Predictive Analytics and Machine Learning with Python
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Intro to Data Science
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What is Data Science
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Roles and Responsibilities of a Data Scientist
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Life cycle of Data Science project
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| Tools and Technologies used |
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Statistics
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Fundamentals of Mathematics and Probability
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Sampling Theory
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Descriptive Statistics
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| Inferential Statistics |
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Python Programming
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Types of Operators
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Data Types
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Flow Controls
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Functions
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| List Compressors |
| Numpy Library for Data Analysis |
| Pandas Library for Data Analysis |
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Data Visualization with Python
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Matplotlib library
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Seaborn library
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Pandas Built-in data Visualization
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Data handling and Data Manipulations with Pandas
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Data Pre-processing
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Sanity Checks
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Missing value detection and treatment
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Outliers detection and treatment
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| Variable transformation techniques |
| Exploratory data analysis |
| Uni-variate & Bi-variate analysis |
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Machine Learning Algorithms
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Supervised Learning Algorithms
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Linear Regression
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Logistic Regression
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| Decision Tree and Random Forest |
| Support Vector Machine |
| KNN |
| Naïve Bayes |
| Unsupervised Learning Algorithms |
| K Means Clustering |
| Hierarchical Clustering |
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Dimensionality Reduction
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PCA – Principal Component Analysis
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LDA – Linear Discriminant Analysis
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Other Topics
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XG Boosting
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K-fold cross validation
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Stratified cross-validation
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