Description
Data Structures, Algorithms, and Machine Learning Optimization LiveLessons (video training) is a course on learning data structures, algorithms, and machine learning optimization.
What you’ll learn in the Data Structures, Algorithms, and Machine Learning Optimization live courses (video training):
- Use the large O marking to describe the effect of time and space based on an algorithm that lets you choose the best way to solve a machine learning problem using existing hardware resources.
- Familiarize yourself with the full range of Python data structures, including list-, dictionary-, tree-, and graph-based structures
- Develop an understanding that can be used for all essential data algorithms, including searching, sorting, hashing, and traversal
- Learn how statistical methods and machine learning work for different optimizations.
- Understand what multidimensional descending gradient optimization algorithms are and how to use them
Course specifications
Publisher: Inform IT
Instructors: Jon Krohn
French language
Average level
Lessons: 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 the data
1.3 A brief history of algorithms
1.4 Applications to machine learning
Lesson 2: Big O Notation
Topics
2.1 Presentation
2.2 Definite time
2.3 Linear time
2.4 Polynomial time
2.5 Common Execution Engines
2.6 Best versus worst case
Lesson 3: List-Based Data Structures
Topics
3.1 Lists
3.2 Tables
3.3 Linked Lists
3.4 Doubly linked lists
3.5 Batteries
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 Hash
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 Chain keys
5.8 Hashing in ML
Lesson 6: Trees
Topics
6.1 Presentation
6.2 Decision trees
6.3 Random forests
6.4 XGBoost: gradient boosted trees
6.5 Additional concepts
Lesson 7: Graphs
Topics
7.1 Presentation
7.2 Directed and undirected graphs
7.3 DAG: directed acyclic graphs
7.4 Additional concepts
7.5 Bonuses: Pandas DataFrames
7.6 Resources for Further Study of DSA
Lesson 8: Optimizing Machine Learning
Topics
8.1 Statistics versus machine learning
8.2 Objective functions
8.3 Mean absolute error
8.4 Root mean square error
8.5 Minimizing costs with gradient descent
8.6 Gradient descent from zero with PyTorch
8.7 Critical Points
8.8 Stochastic gradient descent
8.9 Learning
Maxation with gradient ascent
Lesson 9: Sophisticated 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:
Math: The knowledge of mathematics at the secondary level will facilitate the follow-up of the class. If you are comfortable with quantitative information, such as understanding graphs and rearranging simple equations, you should be well prepared to take all the math.
Programming: All code demonstrations will be in Python, so experience with this or another object-oriented programming language would be helpful to follow the hands-on examples.
Pictures
sample movie
Installation guide
After ripping, watch with your favorite player.
Subtitle: None
Quality: 720p
Download link
File password(s): ngaur.com
Cut
9.3 GB