Module 3 – Python with Data Science

Extract Transform Load

• Web Scraping, Interacting with APIs

Data Handling with NumPy

• NumPy Arrays, CRUD Operations, etc.
• Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.

Data Manipulation Using Pandas

• Loading the data, data frames, series, CRUD operations, splitting the data, etc.

Data Preprocessing

• Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.
• Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross-validation techniques, train-test split, etc.

Data Visualization

• Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc. with Python matplotlib.
• Regression plots, categorical plots, area plots, etc, with Python seaborn.

Module 4 – Linear Algebra and Advanced Statistics

Descriptive Statistics –

• Measure of central tendency, the measure of spread, five points summary, etc.

Probability

• Probability Distributions, Bayes’ theorem, central limit theorem.

Inferential Statistics –

• Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Module 5 – Machine Learning

Introduction to Machine Learning 

• Supervised, Unsupervised Learning.
• Introduction to scikit-learn, Keras, etc.

Regression 

• Introduction classification problems, Identification of a regression problem, dependent and independent variables.
• How to train the model in a regression problem.
• How to evaluate the model for a regression problem.
• How to optimize the efficiency of the regression model.

Classification 

• Introduction to classification problems, Identification of a classification problem, and dependent and independent variables.
• How to train the model in a classification problem.
• How to evaluate the model for a classification problem.
• How to optimize the efficiency of the classification model.

Clustering 

• Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
• How to train the model in a clustering problem.
• How to evaluate the model for a clustering problem.
• How to optimize the efficiency of the clustering model.

Module 6 – Supervised and Unsupervised Learning in ML

Supervised Learning 

• Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
• Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.
• Decision Tree – Creating decision tree models on classification problems in a tree-like format with optimal solutions.
• Random Forest – Creating random forest models for classification problems in a supervised learning approach.
• Support Vector Machine – SVM or support vector machines for regression and classification problems.
• Gradient Descent – The gradient descent algorithm is an iterative optimization approach to finding the local minimum and maximum of a given function.
• K-Nearest Neighbors – A simple algorithm that can be used for classification problems.
• Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.

Unsupervised Learning 

• K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
• Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.
• Linear Discriminant Analysis –  LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
• Principal Component Analysis – PCA follows the same approach in handling multidimensional data.

Module 7 – Performance Metrics

• Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
• Confusion matrix – To evaluate the true positive/negative, and false positive/negative outcomes in the model.
• r2, adjusted r2, mean squared error, etc.