Master Data Science Techniques (AI) using ChatGPT's Secrets - Udemy

What you'll learn
Master the fundamental concepts and tools of data science, including Python programming, data visualization, and statistical analysis.
Gain hands-on experience with popular data science libraries such as NumPy, Pandas, Matplotlib, and Seaborn.
Learn to clean, explore, and visualize data to uncover patterns and insights.
Understand and apply the concepts of linear and logistic regression, decision trees, and random forests for prediction and classification tasks.
Learn unsupervised learning techniques such as clustering and dimensionality reduction.
Learn advanced methods in NLP and deep learning.
Understand the concepts and techniques of time series analysis and forecasting.
Build recommendation systems and web scraping.
Learn Reinforcement Learning and Robotics.
Apply your newfound skills to real-world projects and use cases.
Be equipped with the skills to become a data scientist or data analyst.
Get a strong foundation to pursue advanced topics in machine learning and artificial intelligence.
Requirements
Basic knowledge of programming concepts.
Familiarity with basic mathematical concepts such as probability and statistics.
Understanding of basic linear algebra and calculus concepts would be helpful but not necessary.
Familiarity with basic computer science concepts such as data structures and algorithms is helpful but not necessary.
Some knowledge of machine learning would be helpful but not necessary.
Description
This course is designed to give you a comprehensive introduction to the world of data science. You will learn the fundamental concepts and tools of data science, including Python programming, data visualization, and statistical analysis. Throughout the course, you will gain hands-on experience with popular data science libraries such as NumPy, Pandas, Matplotlib, and Seaborn.You will learn how to clean, explore, and visualize data to uncover patterns and insights. We will cover linear and logistic regression, decision trees, and random forests for prediction and classification tasks.In addition, you will learn unsupervised learning techniques such as clustering and dimensionality reduction. We will also cover advanced methods in NLP and deep learning.You will learn the concepts and techniques of time series analysis and forecasting. We will also cover building recommendation systems and web scraping.We will also cover Reinforcement Learning and Robotics.Throughout the course, you will apply your newfound skills to real-world projects and use cases. By the end of the course, you will be equipped with the skills to become a data scientist or data analyst, and have a strong foundation to pursue advanced topics in machine learning and artificial intelligence.This course is intended for a wide range of learners, including aspiring data scientists and analysts, professionals from various backgrounds who want to learn data science to analyze data and make data-driven decisions, students studying computer science, mathematics, statistics or related fields who want to gain a deeper understanding of data science, entrepreneurs and small business owners who want to gain insights from data to improve their business, IT professionals who want to add data science skills to their toolkit, researchers and academics who want to analyze data to support their research and anyone who is interested in understanding the basics of data science, machine learning and artificial intelligence.
Overview
Section 1: Introduction to Data Science and Python
Lecture 1 Introduction to Python
Lecture 2 Explore and analyze a dataset of your choice using Python and Pandas
Section 2: Linear Regression
Lecture 3 Build a linear regression model to predict housing prices
Section 3: Decision Trees and Random Forest
Lecture 4 Implement a decision tree algorithm to classify iris flowers
Lecture 5 Use Random Forest to classify whether a bank loan will default or not
Section 4: Unsupervised Learning
Lecture 6 Use K-means clustering algorithm to segment customers by purchasing behavior
Section 5: Gradient Boosting
Lecture 7 Build a gradient boosting model for anomaly detection in network traffic
Section 6: Natural Language Processing (NLP)
Lecture 8 Use natural language processing techniques to analyze sentiment in a set of movi
Section 7: Deep Learning
Lecture 9 Create a neural network to classify images of handwritten digits
Lecture 10 Use DL to create a model that can generate new text
Lecture 11 Create a deep learning model for image segmentation
Section 8: Time series and Forecasting
Lecture 12 Build a time series forecasting model to predict stock prices
Section 9: Recommender Systems
Lecture 13 Build a recommendation system to suggest products to online shoppers
Section 10: Web Scraping and Big Data
Lecture 14 Create a web scraping script to collect data from the internet
This course is designed to be accessible to learners of all backgrounds and levels of experience. It will provide a strong foundation in data science concepts and tools and will be valuable for learners looking to start a career in data science, or use data science in their current roles.


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