What is Machine Learning (ML)?

How machines learn

Types of learning: Supervised, Semi-supervised, Unsupervised, Reinforcement.

Basics of Classification, Regression and Clustering algorithms

Creating your first Prediction Model

Training & Model Evaluation

Choosing Machine Learning Algorithm

Operators, Operands and Expressions

Python Data Types

Conditional statements in Python

Loops in Python

Lists and dictionaries and Tuples

Programming practice in Python

Iterators & Generators

File Handling in Python

Modules and Libraries

Classes and Objects

String Formatting in Python

Decorators, Context Managers, Regular Expressions

List and Dictionary Comprehensions

Lambda and Argument Passing

Multiple Inheritance

Introduction to Numpy

Arrays, Matrices

Various operations on arrays and matrices

Introduction to Pandas

Reading csv and matlab files

Data frame object manipulation in python

Various operations on data frame

Visualization using Matplotlib

Scatter plots, line plots etc on a given data

Advance visualization using Seaborn

Histograms, heatmaps, box plots etc using seaborn

Basic Functionalities of a data object

Merging of Data objects

Concatenation of data objects

Types of Joins on data objects

Exploring a Dataset

Analysing a dataset

Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples() operations

Aggregation

Concatenation

Merging

Joining

Data Collection &Preparation

Data Mugging

Outlier Analysis

Missing value treatment

Feature Engineering

Data Transformation

Normalization vs Standardization

Creating Dummies

Principal Component Analysis

Confidence Interval

Student’s t distribution

Binomial Distribution

A/B Testing

Hypothesis Testing

t-Tests

ANOVA

Chi-square test

KNN

PCA

Categorical Variables

R Square

Linear & Logistic and Regression Techniques

Problem of Collinearity

WOE and IV

Residual Analysis

Heteroscedasticity

Homoscedasticity

Supervised Machine Learning algorithms

Linear Regression o Multi Feature

Logistic Regression

2 Class and Multi class

Decision/ Classification Trees Ensemble Models Bagging Boosting Random Forest

K-Nearest Neighbours (KNN)

Naive Bayes

Introduction to Neural Network (DeepLearning)

Feed Forward Neural Network

Forward Propagation

Backward Propagation

Support Vector Machine

Clustering with K-means Clustering

Bias-Variance Trade off

Regularization

Parameter tuning & grid search optimization.

5000+ Handwritten Digit Recognition Problem

4000+ email spam detection problem

Image compression Problem

Flower species classification problem

Titanic Survivor classification problem from kaggle

Fifa ranking dataset from kaggle

Profit Prediction Problem

Business Case of whether a chip will be accepted or not

Business case of clustering from dataset

Wine classification dataset and problem from Kaggle

Variety of Problems from Kaggle

Competition Data Set