Visão Geral

O Machine Learning é um fenômeno global, é a aplicação de sistemas artificiais que dão a capacidade de aprender e melhorar automaticamente a experiência sem a necessidade de programar aspectos explicitamente.

O Python considerado o idioma preferido para o Aprendizado de Máquina é usado extensivamente neste curso para abranger vários conceitos e conduzir os participantes pelas profundezas e vários casos de uso do Aprendizado de Máquina.

Objetivo

No final deste programa de treinamento, os participantes terão uma compreensão profunda das

• Python para aprendizado de máquina
• Vários conceitos e algoritmos de Machine Learning
• Trabalhando com dados para aprendizado de máquina

Pre-Requisitos

• É bom ter um conhecimento básico da programação em Python.

Materiais

Conteúdo Programatico

INTRODUCTION TO MACHINE LEARNING

• Introduction to Artificial Intelligence & Machine Learning
• Who uses AI?
• Supervised & Unsupervised Learning
• Regression & Classification Problems
• What makes a Machine Learning Expert?
• What to learn to become a Machine Learning Developer?
• Overview of Machine Learning Algorithms

INTRODUCTION TO PYTHON

• Basic syntax
• Data structures in Python
• Functions

DATA STRUCTURE & DATA MANIPULATION

• Indexing, Data Processing using Numpy Arrays and Pandas
• Mathematical computing basics
• Basic statistics
• File Input and Output
• Getting Started with Dataframes
• Data Acquisition (Import & Export)
• Selection and Filtering
• Combining and Merging Data Frames
• Removing Duplicates & String Manipulation

LINEAR REGRESSION – CASE STUDY & PROJECT

• Regression Problem Analysis
• Mathematical modelling of Regression Model
• Gradient Descent Algorithm
• Use cases
• Model Specification
• L1 & L2 Regularization
• Building simple Univariate Linear Regression Model
• Multivariate Regression Model

KNN

• KNN Theory
• KNN with Python
• KNN Exercise with real data

DECISION TREESGG

• Forming a Decision Tree
• Components of Decision Tree
• Mathematics of Decision Tree
• Decision Tree Evaluation for use cases

RANDOM FORESTS

• Random Forest Mathematics
• Examples & use cases using Random Forests

BAGGING AND BOOSTING

• Bias Variance Tradeoffs
• Bagging
• Boosting
• Bootstrapping
• Ensemble models with real world data

SUPPORT VECTOR MACHINE

• Concept and Working Principle
• Mathematical Modelling
• Optimization Function Formation
• The Kernel Method and Nonlinear Hyperplanes
• Use Cases & Programming SVM using Python

LOGISTIC REGRESSION

• Assumptions
• Reason for the Logit Transform
• Logit Transformation
• Hypothesis
• Variable and Model Significance
• Maximum Likelihood Concept
• Log Odds and Interpretation

CLUSTERING

• Hierarchical Clustering
• K Means Clustering
• Use Cases for K Means Clustering
• Programming for K Means using Python
• Cluster Size Optimization vs Definition Optimization