Description
Data Structures, Algorithms, and Machine Learning Optimization LiveLessons (Video Training), is a training course on data structures, algorithms, and machine learning optimization.
What you will learn in the Data Structures, Algorithms, and Machine Learning Optimization LiveLessons (Video Training) course:
- Using big O notation to describe the effect of time and space based on an algorithm that enables you to choose the best way to solve a machine learning problem using available hardware resources.
- Getting to know all the range of Python data structures including list-, dictionary-, tree-, and graph-based structures
- Develop a usable understanding of all the necessary algorithms for working with data, including searching, sorting, hashing and traversing
- Discover how statistical methods and machine learning work for different optimizations.
- Understanding what multidimensional gradient descent optimization algorithms are and their use
Course details
Publisher: InformIT
Instructors: Jon Krohn
English language
Education level: Intermediate
Number of courses: 66
Duration: 6 hours and 28 minutes
Course topics:
Lesson 1: Orientation to Data Structures and Algorithms
Topics
1.1 Orientation to the Machine Learning Foundations Series
1.2 A Brief History of Data
1.3 A Brief History of Algorithms
1.4 Applications to Machine Learning
Lesson 2: “Big O” Notation
Topics
2.1 Introduction
2.2 Constant Time
2.3 Linear Time
2.4 Polynomial Time
2.5 Common Runtimes
2.6 Best versus Worst Case
Lesson 3: List-Based Data Structures
Topics
3.1 Lists
3.2 Arrays
3.3 Linked Lists
3.4 Doubly-Linked Lists
3.5 Stacks
3.6 Queues
3.7 Deques
Lesson 4: Searching and Sorting
Topics
4.1 Binary Search
4.2 Bubble Sort
4.3 Merge Sort
4.4 Quick Sort
Lesson 5: Sets and Hashing
Topics
5.1 Maps and Dictionaries
5.2 Sets
5.3 Hash Functions
5.4 Collisions
5.5 Load Factor
5.6 Hash Maps
5.7 String Keys
5.8 Hashing in ML
Lesson 6: Trees
Topics
6.1 Introduction
6.2 Decision Trees
6.3 Random Forests
6.4 XGBoost: Gradient-Boosted Trees
6.5 Additional Concepts
Lesson 7: Graphs
Topics
7.1 Introduction
7.2 Directed versus Undirected Graphs
7.3 DAGs: Directed Acyclic Graphs
7.4 Additional Concepts
7.5 Bonus: Pandas DataFrames
7.6 Resources for Further Study of DSA
Lesson 8: Machine Learning Optimization
Topics
8.1 Statistics versus Machine Learning
8.2 Objective Functions
8.3 Mean Absolute Error
8.4 Mean Squared Error
8.5 Minimizing Cost with Gradient Descent
8.6 Gradient Descent from Scratch with PyTorch
8.7 Critical Points
8.8 Stochastic Gradient Descent
8.9 Learning Rate Scheduling
8.10 Maximizing Reward with Gradient Ascent
Lesson 9: Fancy Deep Learning Optimizers
Topics
9.1 Jacobian Matrices
9.2 Second-Order Optimization and Hessians
9.3 Momentum
9.4 Adaptive Optimizers
9.5 Congratulations and Next Steps
Summary
Course prerequisites:
Mathematics: Familiarity with secondary school-level mathematics will make the class easier to follow along with. If you are comfortable dealing with quantitative information–such as understanding charts and rearranging simple equations–then you should be well-prepared to follow along with all of the mathematics.
Programming: All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples.
Pictures
Sample video
Installation guide
After Extract, view with your favorite Player.
Subtitle: None
Quality: 720p
download link
Password file(s): www.downloadly.ir
Size
9.3 GB
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