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5 Python AI Projects to Build Today

Introduction

Today, I’m sharing with you five Python AI projects and exactly how to build them! Not only will I provide you with the project ideas, but I’ll also walk you through the different modules you should use and give you quick code samples to get started. Stick around until the end as these projects will range from easy to difficult, with the last one being particularly exciting for those diving deeper into AI.

Project 1: Sentiment Analysis Tool

The first project on my list is a sentiment analysis tool. This project involves reading textual data, like movie reviews or comments, and analyzing its sentiment—whether it’s positive, negative, or neutral.

Tools Required

  • Natural Language Toolkit (NLTK) – For processing textual data.
  • Pandas – For data preprocessing.
  • Scikit-learn – To create the sentiment analysis model.

Sample Code Snippet

# Sample code for sentiment analysis
import nltk
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB

# Load dataset
data = pd.read_csv('movie_reviews.csv')

# Preprocess data
X = data['review'] # feature
y = data['sentiment'] # target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Create model and train
model = MultinomialNB()
model.fit(X_train, y_train)

# Evaluation
accuracy = model.score(X_test, y_test)
print(f'Accuracy: {accuracy * 100}%')
        

Project 2: Image Classifier

The next project is to build an image classifier using Convolutional Neural Networks (CNNs) to classify images based on their pixel data.

Tools Required

  • TensorFlow – For creating neural networks.
  • Keras – High-level API for TensorFlow.
  • Pandas and NumPy – For data handling.

Sample Code Snippet

# Sample code for image classification
import tensorflow as tf
from tensorflow import keras

# Load dataset
(train_images, train_labels), (test_images, test_labels) = keras.datasets.mnist.load_data()

# Preprocess data
train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255

# Build model
model = keras.Sequential([keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
                          keras.layers.MaxPooling2D(pool_size=(2, 2)),
                          keras.layers.Flatten(),
                          keras.layers.Dense(64, activation='relu'),
                          keras.layers.Dense(10, activation='softmax')])

# Compile and train model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5)
        

Project 3: Voice Assistant with Speech Recognition

Next, we’ll build a voice assistant that can interpret speech with recognition capabilities. This project allows users to interact using voice commands.

Tools Required

  • SpeechRecognition – For converting speech to text.
  • pyttsx3 – For converting text to speech.

Sample Code Snippet

# Sample code for voice assistant
import speech_recognition as sr
import pyttsx3

# Initialize recognizer and text-to-speech engine
recognizer = sr.Recognizer()
engine = pyttsx3.init()

# Function to speak
def speak(text):
    engine.say(text)
    engine.runAndWait()

# Function for listening and returning the recognized audio
def listen():
    with sr.Microphone() as source:
        audio = recognizer.listen(source)
        try:
            return recognizer.recognize_google(audio)
        except sr.UnknownValueError:
            speak("Sorry, I did not understand.")
            return None

# Main loop
while True:
    command = listen()
    if command:
        speak(f"You said: {command}")
        # Implement further functionalities based on commands
        if 'exit' in command:
            break
        speak("What else can I do for you?")
        continue
        

Project 4: Recommendation System

Building a recommendation system is a great way to understand how recommendations are made on platforms like Netflix or Amazon.

Tools Required

  • Surprise – A dedicated library for building recommendation systems.
  • Pandas and NumPy – For data manipulation.

Sample Code Snippet

# Sample code for recommendation system
from surprise import Dataset, Reader
from surprise import SVD
from surprise.model_selection import train_test_split
from surprise import accuracy

# Load dataset
data = Dataset.load_from_file('movie_ratings.csv', reader=Reader())
trainset, testset = train_test_split(data.build_full_trainset(), test_size=0.2)

# Train model
model = SVD()
model.fit(trainset)

# Predictions
predictions = model.test(testset)
accuracy.rmse(predictions)
        

Project 5: AI Agent

The final project is to create an AI agent capable of performing various tasks such as scheduling events, saving notes, and more.

Tools Required

  • LangChain or Llama Index – For interacting with large language models.
  • API calls and database management libraries – For interaction with external services.

Sample Code Snippet

# Sample code for AI agent
from langchain import LLMChain
from langchain.agents import initialize_agent

# Define tools for the agent
tools = [....]  # Define your tools here

# Initialize agent
agent = initialize_agent(tools)

# Example command
response = agent("What is on my schedule for today?")
print(response)
        

Conclusion

These five Python AI projects are a fantastic way to hone your skills and explore the practical applications of artificial intelligence. Whether you are a beginner looking to learn or an experienced developer wanting to refine your skills, each project will provide valuable experience. Happy coding!

For additional resources, check the links in the description of this article for tutorials and guides to dive deeper into each project.

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  1. thank you for the video, here are my notes from it. I like the sentiment analysis and is something that I would like to integrate in my current web app

    Sentiment analysis
    NLTK (natural language tool kit)
    Pandas
    Scikit learn
    Image classifier
    Tensor flow
    Keras
    Pandas
    Numpy
    AI Voice Assistant
    Sentiment analysis with speech recognition
    PYTTSX3 (text to speech)
    Llamas
    Recommendation System
    User ID
    Purchase patterns
    Surprise (module)
    Scikit learn
    Tensor flow
    Pytorch
    AI Agent
    Lang chain
    llamas

  2. Is it possible to do a Browsergame Bot with an AI Agent? Like create all the methods to control the game and let him do the logic to play?

  3. Hi, thank u for this tutorial… In the first project. when I give print(predict_sentiment("I liked this movie")) the output is ['neg'], I tried various combination, but did not work. I felt that there too much emphasis on the word "fantastic". where ever the word fantastic appears it show positive eg. print(predict_sentiment("I absolutely fantastic disliked this movie")). I tried TfidfVectorizer, got the accuracy score to 82. but still don't seem to work very well. Appreciate ur inputs.

  4. curious… can you do a video about storing and loading the ai model/data… allot of YT videos mention it but then swiftly move along…
    was going to ask about vector db … but see you touched on using Astra… wanted to ask about Mongo as a vector store.

  5. my man…ill jion soon…need really only your help…gotmyself a 3d printer and plan on building alot of projects using open source ai…good content as always

  6. Tim, I turn 69 next month, and after following you for a couple of years, now all I want for the Bday is to be able to turn the hands of time back about 25 years. This was so interesting; I have just started to work with AI's and I cannot get enough of it. While lot of what you talked about was a little over my head, still I could understand a lot of it. I have a ton of questions. I know you are in your twenties but after watching this one I feel like a little kid again. Thank you. It looks like I have to sign up for your pay subscription again as I need to drill down more into this. Question: Have you done any modeling for Stock trading like predictive analysis on different stocks. I am thinking about retiring next year and would like to keep track and gather information to make buys. Thanks again Tim you are a Star in my book sir..

  7. "Could we can collaborate on some projects more effectively? I would greatly appreciate your help in expanding my code. Thank you. Whenever I've reached out to programmers with a request, they've unfortunately been unable to assist due to time constraints, and my requests for collaboration have been declined. I kindly ask that you consider responding positively to my request just this once. My request is for you to review the code I've written . Please consider accepting my request."

  8. I'm an AI student, I have tried some of these projects and I'm not good at coding, and your coding skills are very good , and I'm learning from you a lot 💓💞