Course Content
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Introduction to Data Science This is an overall introduction about Artificial Intelligence, Machine Learning and Data Science
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Python for Data Science
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Descriptive Statistics
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Introduction to Pandas
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Introduction to Numpy
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Data Distribution
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Inferential Statistics
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Statistics in Machine Learning
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Bi-variate Covariance and Correlation
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Bi-variate Chi-square Test
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Bi-variate Anova Test
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Linear Regression Assumption
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Multiple Linear Regression
- Data Science Model Building Process
- Introduction to Multiple Linear Regression
- Exploratory Data Analysis (EDA) Quality issues
- Exploratory Data Analysis (EDA) Fixing Nulls
- Exploratory Data Analysis (EDA) Label Encoding
- Multiple Linear Regression Model Building
- Learning Curve
- Bias-Variance Trade Off
- Code Files
- Quiz-13
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Polynomial Regression
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Normal Equation Solver
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EDA and Model Building Pipeline
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Gradient Descent
- Gradient Descent Explanation
- Gradient Descent Logits
- Gradient Descent Math
- Gradient Descent Convergence and Divergence
- Issues with Gradient Descent
- Stochastic Gradient Descent (SGD) Introduction
- SGD Gradient Calculation
- Understanding SGD Intuitives
- SGD scikitlearn Tuning
- SGD Finetuning Grid Search
- Code Files
- Quiz-17
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Regularization and Feature Selection
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Classification-Logistic Regression - I
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Logistic Regression - II
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Classification - Multi Class
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K Nearest Neighbor (KNN)
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Classification - Multi Label Output
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Support Vector Machine (SVM)
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Linear Algebra - III
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Dimensionality Reduction
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Clustering
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Decision Tree
- Introduction to Decision Trees
- Decision Tree Classification Impurity Measures
- Decision Tree Building Prediction
- Overfitting Problem
- Pro & Cons of Feature Selection Approach
- Decision Tree Pruning and Conclusion
- Decision Tree Classifier SciKit Learn
- Decision Tree Regressor Introduction
- Decision Tree Regressor Recursive Binary Split
- Decision Tree Regression Scikit Learn
- Code Files
- Quiz-28
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Ensemble Models
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Anomaly Detection
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Natural Language Processing (NLP)
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