Visão 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

Conteúdo Programatico

Module I

1. What is the required skillset of a Data Scientist?
2. Combining the technical and non-technical roles of a Data Scientist
3. The difference between a Data Scientist and a Data Engineer
4. Exploring the full lifecycle of Data Science efforts within the organization
5. Turning business questions into Machine Learning (ML) and Artificial Intelligence (AI) models
6. Exploring diverse and wide-ranging data sources that can be used to answer business questions

Module II

1. Introducing the features of Python that are relevant to Data Scientists and Data Engineers
2. Viewing Data Sets using Python’s Pandas library
3. Importing, exporting, and working with all forms of data, from Relational Databases to Google Images
4. Using Python Selecting, Filtering, Combining, Grouping and Applying Functions from Python’s Pandas library
5. Dealing with Duplicates, Missing Values, Rescaling, Standardizing and Normalizing Data
6. Visualizing data for both exploration and communication with the Pandas, Matplotlib and Seaborn Python libraries

Module III

1. Preprocessing Unstructured Data such as web adverts, emails, and blog posts for AI/ML models
2. Exploring the most popular approaches to Natural Language Processing (NLP) such as stemming and “stop” words
3. Preparing a term-document matrix (TDM) of unstructured documents for analysis

Module IV

1. Expressing a business problem, such as customer revenue prediction, as a linear regression task
2. Assessing variables as potential Predictors of the required Target (e.g., Education as a predictor of Salary Build)
3. Interpreting and Evaluating a Linear Regression model in Python using measures such as RMSE
4. Exploring the Feature Engineering possibilities to improve the Linear Regression model

Module V

1. Learning how AI/ML Classifiers are built and used to make predictions such as Customer Churn
2. Exploring how AI/ML Classification models are built using Training, Test, and Validation
3. Evaluating the strength of a Decision Tree Classifier

Module VI

1. Examining alternative approaches to classification
2. Considering how Activation Functions are integral to Logistic Regression Classifiers
3. Investigating how Neural Networks and Deep Learning are used to build self-driving cars
4. Exploring the probability foundations of Naive Bayes classifiers
5. Reviewing different approaches to measuring the performance of AI/ML Classification Models
6. Reviewing ROC curves, AUC measures, Precision, Recall, Confusion Matrices

Module VII

1. Uncovering new ways of segmenting your customers, products, or services using clustering algorithms
2. Exploring what the concept of similarity means to humans and how it can be implemented programmatically through distance measures on descriptive variables
3. Performing top-down clustering with Python’s Scikit-Learn K-Means algorithm
4. Performing bottom-up clustering with Scikit-Learn’s hierarchical clustering algorithm
5. Examining clustering techniques on unstructured data (e.g., Tweets, Emails, Documents, etc.)

Module VIII

1. Building models of customer behaviors or business events from logged data using Association Rules
2. Evaluating the strength of these models through probability measures of support, confidence, and lift
3. Employing feature engineering approaches to improve the models
4. Building a recommender for your customers that is unique to your product/service offering

Module IX

1. Analyzing your organization, its people, and environment as a network of inter-relationships
2. Visualizing these relationships to uncover previously unseen business insights
3. Exploring ego-centric and socio-centric methods of analyzing connections important to your organization

Module X

1. Examining Cloud (Microsoft, Amazon, Google) approaches to handling Big Data analytics
2. Exploring the communications and ethics aspects of being a Data Scientist
3. Surveying the paths of continual learning for a Data Scientist