easylearn.ing
Easy Learning with Machine Learning & AI Foundations Course
Development > Data Science
2h 42m
£14.99 Free
4.5 ★★★★★

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Language: English

Master Artificial Intelligence & Machine Learning: From Zero to Hero

What you will learn:

  • Master the core principles of Artificial Intelligence and Machine Learning
  • Apply advanced mathematical and statistical concepts
  • Develop and evaluate diverse machine learning models
  • Implement and understand deep learning algorithms
  • Navigate and address the ethical dilemmas of AI
  • Gain hands-on experience with industry-standard AI tools

Description

Unlock the power of Artificial Intelligence and Machine Learning! This comprehensive course provides a step-by-step journey from foundational concepts to practical applications. We'll demystify AI, explore its rich history, and delve into real-world examples across diverse sectors like healthcare, finance, and transportation. You'll build a strong understanding of core machine learning principles, including supervised, unsupervised, and reinforcement learning, complemented by essential mathematical concepts like linear algebra and probability.

Learn to build and evaluate various machine learning models using Python libraries such as NumPy, Pandas, and Scikit-learn. We'll cover key algorithms including linear and polynomial regression, logistic regression, decision trees, support vector machines, and k-Nearest Neighbors. You'll also explore the exciting world of deep learning, mastering neural networks, CNNs, and RNNs, and their applications in image recognition and natural language processing.

Beyond the technical aspects, this course emphasizes the critical importance of ethical considerations in AI development. We will discuss bias, fairness, accountability, privacy, and security, ensuring you become a responsible and ethical AI practitioner. Through hands-on labs and quizzes, you'll gain the practical experience needed to confidently apply your newfound skills. By the end of this program, you'll possess the knowledge and practical skills to confidently pursue a career in data science, machine learning engineering, or AI development.

Start your AI and ML journey today!

Curriculum

Introduction to AI & ML

This introductory section lays the groundwork for your AI and ML journey. You'll explore the definition of Artificial Intelligence, tracing its historical evolution and examining its transformative impact across various industries. You'll differentiate between AI, Machine Learning, and Deep Learning, concluding with a hands-on lab to kickstart your practical experience. Lectures cover: What is AI?, History & Evolution of AI, Real-World AI Applications, AI vs ML vs Deep Learning, and a foundational hands-on lab.

Machine Learning Fundamentals

Here, you'll delve into the core concepts of machine learning. You will learn about various types of machine learning, essential concepts, and data preprocessing techniques. Crucially, you will learn how to evaluate model performance using relevant metrics. You'll also gain practical skills in addressing bias, variance, and model evaluation through hands-on labs, concluding with a quiz to test your understanding. Lectures cover: Types of Machine Learning, Key ML Concepts, Data Preprocessing, Evaluation Metrics, a hands-on bias/variance lab, and a quiz.

Regression and Classification Models

This section focuses on regression and classification techniques. You'll master linear and polynomial regression, logistic regression, and delve into advanced algorithms like decision trees, random forests, support vector machines, and k-Nearest Neighbors. Hands-on labs reinforce your learning, and a comprehensive quiz assesses your understanding. Lectures cover: Linear & Polynomial Regression, Logistic Regression & Classification, Decision Trees & Random Forests, Support Vector Machines (SVMs), k-Nearest Neighbors (kNN), a hands-on lab, and a quiz.

Unsupervised Learning Techniques

Explore the world of unsupervised learning, covering clustering algorithms (k-means, hierarchical, DBSCAN), dimensionality reduction techniques (PCA, t-SNE), and association rule mining (Apriori, FP-Growth). Practical experience is gained through a hands-on lab focusing on clustering and PCA. Lectures cover: Clustering (k-Means, Hierarchical, DBSCAN), Dimensionality Reduction (PCA, t-SNE), Association Rule Mining (Apriori, FP-Growth), and a hands-on lab.

Neural Networks and Deep Learning

This section introduces you to the fundamentals of neural networks and deep learning. You will learn about activation functions, backpropagation, gradient descent, and explore convolutional neural networks (CNNs) and recurrent neural networks (RNNs) and LSTMs. A hands-on lab guides you through building your first neural network using MNIST, followed by a comprehensive quiz. Lectures cover: Introduction to Neural Networks, Activation Functions, Backpropagation & Gradient Descent, CNNs, RNNs & LSTMs, a hands-on lab, and a quiz.

Reinforcement Learning

Delve into the principles of reinforcement learning, including Markov Decision Processes (MDPs), Q-learning, and deep reinforcement learning (DQN, Policy Gradient). A hands-on lab provides practical experience with CartPole. Lectures cover: Basics of Reinforcement Learning, Markov Decision Processes (MDP), Q-Learning, Deep Reinforcement Learning (DQN, Policy Gradient), and a hands-on lab.

Natural Language Processing (NLP)

Explore the field of natural language processing (NLP), covering text processing, feature extraction, language models, sentiment analysis, and machine translation. A hands-on lab focuses on sentiment analysis, and a quiz solidifies your understanding. Lectures cover: Introduction to NLP, Text Processing & Feature Extraction, Language Models, Sentiment Analysis, Machine Translation, a hands-on lab, and a quiz.

Computer Vision

This section introduces you to computer vision, covering image processing techniques, object detection, image classification, and image segmentation. You'll delve deeper into convolutional neural networks (CNNs) and gain practical skills through a hands-on lab. Lectures cover: Introduction to Computer Vision, Image Processing Techniques, Object Detection, Image Classification, Image Segmentation, CNNs, and a hands-on lab.

Ethics and the Future of AI

This final section addresses the crucial ethical considerations in AI. You'll examine bias and fairness, privacy and security concerns, and develop strategies for responsible AI development. A hands-on lab focuses on detecting bias in machine learning models. Lectures cover: Ethical Considerations in AI, Bias and Fairness in AI, Privacy and Security in AI, and a hands-on lab.

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