Visão Geral

Curso AI and Deep Learning, 

• Tomada de decisão automatizada: os algoritmos de aprendizado de máquina podem ser usados ​​para tomar decisões e previsões automatizadas com base nos padrões ou tendências que detectam nos dados.
• Aprendizado dinâmico: os algoritmos de aprendizado de máquina são capazes de aprender continuamente com novos dados e atualizar suas previsões à medida que novos pontos de dados são adicionados.
• Autoaperfeiçoamento: os algoritmos de aprendizado de máquina podem ser projetados para se autoaprimorar e se otimizar ao longo do tempo.
• Escalabilidade: os algoritmos de aprendizado de máquina podem ser dimensionados para processar grandes quantidades de dados de forma rápida e eficiente.
• Eficiência de custo: os algoritmos de aprendizado de máquina são econômicos em comparação com os métodos tradicionais de análise de dados.

Pre-Requisitos

Não há pré-requisitos 

Materiais

Conteúdo Programatico

Introduction to Data Science

1. What is Data Science?
2. What is Machine Learning?
3. What is Deep Learning?
4. What is AI?
5. Data Analytics & it’s types

Introduction to Python

1. What is Python?
2. Why Python?
3. Installing Python
4. Python IDEs
5. Jupyter Notebook Overview

Python Basics

1. Python Basic Data types
2. Lists
3. Slicing
4. IF statements
5. Loops
6. Dictionaries
7. Tuples
8. Functions
9. Array
10. Selection by position & Labels

Python Packages

1. Pandas
2. Numpy
3. Sci-kit Learn
4. Mat-plot library

Importing Data

1. Reading CSV files
2. Saving in Python data
3. Loading Python data objects
4. Writing data to csv file

Manipulating Data

1. Selecting rows/observations
2. Rounding Number
3. Selecting columns/fields
4. Merging data
5. Data aggregation
6. Data munging techniques

Statistics Basics

1. Central Tendency
2. Mean
3. Median
4. Mode
5. Skewness
6. Normal Distribution
7. Probability Basics
8. What does mean by probability?
9. Types of Probability
10. ODDS Ratio?
11. Standard Deviation
12. Data deviation & distribution
13. Variance
14. Bias variance Trade off
15. Underfitting
16. Overfitting
17. Distance metrics
18. Euclidean Distance
19. Manhattan Distance
20. Outlier analysis
21. What is an Outlier?
22. Inter Quartile Range
23. Box & whisker plot
24. Upper Whisker
25. Lower Whisker
26. Scatter plot
27. Cook’s Distance
28. Missing Value treatment
29. What is a NA?
30. Central Imputation
31. KNN imputation
32. Dummification
33. Correlation
34. Pearson correlation
35. Positive & Negative correlation

Error Metrics

1. Classification
2. Confusion Matrix
3. Precision
4. Recall
5. Specificity
6. F1 Score
7. Regression
8. MSE
9. RMSE
10. MAPE

Machine Learning

1. Supervised Learning
2. Linear Regression
3. Linear Equation
4. Slope
5. Intercept
6. R square value
7. Logistic regression
8. ODDS ratio
9. Probability of success
10. Probability of failure Bias Variance Tradeoff
11. ROC curve
12. Bias Variance Tradeoff
13. Unsupervised Learning
14. K-Means
15. K-Means ++
16. Hierarchical Clustering
17. SVM
18. Support Vectors
19. Hyperplanes
20. 2-D Case
21. Linear Hyperplane
22. SVM Kernal
23. Linear
24. Radial
25. polynomial
26. Other Machine Learning algorithms
27. K – Nearest Neighbour
28. Naïve Bayes Classifier
29. Decision Tree – CART
30. Decision Tree – C50
31. Random Forest

ARTIFICIAL INTELLIGENCE

1. AI Introduction
2. Perceptron
3. Multi-Layer perceptron
4. Markov Decision Process
5. Logical Agent & First Order Logic
6. AL Applications

Deep Learning Algorithms

1. CNN – Convolutional Neural Network
2. RNN – Recurrent Neural Network
3. ANN – Artificial Neural Network
4. Introduction to NLP
5. Text Pre-processing
6. Noise Removal
7. Lexicon Normalization
8. Lemmatization
9. Stemming
10. Object Standardization
11. Text to Features (Feature Engineering)
12. Syntactical Parsing
13. Dependency Grammar
14. Part of Speech Tagging
15. Entity Parsing
16. Named Entity Recognition
17. Topic Modelling
18. N-Grams
19. TF – IDF
20. Frequency / Density Features
21. Word Embedding’s
22. Tasks of NLP
23. Text Classification
24. Text Matching
25. Levenshtein Distance
26. Phonetic Matching
27. Flexible String Matching

Design Effective Reports

1. Enhance report design
2. Add report objects to enhance design
3. Format data and report objects
4. Add a background image to a report
5. Add row numbers to a report

Customize Reports with Conditional Formatting

1. Create multi-lingual reports
2. Highlight exceptional data
3. Show and hide data
4. Conditionally render objects in reports

Analysis Studio

1. Analysis Studio Fundamentals
2. Nest Data in Crosstabs in Analysis Studio
3. Create Analysis with Multiple filter
4. Reusable analysis
5. Build Advanced Crosstabs in Analysis Studio
6. Focus with Filters in Analysis Studio
7. Creating reports from cubes
8. Drill down and drill up

Event Studio

1. Introduction to Event Studio
2. Create an agent
3. Add tasks to an agent
4. Run an agent through its lifecycle
5. Schedule an agent

Business Insight

1. Introdcution to Dashboards
2. Create Dashboard
3. Types of Filter-Value, Slider and advanced filter
4. Overview of RSS Feed and web Page
5. Content Pane
6. Create Widgets
7. Sort, Filter and Calculate data
8. Hands on

Business Insight Advanced

1. Overview of Business Intelligence Advance level
2. Create Different types of Reports
3. Reporting Styles and filters
4. Create dashboard objects
5. Summarize data and Create Calculations
6. Dispatcher and Services

Dispatcher in detail

1. All Services
2. Properties of Services