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Master Course : Fundamentals of Machine Learning (101 level)
Rating: 4.1 out of 5(328 ratings)
23,723 students

Master Course : Fundamentals of Machine Learning (101 level)

Machine Learning, Supervised Machine Learning, Unsupervised Machine Learning, Deep Learning, TensorFlow, Keras, NLP
Created byDr. José Prabhu J
Last updated 7/2025
English

What you'll learn

  • Understand the fundamental principles of data preprocessing and supervised learning techniques.
  • Apply unsupervised learning methods and evaluate model performance effectively.
  • Implement feature engineering strategies to enhance machine learning model accuracy.
  • Build and train deep learning models using TensorFlow and Keras frameworks.
  • Develop natural language processing (NLP) solutions for text-based applications.
  • Analyze and interpret computer vision models and reinforcement learning algorithms.
  • Evaluate ethical considerations in the development and deployment of AI systems.
  • Explore advanced AI techniques such as generative models and transfer learning in real-world scenarios.

Course content

3 sections • 7 lectures • 1h 51m total length

  • Foundations of Machine Learning: Preprocessing, Supervised Learning, and Beyond19:42

    Explore the foundations of machine learning, including preprocessing techniques, supervised learning, and beyond, with real-world applications, and compare regression and classification, and review key algorithms and evaluation metrics.

  • Mastering Machine Learning: Unsupervised Techniques, Model Evaluation, and More11:18

    Explore unsupervised learning to uncover hidden patterns with clustering, dimensionality reduction, and anomaly detection, and master model evaluation through train-test splits, cross-validation, and hyperparameter tuning.

  • Feature Engineering and Deep Learning: Unlocking the Power of Data11:41

    Explore feature engineering and deep learning to unlock the power of data, covering feature selection, feature extraction, neural networks, backpropagation, text embeddings, and dimensionality reduction techniques.

  • TensorFlow, Keras, and NLP: Building Bridges to Natural Language Understanding11:27

    Explore building and training neural networks with TensorFlow and Keras, and NLP basics from text pre-processing and vectorization to tasks like sentiment analysis, classification, named entity recognition, and translation.

  • Visualizing the Future: Computer Vision, Reinforcement Learning, and Ethical...16:04

    Explore computer vision fundamentals, including image pre-processing, CNNs, and image classification, alongside reinforcement learning basics—MDPs, Q-learning, and DQN—and address bias, privacy, and responsible AI.

  • Model Evaluation and Validation in Data Science and Machine Learning28:43

    Master data science foundations from data collection and cleaning to preprocessing, feature engineering, and modeling; evaluate and validate with cross‑validation, holdout, metrics, and deployment considerations.

Requirements

  • Basic skills and ideas of machine learning and deep learning

Description

This course offers a comprehensive journey through the evolving field of machine learning and artificial intelligence, beginning with the foundational techniques and progressing to advanced methodologies. The first module delves into the essential steps of data preprocessing, supervised learning algorithms, and their real-world applications. As students advance, they will explore unsupervised learning techniques, model evaluation methods, and the critical importance of feature engineering in improving model performance. The course emphasizes the power of deep learning in extracting meaningful insights from complex data, equipping learners with the necessary skills to build cutting-edge machine learning models.

Building on this foundation, the course moves into advanced AI topics, including the use of TensorFlow and Keras for constructing deep learning architectures, and natural language processing (NLP) for enabling machines to understand human language. Students will gain hands-on experience applying these techniques to practical problems, including computer vision and reinforcement learning. Ethical considerations in AI deployment are also discussed, providing students with a holistic understanding of the technology’s societal impact. In the final modules, the course addresses state-of-the-art methods such as generative models, transfer learning, and the future of AI in practice, preparing students to navigate and innovate in the rapidly evolving landscape of artificial intelligence.

In this master course, I would like to teach the major topics:

1. Foundations of Machine Learning: Preprocessing, Supervised Learning, and Beyond

2. Mastering Machine Learning: Unsupervised Techniques, Model Evaluation, and More

3. Feature Engineering and Deep Learning: Unlocking the Power of Data

4. TensorFlow, Keras, and NLP: Building Bridges to Natural Language Understanding

5. Visualizing the Future: Computer Vision, Reinforcement Learning, and Ethical Dilemmas in AI

6. Model Evaluation and Validation in Data Science and Machine Learning

Additional Lectures : 2025

1. Advanced AI Techniques: Generative Models, Transfer Learning, and AI in Practice

Enroll now and learn today !

Who this course is for:

  • All UG and PG Computer Science and Information Technology and Business Systems Domain Students
  • Interested students to learn about the concepts of Fundamentals of Machine Learning (101 level)

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