Visão Geral

Curso IT & ML With Python, A Inteligência Artificial (IA) é um ramo da Ciência que lida com ajudar as máquinas a encontrar soluções para problemas complexos de uma forma mais humana. Este curso ajuda a entender a definição de IA ("geral" e "estreita"), a relação entre IA e máquina, supervisão e não supervisionada, aprendizado por reforço. Após este programa, os participantes poderão iniciar AI & ML com programação em Python

Materiais

Conteúdo Programatico

Module-1

1. Introduction – Data Science (AI/ML)?
2. Data Extraction
3. Data Wrangling
4. Data Exploration
5. Data Visualisation
6. Statistics

Module-2 Python

1. Overview of Python
2. Creating “Hello World” code
3. Variables
4. Python files I/O Functions
5. Numbers
6. Strings and related operations
7. Tuples and related operations
8. Lists and related operations
9. Dictionaries and related operations
10. Tuple – properties, related operations, compared with a list
11. List – properties, related operations
12. Dictionary – properties, related operations

Module-3

1. • NumPy – arrays
2. • Operations on arrays
3. • Indexing slicing and iterating
4. • Pandas – data structures & index operations
5. • Reading and Writing data from Excel/CSV formats into Pandas
6. • Matplotlib library

Module-4

1. Python Revision (numpy, Pandas, scikit learn, matplotlib)
2. What is Machine Learning?
3. Machine Learning Use-Cases
4. Machine Learning Process Flow
5. Machine Learning Categories
6. Linear regression
7. What are Classification and its use cases?

Supervised Learning

1. What is Decision Tree?
2. Confusion Matrix
3. What is Random Forest?
4. What is Naïve Bayes?
5. How Naïve Bayes works?
6. Implementing Naïve Bayes Classifier
7. What is Support Vector Machine?

Module-5

Unsupervised Learning

1. What is Clustering & its Use Cases?
2. What is K-means Clustering?
3. How does K-means algorithm work?
4. What is Hierarchical Clustering?
5. How Hierarchical Clustering works? Reinforcement Learning
6. What is Reinforcement Learning
7. Why Reinforcement Learning
8. Elements of Reinforcement Learning
9. Exploration vs Exploitation dilemma
10. Market Basket Analysis

Module-6

1. Introduction to Dimensionality
2. Why Dimensionality Reduction
3. PCA (Principal Component Analysis)
4. Factor Analysis Time Series Analysis (TSA)

• What is Time Series Analysis?

1. Importance of TSA
2. Components of TSA
3. White Noise
4. AR model (Auto regression )
5. MA model (moving-average )
6. ARMA model (Auto regressive moving average)
7. ARIMA model ( Auto Regressive Integrated Moving Average )
8. Stationarity
9. Data Visualization

Hands on for all the modules:

1. Creating “Hello World” code
2. Linear Regression
3. Logistic regression
4. Decision tree
5. Principal Component Analysis (PCA)
6. Factor Analysis
7. Time Series Analysis/ Forecasting
8. Market Basket Analysis
9. Data Visualization