Machine Learning & Deep Learning Projects with Python

Master Artificial Intelligence with Hands-On Projects

Unlock the power of Artificial Intelligence (AI) with this comprehensive course! Dive into the world of Machine Learning and Deep Learning through practical projects designed to transform real-world challenges into intelligent solutions. This course is perfect for those with a foundational understanding of Python and Machine Learning.

What You'll Achieve:

• Gain a holistic understanding of Artificial Intelligence by applying algorithms to solve practical problems.
• Master the core principles of Machine Learning software and Deep Learning algorithms.
• Understand the key differences between Machine Learning and Deep Learning.
• Become proficient in using industry-standard libraries: Scikit-Learn (for Machine Learning) and TensorFlow & Keras (for Deep Learning).
• Develop the ability to independently create AI-powered solutions for everyday problems.
• Gain experience with diverse datasets, including popular datasets used in higher education and custom datasets.

Course Highlights: 12 Engaging AI Projects

This course features 12 in-depth projects, progressing from beginner to advanced levels, covering a wide range of AI applications:

1. House Price Prediction using Machine Learning: Build a predictive model using multiple linear regression with Scikit-Learn to estimate property values.
2. Salary Calculation using Machine Learning: Develop a polynomial linear regression model to accurately calculate employee salaries, accounting for non-linear relationships.
3. Handwritten Digit Recognition using Multiple Machine Learning Models: Combine various Machine Learning models to create a robust system for recognizing handwritten text – a practical application of Artificial Intelligence.
4. Advanced Customer Segmentation using Machine Learning: Implement a sophisticated clustering algorithm (beyond K-Means) using a library developed by MIT to segment customer data (both numeric and categorical) for more accurate targeting.
5. IMDB Sentiment Analysis Using NLP (Natural Language Processing): Develop sentiment analysis software using NLP techniques and the Kaggle dataset to automatically classify movie reviews as positive or negative.
6. Predicting Diabetes using Artificial Neural Networks: Build and compare two Artificial Neural Network (ANN) models – one with a single neuron and another with two hidden layers – to predict diabetes based on patient data from the Pima Indians Diabetes Database.
7. Image Classification using Convolutional Neural Network (CNN) and Artificial Neural Network Algorithms (Deep Learning): Classify thousands of images using Convolutional Neural Networks (CNNs) and other Deep Learning techniques with TensorFlow and Keras.
8. San Francisco Crime Geographical Clustering using Machine Learning: Perform geographic clustering of crime data using geolocation information (latitude & longitude) from the SFPD dataset. Visualize the results on a Python-based map and export the map to HTML.
9. Image Classification (ImageNet Library) using Transfer Learning - Keras InceptionResNetV2: Leverage transfer learning with the pre-trained InceptionResNetV2 model on the ImageNet dataset to classify images efficiently.
10. Military Aircraft (Satellite) Imagery Classification using Deep Learning (Custom Datasets): Classify images of military aircraft (F-22 Raptor, B-52, A-10, etc.) using Deep Learning algorithms. Learn to create and utilize custom datasets for model training.
11. Sound Signal Processing for Deep Learning using Python (Custom Datasets) (Part - 1/2): Prepare audio files for Deep Learning by processing sound signals using Mel-Frequency Cepstral Coefficients (MFCC). This project is a precursor to the sound classification project.
12. Sound Classification using Deep Learning (Part - 2/2): Build a Convolutional Neural Network (CNN) architecture with three hidden layers to classify sound signals using TensorFlow and Keras, utilizing the pre-processed audio data from the previous project.

Project Implementation:

Each project is implemented in Python using Jupyter Notebook. Python source code for all projects is included in the relevant Udemy course sections, allowing you to download and explore the code.

Start your AI journey today!