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Udemy – 25 Projects in 25 days of AI Development Bootcamp 2024-11

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Lecture 21/25

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Lecture 21 of 25

Predict Car Prices using Random Forest

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Course Content

0 / 25 completed

Section 1: Week 15 videos

Simple Chatbot using predefined responses

28m

Basic Calculator using Python

44m

Spam Email Detector using Scikitlearn

1h 2m

Handwritten Digit Recognition with MNIST dataset

56m

Image Classifier using Keras and TensorFlow

1h 30m

Section 2: Week 25 videos

Sentiment Analysis on text data using NLTK

45m

Movie Recommendation System using cosine similarity

23m

Weather Forecasting using historical data

20m

Predict House Prices with Linear Regression

33m

Basic Neural Network from scratch

1h 30m

Section 3: Week 35 videos

Stock Price Prediction using historical data with a simple Linear Regression

47m

TicTacToe AI using Minimax Algorithm

19m

Predict Diabetes using logistic regression

1h 1m

Dog vs Cat Classifier with CNN

1h 21m

Credit Card Fraud Detection using Scikitlearn

35m

Section 4: Week 45 videos

Iris Flower Classification using decision trees

25m

Simple Personal Assistant using Python speech libraries

51m

Text Summarizer using Gensim

36m

Fake Product Review Detection using NLP techniques

31m

Detect Emotion in Text using Natural Language Toolkit NLTK

23m

Section 5: Week 55 videos

Predict Car Prices using Random Forest

16mNow Playing

Book Recommendation System using collaborative filtering

35m

Create a Resume Scanner using keyword extraction

34m

Identify Fake News using Naive Bayes

39m

Customer Churn Prediction using classification algorithms

36m

About this Lecture

Section: Week 5 • Size: 35.93 MB • Duration: 16m

Course Summary

25 Projects in 25 days of AI Development Bootcamp is a training program that allows you to do 25 practical projects in 25 days. These projects will help you gain basic skills and a comprehensive understanding of various AI concepts and machine learning techniques. The course starts with simple and understandable projects and gradually progresses to more complex applications. By the end, you’ll have an impressive portfolio of projects in areas such as natural language processing, image classification, recommender systems, predictive modeling, and more. Each project provides a hands-on learning experience and focuses on a specific machine learning concept, algorithm, or tool. What you will learn Aspiring AI Professionals: People who want to build a strong foundation in artificial intelligence, machine learning, and deep learning. Data Scientists and Analysts: Professionals looking to upgrade their skills and integrate artificial intelligence into their work. Developers and Software Engineers: People who are interested in building programs based on artificial intelligence. Students and Researchers: People studying AI or working on AI projects and looking for hands-on experience. This course is suitable for people who Aspiring AI Professionals: People who want to enter the world of artificial intelligence and machine learning. Data Scientists and Analysts: Professionals who work with data and want to use artificial intelligence and machine learning techniques in their data analysis pipelines. Software Engineers and Developers: Developers who have software engineering experience and are looking to enter the world of AI-based development. Students and Researchers: University students, PhDs or academic researchers interested in AI for academic projects or advancement in academic or industrial research roles. Tech Enthusiasts and Self-learners: Enthusiasts and beginners who are interested in artificial intelligence and are looking for a structured and practical approach to understanding artificial intelligence concepts, machine learning algorithms and how to apply them in real-world scenarios.

Course Content

0 / 25 completed

Section 1: Week 15 videos

Simple Chatbot using predefined responses

28m

Basic Calculator using Python

44m

Spam Email Detector using Scikitlearn

1h 2m

Handwritten Digit Recognition with MNIST dataset

56m

Image Classifier using Keras and TensorFlow

1h 30m

Section 2: Week 25 videos

Sentiment Analysis on text data using NLTK

45m

Movie Recommendation System using cosine similarity

23m

Weather Forecasting using historical data

20m

Predict House Prices with Linear Regression

33m

Basic Neural Network from scratch

1h 30m

Section 3: Week 35 videos

Stock Price Prediction using historical data with a simple Linear Regression

47m

TicTacToe AI using Minimax Algorithm

19m

Predict Diabetes using logistic regression

1h 1m

Dog vs Cat Classifier with CNN

1h 21m

Credit Card Fraud Detection using Scikitlearn

35m

Section 4: Week 45 videos

Iris Flower Classification using decision trees

25m

Simple Personal Assistant using Python speech libraries

51m

Text Summarizer using Gensim

36m

Fake Product Review Detection using NLP techniques

31m

Detect Emotion in Text using Natural Language Toolkit NLTK

23m

Section 5: Week 55 videos

Predict Car Prices using Random Forest

16mNow Playing

Book Recommendation System using collaborative filtering

35m

Create a Resume Scanner using keyword extraction

34m

Identify Fake News using Naive Bayes

39m

Customer Churn Prediction using classification algorithms

36m