Course Content
Slides From All Lectures & Access to Our Discussion Group
Access to Our Exclusive Q&A and Discussion Group
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Slides From All Lectures
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Machine Learning Foundations
Lecture 2: Statistics and Probability for Machine Leaning
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Practice Notebook 1: Introduction to Python for Machine and Deep learning
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Introduction to Python
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Machine Learning Terms and Algorithms
Lecture 3: Linear and Polynomial Regression and solving general problems
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Lecture 4: Gradient Descent variations & other optimizers (Momentum, RMS Prop & ADAM)
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Practice Notebook 2: NumPy, Vectorization, Data Visualization & OOP
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Vectorization, Data Visualization and OOP Walkthrough
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Lecture 5: Perceptron and Adaline
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Lecture 6: Logistic Regression, Multilayer perceptron (MLP), Deep Nets
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Practice Notebook 3: Regression on real data (+20,000 houses information) and Optimizers
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Building a Solid Machine Learning Pipeline
Lecture 7: Pre-processing (handling missing values, outliers, z-scoring) & Cross validation
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Lecture 8: Overfitting, Covariate shifts, Multicollinearity, Regularization, LR schedulers
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Practice Notebook 4: Classification with Perceptron, Adaline, logistic & DeepNNs on real data
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Lecture 9: Evaluation Metrics(MSE, Confusion matrix etc.) & Hypothesis Testing(ttest, etc.)
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Lecture 10: Dimensionality Reduction (PCA etc.), Feature Selection (SFS), Autoencoders
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Practice Notebook 5: Pre-processing, Multicollinearity, Cross-validation, Covariate shifts, LR schedulers
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VS Code واستخدام Notebooks الخروج من الـ
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