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[udemy] Algorithms for Data Science, Python, AI & Machine Learning
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 17 Lectures ( 3h 27m ) | Size: 1.43 GB
Learn how Algorithms are the Hidden Foundations of Python programing,
Data Science, AI and Machine Learning
What you'll learn
Understanding of Fundamental Algorithms:
By the end of the course, learners will comprehend the fundamental concepts of algorithms and their significance
Proficiency in Implementing Algorithms:
Learners will gain proficiency in implementing algorithms in programming, specifically using Python.
Application of Algorithms in Data Science:
learners will be capable of applying algorithms in the field of data science.
Data Structures:
Explore fundamental data structures such as arrays, linked lists, stacks, queues, trees, and graphs.
Sorting Algorithms:
Master various sorting techniques including bubble sort, merge sort, quicksort, and heap sort.
Searching Algorithms:
Understand and implement searching algorithms like binary search and linear search.
Recursion:
Learn the concept of recursion and how to apply it in solving problems.
Dynamic Programming:
Explore dynamic programming techniques for optimizing recursive algorithms.
Greedy Algorithms:
Understand greedy strategies for problem-solving and their applications.
Graph Algorithms:
Study algorithms related to graphs including depth-first search (DFS), breadth-first search (BFS), Dijkstra's algorithm, and A* algorithm.
Algorithm Design Techniques:
Delve into different algorithm design paradigms such as divide and conquer, dynamic programming, and greedy methods.
Python Programming Basics:
Get a solid foundation in Python programming language, covering syntax, control structures, and data types.
Exploratory Data Analysis (EDA):
Gain skills in analyzing datasets to discover patterns, trends, and insights.
Machine Learning Fundamentals:
Introduction to machine learning concepts, including supervised and unsupervised learning.
Natural Language Processing (NLP):
Introduction to NLP concepts and techniques for processing and analyzing text data.
Capstone Project:
Apply the knowledge gained through the course in a comprehensive capstone project,
solving a real-world data science problem using algorithms
Requirements
This course is designed to accommodate learners with varying levels of experience, including beginners.
While there are no strict prerequisites, having a basic understanding of programming concepts
and familiarity with Python would be beneficial
Interest in Data Science and Machine Learning
Access to a Computer and internet (google Colab)
Desire to Learn and research
Who this course is for:
Students and Professionals
Beginners in Programming and Data Science
Aspiring Data Scientists and Machine Learning Engineers
Self-Learners and Lifelong Learners
Professionals Seeking Career Advancement
Download
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 17 Lectures ( 3h 27m ) | Size: 1.43 GB
Learn how Algorithms are the Hidden Foundations of Python programing,
Data Science, AI and Machine Learning
What you'll learn
Understanding of Fundamental Algorithms:
By the end of the course, learners will comprehend the fundamental concepts of algorithms and their significance
Proficiency in Implementing Algorithms:
Learners will gain proficiency in implementing algorithms in programming, specifically using Python.
Application of Algorithms in Data Science:
learners will be capable of applying algorithms in the field of data science.
Data Structures:
Explore fundamental data structures such as arrays, linked lists, stacks, queues, trees, and graphs.
Sorting Algorithms:
Master various sorting techniques including bubble sort, merge sort, quicksort, and heap sort.
Searching Algorithms:
Understand and implement searching algorithms like binary search and linear search.
Recursion:
Learn the concept of recursion and how to apply it in solving problems.
Dynamic Programming:
Explore dynamic programming techniques for optimizing recursive algorithms.
Greedy Algorithms:
Understand greedy strategies for problem-solving and their applications.
Graph Algorithms:
Study algorithms related to graphs including depth-first search (DFS), breadth-first search (BFS), Dijkstra's algorithm, and A* algorithm.
Algorithm Design Techniques:
Delve into different algorithm design paradigms such as divide and conquer, dynamic programming, and greedy methods.
Python Programming Basics:
Get a solid foundation in Python programming language, covering syntax, control structures, and data types.
Exploratory Data Analysis (EDA):
Gain skills in analyzing datasets to discover patterns, trends, and insights.
Machine Learning Fundamentals:
Introduction to machine learning concepts, including supervised and unsupervised learning.
Natural Language Processing (NLP):
Introduction to NLP concepts and techniques for processing and analyzing text data.
Capstone Project:
Apply the knowledge gained through the course in a comprehensive capstone project,
solving a real-world data science problem using algorithms
Requirements
This course is designed to accommodate learners with varying levels of experience, including beginners.
While there are no strict prerequisites, having a basic understanding of programming concepts
and familiarity with Python would be beneficial
Interest in Data Science and Machine Learning
Access to a Computer and internet (google Colab)
Desire to Learn and research
Welcome to the comprehensive Udemy course, "Algorithms for Data Science, Python, and Machine Learning," where we embark on an exciting journey through the fascinating world of algorithms. This course is meticulously designed to cover essential concepts in programming, data science, machine learning, and artificial intelligence (AI). Our goal is to provide you with a deep understanding of how algorithms power the technologies behind modern applications and equip you with the skills to harness their potential for solving real-world challenges. Throughout this course, you'll gain both theoretical knowledge and practical experience in applying algorithms to solve intricate problems. This course is beginner-friendly but assumes some basic knowledge of programming and data science concepts. Don't worry, though; I will guide you through each topic step-by-step. You will explore fundamental data structures, sorting and searching algorithms, recursion, dynamic programming, greedy algorithms, and graph algorithms. Additionally, you'll delve into Python programming, essential libraries for data science, data preprocessing, and exploratory data analysis. We'll also cover probability and statistics, linear algebra, machine learning fundamentals, and advanced techniques like feature selection, dimensionality reduction, and natural language processing. Real-world applications and a comprehensive capstone project will solidify your understanding and prepare you to tackle complex problems in various domains. Join me on this journey through the world of algorithms for data science, Python, AI, and machine learning. I want to express my heartfelt gratitude to each and every one of you who will be joining me on this fascinating adventure. It will be an absolute pleasure to share my knowledge and expertise with you, and I hope that the insights and skills we will cover will serve you well in your future endeavors.
By the end of this course, you'll possess the knowledge and skills to confidently apply algorithms to solve complex problems in data science, programming, AI, and machine learning. You'll be equipped with practical experience and a deep understanding of how to use algorithms to drive innovation and efficiency in various domains.
By the end of this course, you'll possess the knowledge and skills to confidently apply algorithms to solve complex problems in data science, programming, AI, and machine learning. You'll be equipped with practical experience and a deep understanding of how to use algorithms to drive innovation and efficiency in various domains.
Who this course is for:
Students and Professionals
Beginners in Programming and Data Science
Aspiring Data Scientists and Machine Learning Engineers
Self-Learners and Lifelong Learners
Professionals Seeking Career Advancement
Download
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