Foundations for Machine Learning

Linear Algebra Probability Statistics Calculus Programming Optimization

Curated by: Vizuara (39 videos)

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Tracks in this Playlist

• Foundations for Machine Learning | Linear Algebra, Probability, Calculus, Optimization [Lecture 1]
• Foundations for Machine Learning | Linear Algebra | Vector, Transformation, Span, Basis [Lecture 2]
• Foundations for ML | Linear Algebra | Linear transformation as Matrix multiplication [Lecture 3]
• Foundations for ML | Linear Algebra | Product of 2 matrices = Composite Transformation [Lecture 5]
• Foundations for Machine Learning | Linear Algebra | 3D linear transformation [Lecture 4]
• Foundations for Machine Learning | A simple physical intuition for determinants [Lecture 6]
• Foundations for ML | Linear Algebra | Transformation with non-square matrices 2D to 3D [Lecture 7]
• Foundations for ML | Linear Algebra | Transformation with non-square matrices 2D to 3D [Lecture 7]
• Foundations for Machine Learning | Matrix Inverse - Physical Meaning in Transformations [Lecture 8]
• Foundations for ML | Linear Algebra | Relation between dot product and transformation [Lecture 9]
• Foundations for ML | Simple intuition of eigenvalues and eigenvectors | Linear Algebra [Lecture 10]
• Foundations for Machine Learning | Probability and Statistics | An introduction [Lecture 11]
• Foundations for Machine Learning | Conditional probability | Probability & Statistics [Lecture 12]
• Foundations for Machine Learning | Bayes Theorem - Intuition and basics [Lecture 13]
• Foundations for Machine Learning | Probability Distributions [Lecture 14]
• Foundations for Machine Learning | Null & Alternate hypothesis in probability [Lecture 15]
• Foundations for ML | Naive-Bayes classification, ML model evaluation | confusion matrix [Lecture 16]
• Introduction to Calculus for Machine Learning | Foundations for ML [Lecture 17]
• Chain rule for Machine Learning | Calculus for ML | Mathematical Foundations for ML [Lecture 18]
• Integral calculus for Machine Learning | Mathematical foundations for ML [Lecture 19]
• Partial Derivatives and Gradient Descent: The Engine Driving ML | ML foundations [Lecture 20]
• Gradient descent in machine learning [Lecture 21]
• Introduction to Optimization for Machine Learning [Lecture 22]
• Stochastic Gradient Descent from scratch | Intro to Optimization | Foundations for ML [Lecture 23]
• Momentum-based gradient descent from scratch: optimization | Foundations for ML [Lecture 24]
• RMSprop Gradient Descent from scratch | Optimization in ML | Foundations for ML [Lecture 25]
• Adam Optimizer from scratch | Gradient descent made better | Foundations for ML [Lecture 26]
• Regularization in ML explained simply | Lasso (L1) and Ridge (L2) | Foundations for ML [Lecture 27]
• Basics of Python [beginners only] | Foundations for ML [Lecture 28]
• Matrix multiplication from scratch in Python | No NumPy, no libraries | Coding for ML [Lecture 29]
• Introduction to classes in Python for beginners [Lecture 30]
• Introduction to Python Classes & Objects in the context of ML [Lecture 31]
• Introduction to NumPy in Python | Programming foundations for Machine Learning [Lecture 32]
• Pandas in Python for ML and Data Science: A comprehensive introduction for beginners [Lecture 33]
• Intro to data visualization libraries for ML in Python - Matplotlib, Seaborn, Plotly
• Introduction to Scikit-learn in Python | Foundations for Machine Learning
• Intro to Python Deep Learning libraries- Tensorflow, Keras, PyTorch | Programming foundations for ML
• Machine Learning Algorithms Overview - What all exist out there?
• Where is AI headed? Evolution, current trends and job opportunities