlow Tensors
41. Explore Neural Network Regression with TensorFlow
42. Build a Simple Regression Model with TensorFlow
43. Evaluate Regression Models with TensorFlow
44. Visualize and Evaluate Performance with TensorFlow
45. Normalize and Feature Scale Data with TensorFlow
46. Explore TensorFlow Neural Network Classification
47. Build a Neural Network Classifier with TensorFlow
48. Build a TensorFlow Classifier with Non-Linearity
49. Evaluate TensorFlow Classification Models
50. Explore Multi-Class Classification with TensorFlow
51. Tune Multi-Class Classification TensorFlow Models
52. Explore Multi-Label Classification with TensorFlow
53. Explore The Fundamentals of Large Language Models
54. Build LLM Apps with ChatGPT and the OpenAI API
55. Design Effective Prompts for Large Language Models
56. Implement LangChain in Language Model Workflows
57. Implement LangChain Memory for Autonomous Tasks
58. Combine LangChain Components for Coherent Apps
59. Build Task-Driven Autonomous Agents with LangChain
60. Use LangChain to Interact with PDFs and Documents
61. Use LangChain to Chat with PDFs and Documents
62. Explore Transformer Encoders and Decoders
63. Examine the Fundamentals of HuggingFace


(Course Preview)


File Info

Official Website : https://www.cbtnuggets.com/it-training/data-science/introduction-machine-learning
File Name : Introduction to Machine Learning.part(1-3).rar File Size : 28.9 GB File Type : *.rar Server : Google Drive Upload date : 16/04/2024 Last modified : 16/04/2024 Password : sbz

Warning! This file is for educational and non-commercial use only. Downloading copyrighted material is illegal and all the files here are only for educational uses. To support creators/developers Please purchase a genuine version from the official website. We don’t own and resell this product, we got this from a free source. Developers/creator/maker made it with difficulty. Please purchase a genuine license from the official website.

💾 ดาวน์โหลด

รหัสแตกไฟล์คือ sbz
วิธีดาวน์โหลด | วิธีแก้ลิ้งค์เกินโควต้า