Mastering Data Science Machine Learning and AI

From Beginner to Expert
Mastering Data Science Machine Learning and AI
File Size :
732.43 MB
Total length :
1h 49m



Thomas Keyt


Last update




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

Mastering Data Science Machine Learning and AI


No Programming experience needed, you will learn what you need to know


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!


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

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