What is Data?

What is Data Science?

What does Data Sc. Involve?

Tools of data Sc.

What is Machine Learning?

Where is Machine Learning used?

Job Roles

Introduction to probability

Sampling and sampling distributions

Unions of Events and Addition Rules

Unions of Events and Addition Rules

Conditional Probability

Independence

Random Variables

Baye'S Theorem

Average, Mean, Median, Mode

Range, Quartiles & Percentiles

Interquartiles Range

R-SQUARED

Variance & Standard Deviation

Statistical Inference

Normal Distribution

Standard Normal Distribution

Poisson Distribution

Bernoulli Distribution

Point Estimate

Confidence level & Confidence Intervals

Margin of Error

Population Means

Hypothesis Testing

Hypothesis Testing : Two Sample Test

Hypothesis Testing Proportion & Mean

Z- Test & T-Test

One-tailed and two-tailed tests

The F distribution

The chi-square distribution

The chi-square test of independence

Introduction to Python

Computer Programming Data Types

Variables & Data Types

Basic Input-Output Operations

Basic Operators

Boolean Values

Strings

Lists & Tuples

Dictionary

Sets

Conditional Expressions

Loops

Logical and Bitwise Operations

Functions & Recursions

File Input & Output

Exceptions handling

Object Oriented Programming

Virtual environment & Python libraries

Some special functions in python e.g Lambda, bin, format, map, filter, reduce

Web scraping

Ipython

Jupyter

NumPy Array

Sorting Array

NumPy Universal Functions

Array Indexing: Accessing Single Elements

Array Slicing: Accessing Subarrays

Reshaping of Arrays

Array Concatenation and Splitting

NumPy Arrays: Universal Functions

Aggregations: Min, Max, and Everything in Between

Sorting Arrays

Fast Sorting in NumPy: np.sort and np.argsort

Partial Sorts: Partitioning

Structured Data: NumPy’s Structured Arrays

Pandas Series Object

Pandas DataFrame Object

Pandas Index Object

Data Indexing and Selection

Operating on Data in Pandas

Handling Missing Data

Hierarchical Indexing

Combining Datasets: Concat and Append

Combining Datasets: Merge and Join

Aggregation and Grouping

Pivot Tables

Vectorized String Operations

Working with Time Series

Motivating query() and eval(): Compound Expressions

pandas.eval() for Efficient Operations

DataFrame.eval() for Column-Wise Operations

DataFrame.query() Method

4. Visualization with Matplotlib

Line Plots

Scatter Plots

Scatter Plots with plt.scatter

Visualizing Errors

Density and Contour Plots

Histograms, Binnings, and Density

Customizing Plot Legends, Colorbars

Multiple Subplots

Text and Annotation

Configurations and Stylesheets

Three-Dimensional Plotting in Matplotlib

Basemap

Visualization with Seaborn

Matplotlib Vs. Seaborn

Introduction

Types of Machine Learning

Supervised Vs. Unsupervised Learning

Instance-Based Vs. Model-Based Learning

Insufficient quantity of Training Data

Nonrepresentative Training Data

Poor-Quality Data

Irrelevant Features

Overfitting the Training Data

Underfitting the Training Data

Supervised Learning & Types

Classifiction & Types

Regression & Types

Acuracy Metrics

Training a Binary Classifier

Performance Measures

Measuring Accuracy Using Cross-Validation

Confusion Matrix

Precision and Recall

Multiclass Classification

Error Analysis

Multilabel Classification

Multioutput Classification

Projection & Manifold Learning

Principal Component Analysis

Data wrangling

Gradient Descent

Polynomial Regression

Learning Curves

Ridge, Lasso Regression

Elastic Net & Early Stopping

Logistic Regression

Training and Cost Function

Sigmoid Probability

Accuracy Matrix

Decision Boundaries

Linear SVM & Classification

SVM : Linear Separability

SVM : Mathematical Representation

Nonlinear SVM & Classification

SVM Regression

Kernal Trick

Decision Function Classifier

Overfitting

Random Forest Classifier

Performance Measure Confusion Matrix & Cost Matrix

Estimating Class Probabilities

The CART Training Algorithm

Gini Impurity or Entropy?

Regularization Hyperparameters

Instability

Overview & Applications

Types of Unsupervised Learning

Overview

Hierarchical Clustering

Ensemble Learning

Bagging and Pasting with Scikit-Learn

Out-of-Bag Evaluation

Random Patches and Random Subspaces

Random Forests

AdaBoost

Gradient Boosting

XGBoost & Parameters

Model Selection

Overview of Time Series Modeling

Time Series Patterns & Types

White Noise

Stationarity

Removal of Non-Stationarity

Time Series Models

Purpose of Recommender Systems

Paradigms of Recommender Systems

Collaborative Filtering

Association Rule Mining

Text Mining: Overview & Significance

Application

Text Extraction & Preprocessing

Introduction

Why Tensorflow

Installation

Node Value

Linear Regression with Tensorflow

Gradient Descent

Training Machine Learning Program with Tensorflow API

Training Deep Neural Nets

Distributing Tensorflow across servers

Convolutional Neural Network

Recurrent Neural Network

Autoincoders

Reinforcement Learning

Quantitative Reasoning

Logical Reasoning 

 Critical Reasoning

Integrated Reasoning

Verbal Reasoning

 Integrated Reasoning

Case Studies

Capstone Projects

Resume building

Email etiquette

​PowerPoint Presentation

Telephone etiquette

Linkedin profile building

GitHub profile making

Mock Interviews