Machine Learning using Python

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.


Outcome
  • 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

Course Overview

Introduction

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

Introduction to Machine Learning

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

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

Unsupervised Learning

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

Reinforcement Learning

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…


Course Benefits
  • 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
Prerequisites
  • Eagerness to learn
  • Laptop
Duration

2-2.5 hours/day
Total 20 days (40-45 hours)


Fee per head

₹ 6500

*fee discounts available at selective locations.

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