What you'll learn

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✅ Fundamentals of Machine Learning – Understand AI, ML, and Deep Learning
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✅ Python for ML – Data handling with NumPy, Pandas, and visualization with Matplotlib
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✅ Supervised Learning – Master Regression and Classification (Linear Regression, Decision Trees, SVM, etc.)
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✅ Unsupervised Learning – Learn Clustering (K-Means, Hierarchical Clustering) and Dimensionality Reduction (PCA, t-SNE)
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✅ Model Evaluation & Hyperparameter Tuning – Improve model performance with Grid Search and Cross-Validation
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✅ Deep Learning & Neural Networks – Build and train Artificial Neural Networks (ANNs, CNNs, RNNs) using TensorFlow and Keras
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✅ Natural Language Processing (NLP) – Work with text data, sentiment analysis, and chatbot development
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✅ Reinforcement Learning (RL) – Understand Q-Learning, Deep Q-Networks (DQN), and their applications in robotics and gaming
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✅ Real-World Applications of ML – Work on practical AI applications in healthcare, finance, and self-driving cars

Course Curriculum

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10 Lectures

Chapters 10 Curriculum Elements

Chapter 1: Introduction to Machine Learning

Chapter 2: Python for Machine Learning

Chapter 3: Supervised Learning – Regression

Chapter 4: Supervised Learning – Classification

Chapter 5: Unsupervised Learning – Clustering & Dimensionality Reduction

Chapter 6: Model Evaluation & Hyperparameter Tuning

Chapter 7: Neural Networks & Deep Learning

Chapter 8: Natural Language Processing (NLP)

Chapter 9: Computer Vision

Chapter 10: Reinforcement Learning (RL)

Requirements

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✅ Basic Math Knowledge – Understanding of algebra, probability, and basic statistics
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✅ Basic Python Skills – Some familiarity with Python syntax (loops, functions, lists, etc.)
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✅ No Prior ML Experience Needed! – This course starts from the basics and gradually moves to advanced concepts

Description

Machine Learning (ML) is revolutionizing industries by enabling computers to learn from data and make intelligent decisions. From self-driving cars and healthcare diagnostics to recommendation systems and AI assistants, ML is at the core of modern innovation.

This comprehensive course takes you from the fundamentals of ML to advanced AI applications. You’ll start by understanding core ML concepts, setting up Python for ML, and working with Supervised Learning (Regression & Classification) models. You’ll then dive into Unsupervised Learning, Neural Networks & Deep Learning, Natural Language Processing (NLP), and Reinforcement Learning (RL). Finally, you’ll explore real-world applications of ML in finance, healthcare, and autonomous systems.

Throughout this course, you’ll work on hands-on projects and real datasets, learning to build and fine-tune ML models using Python, Scikit-Learn, TensorFlow, and Keras.

By the end of this course, you’ll have the skills to build your own AI-powered applications and confidently apply ML techniques to real-world problems.

Instructors

SP

Shivam Pandey
Digital Marketing

(3.67)

  156 Courses

  25 Students

  3 Reviews