#### DATA SCIENCE WITH MACHINE LEARNING

25000
2y ago
Course Duration:3 Months

#### Course Details

1. What is Data Science

• Demand of Data Science

• Venn Diagram

• Pipeline

• Roles

• Team

• Knowledge Check

2. Field of study
• Big Data overview

• Programming involvement in Data Science

• Statistics

• Knowledge check

3. Ethics
• Ethical issues

• Knowledge check

4. Data Sources (Getting Data)
• Data Metrics

• Existing data

• APIs

• Scraping

• Creating Data

• Knowledge check

5. Data Exploration (Cleaning Data)
• Exploratory graphs

• Exploratory statistics

• Knowledge check

6. Programming

• R programming

• Python

• SQL

• Web formats

• Knowledge check

7.Mathematics
• Algebra

• Systems of equations

• Calculus

• Big O

• Bayes probability

• Knowledge check

8. Applied Statistics
• Hypothesis

• Confidence

• Problems

• Validating

• Knowledge check

9. Machine Learning
• Linear Regression with one and multiple variables.

Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning.

• Cost function

• Normal Equations

• Logistic regression. What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features.

• Cost Function

• Neural Networks. Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech;

• Back propagation

• Application of Neural Network

• Support Vector Machines (SVM). Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and discuss how to use it in practice.

• Large Margin classification

• Kernels

• UNSUPERVISED

• Clustering

• Gaussian Mixture Models

• HMM

10. R Programming
• Writing code and setting your working directory

• Getting started and R nuts and Bolts

• R console Input and evaluation

• Data types – R Objects and attributes

• Data types – Vectors and Lists

• Data types – Matrices

• Data types – Factors

• Data types – Missing values

• Data types – Data frames

• Data types – Names Attributes

• Data types – summary

• Textual data formats

• Connections: Interfaces to outside world

• Subsettings – Basics

• Subsettings – Lists

• Subsettings – Matrices

• Subsettings – Partial Matching

• Subsettings – Removing Missing values

• Vectorized Operations

10. Communicating
• Interpretability

• Actionable insights

• Visualization for presentation

• Reproducible research

• Knowledge check

Conclusion and final test

• Are you providing Training Classes
IT Courses / Govt Exam Preparation