Course Features
- Lectures 142
- Quizzes 0
- Duration 23 hours
- Skill level Expert
- Language English
- Students 297
- Assessments Yes
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Introduction to Python for Data Science & Machine Learning from A-Z
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Data Science & Machine Learning Concepts
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Python For Data Science
- What is Programming?
- Why Python for Data Science?
- What is Jupyter?
- What is Google Colab?
- Python Variables, Booleans
- Jupyter Notebook
- Getting Started with Google Colab
- Python Operators
- Python Numbers & Booleans
- Python Strings
- Python Conditional Statements
- Python For Loops and While Loops
- Python Lists
- More about Lists
- Python Tuples
- Python Dictionaries
- Python Sets
- Compound Data Types & When to use each one?
- Python Functions
- Object-Oriented Programming in Python
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Statistics for Data Science
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Probability and Hypothesis Testing
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NumPy Data Analysis
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Pandas Data Analysis
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Python Data Visualization
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Introduction to Machine Learning
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Data Loading & Exploration
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Data Cleaning
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Feature Selecting and Engineering
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Linear and Logistic Regression
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K Nearest Neighbors
- KNN Overview
- Parametric vs non-parametric models
- EDA on Iris Dataset
- The KNN Intuition
- Implement the KNN algorithm from scratch
- Compare the result with the Sklearn Library
- Hyperparameter tuning using the cross-validation
- The decision boundary visualization
- Manhattan vs Euclidean Distance
- Feature scaling in KNN
- Curse of dimensionality
- KNN use cases
- KNN pros and cons
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Decision Trees
- Decision Trees Section Overview
- EDA on Adult Dataset
- What is Entropy and Information Gain?
- The Decision Tree ID3 algorithm from scratch Part 1
- The Decision Tree ID3 algorithm from scratch Part 2
- The Decision Tree ID3 algorithm from scratch Part 3
- ID3 – Putting Everything Together
- Evaluating our ID3 implementation
- Compare with Sklearn implementation
- Visualizing the tree
- Plot the Important Features
- Decision Trees Hyper-parameters
- Pruning
- [Optional] Gain Ration
- Decision Trees Pros and Cons
- [Project] Predict whether income exceeds $50K/yr – Overview
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Ensemble Learning and Random Forests
- Ensemble Learning Section Overview
- What is Ensemble Learning?
- What is Bootstrap Sampling?
- What is Bagging?
- Out-of-Bag Error (OOB Error)
- Implementing Random Forests from scratch Part 1
- Implementing Random Forests from scratch Part 2
- Compare with sklearn implementation
- Random Forests Hyper-Parameters
- Random Forests Pros and Cons
- What is Boosting?
- AdaBoost Part 1
- AdaBoost Part 2
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Support Vector Machines
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K-Means
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PCA
- PCA Section Overview
- What is PCA?
- PCA Drawbacks
- PCA Algorithm Steps (Mathematics)
- Covariance Matrix vs SVD
- PCA – Main Applications
- PCA – Image Compression
- PCA Data Preprocessing
- PCA – Biplot and the Screen Plot
- PCA – Feature Scaling and Screen Plot
- PCA – Supervised vs Unsupervised
- PCA – Visualization
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Data Science Career
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Additional Resources