Mastering Data Science Machine Learning and AI
What you’ll learn
Introduction to Data Science
Data Collection and Preprocessing
Exploratory Data Analysis
Statistical Modeling
Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Artificial Intelligence
Ethics and Bias in AI
Conclusion
Requirements
No Programming experience needed, you will learn what you need to know
Description
This comprehensive course is designed to take you on a journey through the exciting world of data science, machine learning, and artificial intelligence. You’ll learn the fundamental concepts, tools, and techniques used in these fields and gain practical skills that you can apply in real-world scenarios.Starting with an overview of data science and its various stages, you’ll dive into the different tools and techniques used in data science, such as data cleaning, feature engineering, and model evaluation. You’ll then explore various machine learning algorithms, including regression, decision trees, support vector machines, and neural networks.In addition to machine learning, this course also covers artificial intelligence, including natural language processing, computer vision, and deep learning. You’ll learn about the impact of AI on society, ethics, and best practices for avoiding bias in AI models.Data Science is an interdisciplinary field that involves the extraction, analysis, and interpretation of large and complex data sets to identify meaningful insights, make informed decisions, and support evidence-based decision making. It combines techniques and methods from various fields, including statistics, mathematics, computer science, and domain-specific knowledge, to work with structured and unstructured data.Data science is important for several reasons:1. Improved Decision Making: Data science enables organizations to make informed decisions based on data-driven insights, rather than relying on intuition or subjective opinions.2. Better Understanding of Customers: By analyzing large amounts of customer data, organizations can gain a better understanding of their customers’ behavior, preferences, and opinions, allowing them to tailor their products and services to meet their customers’ needs.So if you’re ready to master the exciting world of data science, machine learning, and artificial intelligence, enroll in this course today!
Overview
Section 1: Introduction to Data Science
Lecture 1 What is data science and why is it important?
Lecture 2 The process of data science and its different stages
Lecture 3 Different fields that use data science and examples of real-world applications
Lecture 4 Overview of the tools and techniques used in data science
Section 2: Data Collection and Preprocessing
Lecture 5 Sources of data and methods of collecting data
Lecture 6 Understanding and cleaning data
Lecture 7 Dealing with missing and duplicate values
Lecture 8 Feature engineering and selection
Section 3: Exploratory Data Analysis
Lecture 9 Univariate and multivariate analysis
Lecture 10 Data visualization techniques
Lecture 11 Identifying relationships and patterns in data
Section 4: Statistical Modeling
Lecture 12 Overview of statistics and probability
Lecture 13 Introduction to regression and classification models
Lecture 14 Overfitting and underfitting
Lecture 15 Model evaluation and selection
Section 5: Machine Learning
Lecture 16 Introduction to machine learning
Lecture 17 Different types of machine learning algorithms
Lecture 18 Overfitting and regularization
Lecture 19 Feature scaling and normalization
Section 6: Supervised Learning
Lecture 20 Linear regression and logistic regression
Lecture 21 Decision trees and random forests
Lecture 22 Support vector machines
Lecture 23 Neural networks and deep learning
Section 7: Unsupervised Learning
Lecture 24 Clustering algorithms
Lecture 25 Dimensionality reduction
Lecture 26 Anomaly detection
Section 8: Reinforcement Learning
Lecture 27 Markov decision processes
Lecture 28 Q-learning
Lecture 29 Policy gradient methods
Section 9: Artificial Intelligence
Lecture 30 Definition and history of artificial intelligence
Lecture 31 Types of artificial intelligence and their applications
Lecture 32 Natural language processing and computer vision
Lecture 33 Overview of deep learning and its applications
Section 10: Ethics and Bias in AI
Lecture 34 The impact of AI on society and ethics
Lecture 35 Bias in AI and its implications
Lecture 36 Fairness and accountability in AI
Lecture 37 Best practices for avoiding bias in AI models
Section 11: Conclusion
Lecture 38 Recap of key concepts and takeaways
Lecture 39 Opportunities and challenges in data science, machine learning, and AI
Lecture 40 Suggestions for further learning and resources
Antone interested in Mastering Data Science, Machine Learning, and Artificial Intelligence
Course Information:
Udemy | English | 1h 49m | 732.43 MB
Created by: Thomas Keyt
You Can See More Courses in the Developer >> Greetings from CourseDown.com