easylearn.ing
Easy Learning with Building AI Projects Machine Learning & Deep Learning
Development > Data Science
3 h
£19.99 £12.99
4.1 ★★★★★
6232 students

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Language: English

Master AI: Hands-On Machine Learning & Deep Learning Projects

What you will learn:

  • Master Python programming for AI applications.
  • Grasp core Machine Learning and Deep Learning concepts.
  • Build and deploy 5 diverse real-world AI projects.
  • Develop expertise in various AI techniques: image classification, natural language processing, time-series forecasting, and more.
  • Gain hands-on experience with popular AI/ML libraries and frameworks.
  • Learn model deployment and scaling strategies.
  • Understand and implement various model evaluation metrics.
  • 25+ hours of comprehensive video content and downloadable project materials.
  • Become proficient in EDA and feature engineering.
  • Enhance your portfolio with demonstrable AI/ML skills.

Description

Course Description:

Unlock the power of Artificial Intelligence with our project-based course! Learn to build practical Machine Learning and Deep Learning applications from the ground up. Whether you're a complete novice or an experienced developer seeking to expand your skills, this immersive program is perfect for you.

This course provides a dynamic learning journey, guiding you through the creation and deployment of several real-world AI projects. We start with fundamental concepts and gradually progress to advanced techniques, ensuring a smooth learning curve for everyone. You'll gain hands-on experience with a wide variety of applications, including image recognition, natural language processing, and predictive modeling, and explore deployment strategies to showcase your skills.

Key Features:

- Project-Driven Approach: Learn by doing! Each module focuses on a complete project, providing practical application of theoretical knowledge.

- Comprehensive Curriculum: From foundational concepts to cutting-edge Deep Learning techniques, we cover everything you need to succeed.

- Diverse Applications: Explore diverse real-world scenarios, including image classification, natural language processing, time-series forecasting, and more.

- Deep Dive into Deep Learning: Master neural networks, convolutional networks, and recurrent networks, and apply them to complex problems.

- Deployment and Scaling: Learn to deploy your models effectively and scale them for broader application. We'll explore cloud-based solutions.

- Supportive Community: Collaborate with fellow learners, share ideas, and receive support from experienced instructors.

- Expert Guidance: Benefit from mentorship and feedback from leading AI professionals.

By the end of this course, you'll have a strong AI portfolio demonstrating your ability to build and deploy intelligent systems. Prepare to launch your career in AI, data science, or enhance your existing projects with powerful AI capabilities. Enroll now and start building the future of AI, one project at a time!

Curriculum

Project 1: Flight Fare Prediction

This project begins with an introduction to problem definition and exploratory data analysis (EDA). You'll then delve into feature engineering, applying classical machine learning models to predict flight fares. Finally, learn to deploy your model using the Flask framework, making your prediction system accessible.

Project 2: Mushroom Classification

Here, you'll explore classification techniques and conduct EDA on a mushroom dataset. You'll build a benchmark model for mushroom classification and learn how to evaluate its performance, gaining crucial skills in predictive modeling.

Project 3: Nursery School Application Classification

This project introduces you to a nursery school application dataset and EDA techniques. You'll build and evaluate classification models such as Logistic Regression, Support Vector Machines (SVM), and Decision Trees, mastering crucial evaluation metrics.

Project 4: Toxic Comment Classification

This project tackles the challenge of toxic comment classification. You’ll perform EDA, utilize Natural Language Processing (NLP) techniques like tokenizing sequences for visualization, and refine models like Naive Bayes, SVM, and Logistic Regression through feature weighting, gaining experience in text analysis.

Project 5: UK Road Accident Time Series Forecasting

This project focuses on time series forecasting using a UK road accident dataset. You'll perform EDA and then forecast accident rates based on the number of casualties, employing models like SARIMA, Facebook Prophet (FbP), and Long Short-Term Memory networks (LSTMs), mastering time series analysis techniques.