Machine Learning Course

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.

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.

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.

Supervised Learning 

• Linear Regression
• Logistic Regression
• Decision Tree
• Random Forest
• Support Vector Machine
• Gradient Descent
• K-Nearest Neighbors
• Time Series Forecasting

Unsupervised Learning 

• K-means
• Dimensionality reduction
• Linear Discriminant Analysis
• Principal Component Analysis

Artificial Intelligence Basics 

• Introduction to keras API and TensorFlow

Neural Networks

• Neural networks
• Multi-layered Neural Networks
• Artificial Neural Networks 

Deep Learning

• Introduction to Deep Learning
• Deep neural networks
• Convolutional Neural Networks 
• Recurrent Neural Networks
• GPU in deep learning
• Autoencoders, Restricted Boltzmann machine

The Data Science capstone project focuses on establishing a stronghold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals:

• Extracting, loading and transforming data into usable format to gather insights. 
• Data manipulation and handling to pre-process the data.
• Feature engineering and scaling the data for various problem statements. 

Model selection and model building on various classification, and regression problems using supervised/unsupervised Machine Learning algorithms.

• Assessment and monitoring of the model created using the Machine Learning models.