**Machine learning (ML)** is the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task.

Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.

Machine learning algorithms are used in the applications of email filtering, detection of network intruders, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers.

- Create a firm grip on Python and it's applications in Machine Learning domain
- Understand the various elements of machine learning algorithm like parameters, hyper parameters, loss function and optimization
- Hands-on experience on data manupulation and Machine Learning algorithms

What is Data Science?

What does Data Science involve?

Era of Data Science

Business Intelligence vs Data Science

Life cycle of Data Science

Tools of Data Science

Introduction to Python

**Data Extraction, Wrangling and Visualization**

Data Analysis Pipeline

What is Data Extraction

Types of Data

Raw and Processed Data

Data Wrangling

Exploratory Data Analysis

Visualization of Data

Python Revision (numpy, Pandas, scikit learn, matplotlib)

What is Machine Learning?

Machine Learning Use-Cases

Machine Learning Process Flow

Machine Learning Categories

Linear regression

Gradient descent and more…

**Supervised Learning - I**

What is Classification and its use cases?

What is Decision Tree?

Algorithm for Decision Tree Induction

Creating a Perfect Decision Tree

Confusion Matrix

What is Random Forest?

**Dimentionality Reduction**

Introduction to Dimensionality

Why Dimensionality Reduction

PCA

Factor Analysis

Scaling dimensional model

LDA and more…

**Supervised Learning - II**

What is Naïve Bayes?

How Naïve Bayes works?

Implementing Naïve Bayes Classifier

What is Support Vector Machine?

Illustrate how Support Vector Machine works?

Hyperparameter optimization

Grid Search vs Random Search

Implementation of Support Vector Machine for Classification

What is Clustering & its Use Cases?

What is K-means Clustering?

How K-means algorithm works?

How to do optimal clustering

What is C-means Clustering?

What is Hierarchical Clustering?

How Hierarchical Clustering works?

**Association Rules and Recommendation Engines**

What are Association Rules?

Association Rule Parameters

Calculating Association Rule Parameters

Recommendation Engines

How Recommendation Engines work?

Collaborative Filtering

Content Based Filtering

What is Reinforcement Learning

Why Reinforcement Learning

Elements of Reinforcement Learning

Exploration vs Exploitation dilemma

Epsilon Greedy Algorithm

Markov Decision Process (MDP)

Q values and V values

Q – Learning

α values

**Time Series Analysis**

What is Time Series Analysis?

Importance of TSA

Components of TSA

White Noise

AR model

MA model

ARMA model

ARIMA model

Stationarity

ACF & PACF and more…

**Model Selection and Boosting**

What is Model Selection?

Need of Model Selection

Cross – Validation

What is Boosting?

How Boosting Algorithms work?

Types of Boosting Algorithms

Adaptive Boosting and more…

- Get a firm grip on Python and Machine Learning
- Understanding Data-sets and extracting useful data to aid intelligent decision making
- Hands-on experience on data manupulation and Machine Learning algorithms

- Eagerness to learn
- Laptop

2-2.5 hours/day

Total 20 days (40-45 hours)

**Fee per head**

Start your growth with us today!