InformIT – Data Structures, Algorithms, and Machine Learning Optimization LiveLessons (Video Training) 2021-6

InformIT – Data Structures, Algorithms, and Machine Learning Optimization LiveLessons (Video Training) 2021-6 Downloadly IRSpace

Apr 17, 2025 - 06:32 Updated: Aug 17, 2025 - 10:14

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InformIT – Data Structures, Algorithms, and Machine Learning Optimization LiveLessons (Video Training) 2021-6

Data Structures, Algorithms, and Machine Learning Optimization LiveLessons (Video Training) is a course on learning data structures, algorithms, and machine learning optimization.

What you will learn in Data Structures, Algorithms, and Machine Learning Optimization LiveLessons (Video Training):

Use big O marking 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 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 traversing
Discover how statistical methods work and machine learning for different optimizations.
Understand what multidimensional descending gradient optimization algorithms are and how to use them

Course specifications

Publisher: InformIT
Instructors: Jon Krohn
Language: English
Level: Average
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 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
Maxating 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.