Curso Data Science, Machine Learning & AI using Python Introduction
24 horasVisão Geral
Neste Curso Data Science, Machine Learning & AI using Python Introduction, você aprenderá a usar bibliotecas Python para criar, avaliar e implantar modelos de Machine Learning ( ML ) e Inteligência Artificial ( AI ) que podem ajudá-lo a obter insights previamente descobertos de seus dados.
Este curso abrange todas as etapas do ciclo de vida da ciência de dados e ensina como gerenciar, transformar e visualizar dados brutos para criar modelos preditivos que ajudarão você a encontrar e avaliar oportunidades futuras.
Objetivo
Após realizar este Curso Data Science, Machine Learning & AI using Python Introduction, você será capaz de:
- Traduza questões e problemas de negócios diários em tarefas de Machine Learning para tomar decisões orientadas por dados
- Use as bibliotecas Python Pandas , Matplotlib e Seaborn para explorar, analisar e visualizar dados de várias fontes, incluindo a Web, documentos do Word, e-mail, armazenamentos NoSQL, bancos de dados e data warehouses
- Treine um classificador de aprendizado de máquina usando diferentes técnicas algorítmicas da biblioteca Scikit-Learn , como árvores de decisão, regressão logística e redes neurais
- Re-segmente seu mercado de clientes usando K-Means e algoritmos hierárquicos para melhor alinhamento de produtos e serviços às necessidades do cliente
- Descubra comportamentos ocultos do cliente nas regras de associação e crie um mecanismo de recomendação com base em padrões comportamentais
- Investigue relacionamentos e fluxos entre pessoas e entidades relevantes para os negócios usando a Análise de Rede Social
- Crie modelos preditivos de receita e outras variáveis numéricas usando a regressão linear
- Obtenha acesso a um grupo exclusivo do LinkedIn para suporte de colegas e da comunidade
- Teste seus conhecimentos com o exame de final de curso incluído
- Aproveite o suporte contínuo com treinamento individual de instrutor após o curso e sandbox de computação
Materiais
Português + Exercícios + Lab PraticoConteúdo Programatico
Module I
- What is the required skillset of a Data Scientist?
- Combining the technical and non-technical roles of a Data Scientist
- The difference between a Data Scientist and a Data Engineer
- Exploring the full lifecycle of Data Science efforts within the organization
- Turning business questions into Machine Learning (ML) and Artificial Intelligence (AI) models
- Exploring diverse and wide-ranging data sources that can be used to answer business questions
Module II
- Introducing the features of Python that are relevant to Data Scientists and Data Engineers
- Viewing Data Sets using Python’s Pandas library
- Importing, exporting, and working with all forms of data, from Relational Databases to Google Images
- Using Python Selecting, Filtering, Combining, Grouping and Applying Functions from Python’s Pandas library
- Dealing with Duplicates, Missing Values, Rescaling, Standardizing and Normalizing Data
- Visualizing data for both exploration and communication with the Pandas, Matplotlib and Seaborn Python libraries
Module III
- Preprocessing Unstructured Data such as web adverts, emails, and blog posts for AI/ML models
- Exploring the most popular approaches to Natural Language Processing (NLP) such as stemming and “stop” words
- Preparing a term-document matrix (TDM) of unstructured documents for analysis
Module IV
- Expressing a business problem, such as customer revenue prediction, as a linear regression task
- Assessing variables as potential Predictors of the required Target (e.g., Education as a predictor of Salary Build)
- Interpreting and Evaluating a Linear Regression model in Python using measures such as RMSE
- Exploring the Feature Engineering possibilities to improve the Linear Regression model
Module V
- Learning how AI/ML Classifiers are built and used to make predictions such as Customer Churn
- Exploring how AI/ML Classification models are built using Training, Test, and Validation
- Evaluating the strength of a Decision Tree Classifier
Module VI
- Examining alternative approaches to classification
- Considering how Activation Functions are integral to Logistic Regression Classifiers
- Investigating how Neural Networks and Deep Learning are used to build self-driving cars
- Exploring the probability foundations of Naive Bayes classifiers
- Reviewing different approaches to measuring the performance of AI/ML Classification Models
- Reviewing ROC curves, AUC measures, Precision, Recall, Confusion Matrices
Module VII
- Uncovering new ways of segmenting your customers, products, or services using clustering algorithms
- Exploring what the concept of similarity means to humans and how it can be implemented programmatically through distance measures on descriptive variables
- Performing top-down clustering with Python’s Scikit-Learn K-Means algorithm
- Performing bottom-up clustering with Scikit-Learn’s hierarchical clustering algorithm
- Examining clustering techniques on unstructured data (e.g., Tweets, Emails, Documents, etc.)
Module VIII
- Building models of customer behaviors or business events from logged data using Association Rules
- Evaluating the strength of these models through probability measures of support, confidence, and lift
- Employing feature engineering approaches to improve the models
- Building a recommender for your customers that is unique to your product/service offering
Module IX
- Analyzing your organization, its people, and environment as a network of inter-relationships
- Visualizing these relationships to uncover previously unseen business insights
- Exploring ego-centric and socio-centric methods of analyzing connections important to your organization
Module X
- Examining Cloud (Microsoft, Amazon, Google) approaches to handling Big Data analytics
- Exploring the communications and ethics aspects of being a Data Scientist
- Surveying the paths of continual learning for a Data Scientist