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50 Algorithms Every Programmer Should Know
Duration
4 Days
Course
PLPJ-220
Available Formats
Classroom Training, Online Training
Course Description
Overview
The ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world by understanding how it works. You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and even work with machine learning algorithms to understand the math and logic behind them. Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and will learn about different types of deep learning models along with their practical use. You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that used to implement Large Language Models (LLMs) such as ChatGPT. Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.Objectives
What you will learn:
- Design algorithms for solving complex problems
- Become familiar with neural networks and deep learning techniques
- Explore existing data structures and algorithms found in Python libraries
- Implement graph algorithms for fraud detection using network analysis
- Delve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examples
- Create a recommendation engine that suggests relevant movies to subscribers
- Grasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMs
- Familiarize yourself with advanced deep learning architectures
- Explore newer topics, such as handling hidden bias in data and algorithm explainability
- Get to grips with different programming algorithms and choose the right data structures for their optimal implementation
Audience
Application Developers
Topics
- What is an algorithm?
- Python packages
- Algorithm design techniques
- Performance analysis
- Selecting an algorithm
- Validating an algorithm
- Summary
- Exploring Python built-in data types
- Exploring abstract data types
- Summary
- Introducing sorting algorithms
- Introduction to searching algorithms
- Practical applications
- Summary
- Introducing the basic concepts of designing an algorithm
- Understanding algorithmic strategies
- A practical application – solving the TSP
- Presenting the PageRank algorithm
- Understanding linear programming
- Summary
- Understanding graphs: a brief introduction
- Graph theory and network analysis
- Representations of graphs
- Graph mechanics and types
- Introducing network analysis theory
- Understanding graph traversals
- Case study: fraud detection using SNA
- Summary
- Introducing unsupervised learning
- Understanding clustering algorithms
- Steps of hierarchical clustering
- Coding a hierarchical clustering algorithm
- Understanding DBSCAN
- Creating clusters using DBSCAN in Python
- Evaluating the clusters
- Dimensionality reduction
- Association rules mining
- Summary
- Understanding supervised machine learning
- Formulating supervised machine learning problems
- Understanding classification algorithms
- Decision tree classification algorithm
- Understanding the ensemble methods
- Logistic regression
- The SVM algorithm
- Bayes’ theorem
- For classification algorithms, the winner is...
- Linear regression
- For regression algorithms, the winner is...
- Practical example – how to predict the weather
- The evolution of neural networks
- Understanding neural networks
- Training a neural network
- Understanding the anatomy of a neural network
- Defining gradient descent
- Activation functions
- Tools and frameworks
- Choosing a sequential or functional model
- Understanding the types of neural networks
- Using transfer learning
- Case study – using deep learning for fraud detection
- Introducing NLP
- Understanding NLP terminology
- Cleaning data using Python
- Understanding the Term Document Matrix
- Introduction to word embedding
- Implementing word embedding with Word2Vec
- Case study: Restaurant review sentiment analysis
- Applications of NLP
- Understanding sequential data
- Data representation for sequential models
- Introducing RNNs
- GRU
- Introducing LSTM
- The evolution of advanced sequential modeling techniques
- Exploring autoencoders
- Understanding the Seq2Seq model
- Understanding the attention mechanism
- Delving into self-attention
- Transformers: the evolution in neural networks after self-attention
- LLMs
- Bottom of Form
- Introducing recommendation systems
- Types of recommendation engines
- Understanding the limitations of recommendation systems
- Areas of practical applications
- Practical example – creating a recommendation engine
- Introduction to data algorithms
- Presenting the CAP theorem
- Decoding data compression algorithms
- Practical example: Data management in AWS: A focus on CAP theorem and compression algorithms
- Introduction to cryptography
- Understanding the types of cryptographic techniques
- Example: security concerns when deploying a machine learning model
- Introduction to large-scale algorithms
- Characterizing performant infrastructure for large-scale algorithms
- Strategizing multi-resource processing
- Understanding theoretical limitations of parallel computing
- How Apache Spark empowers large-scale algorithm processing
- Using large-scale algorithms in cloud computing
- Challenges facing algorithmic solutions
- Failure of Tay, the Twitter AI bot
- The explainability of an algorithm
- Understanding ethics and algorithms
- Reducing bias in models
- When to use algorithms
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