Artificial Intelligence Training

Artificial Intelligence (AI)

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It encompasses a broad range of technologies and techniques, including machine learning, natural language processing, computer vision, robotics, and more.

AI enables machines to perform tasks that typically require human intelligence, such as recognizing speech, making decisions, solving problems, and understanding natural language. The ultimate goal of AI is to create systems that can mimic human cognitive abilities and automate complex processes efficiently.

Artificial Intelligence exists when a machine can have human based skills such as learning, reasoning, and solving problems

With Artificial Intelligence you do not need to preprogram a machine to do some work, despite that you can create a machine with programmed algorithms which can work with own intelligence, and that is the awesomeness of AI.

Why Artificial Intelligence?

Artificial intelligence (AI) is pursued for several reasons, each tied to its potential benefits and capabilities:

1. Automation: AI enables automation of tasks that traditionally require human intelligence, improving efficiency and reducing human error in various fields such as manufacturing, logistics, and customer service.
2. Decision Making: AI systems can analyze large amounts of data quickly and make data-driven decisions, aiding businesses and organizations in making informed choices and predictions.
3. Innovation: AI fosters innovation by enabling the development of new products and services that were previously not possible or practical, such as autonomous vehicles, personalized medicine, and smart devices.
4. Efficiency: By handling repetitive and mundane tasks, AI frees up human workers to focus on higher-level responsibilities, creativity, and innovation.
5. Scalability: AI systems can scale operations quickly and efficiently, adapting to changes in demand or complexity without the need for significant human intervention.
6. Personalization: AI allows for personalized user experiences in fields like marketing, entertainment, and healthcare, by understanding individual preferences and behaviors.
7. Problem Solving: AI can tackle complex problems that require pattern recognition, data analysis, and decision-making skills, contributing to advancements in fields such as climate science, drug discovery, and cyber security.

Where to use Artificial Intelligence?

Artificial intelligence (AI) finds applications across a wide range of industries and sectors due to its versatility and ability to automate tasks, analyze data, and make decisions. Here are some key areas where AI is commonly used:

• Entertainment: AI is used for content recommendation on streaming platforms, personalized advertising, video and audio analysis, and content creation (like AI-generated art and music).Data binding, and modular development.
• Healthcare: AI is used for medical imaging analysis (such as X-rays and MRIs), personalized treatment plans, drug discovery, virtual nursing assistants, and predictive analytics for patient outcomes.
• Finance: AI is employed for fraud detection, algorithmic trading, credit scoring, personalized financial advice, risk assessment, and customer service chat bots.
• Retail and E-commerce: AI powers recommendation systems, demand forecasting, personalized shopping experiences, supply chain optimization, and chat bots for customer support.
• Automotive and Transportation: AI is utilized in autonomous vehicles, predictive maintenance of vehicles and infrastructure, route optimization, traffic management, and driver behavior analysis.
• Manufacturing: AI improves process optimization, predictive maintenance of machinery, quality control through computer vision, supply chain management, and autonomous robots for assembly and logistics.
• Customer Service: AI chat bots and virtual assistants handle customer inquiries, provide personalized recommendations, and automate responses across various industries.
• Education: AI aids in personalized learning platforms, automated grading systems, adaptive learning programs, and educational content creation.
• Natural Language Processing (NLP): AI powers voice assistants (like Siri and Alexa), language translation services, sentiment analysis, chat bots, and text summarization.
• Cyber security: AI enhances threat detection, anomaly detection in network traffic, behavior analysis to identify potential threats, and automated responses to cyberattacks.

Module 1: Introduction

• What is Data Science?
• What is Machine Learning?
• What is Deep Learning?
• What is AI?
• Data Analytics & its types

Module 2: Introduction to Python

• What is Python?
• Why Python?
• Installing Python
• Python IDEs
• Jupyter Notebook Overview

Module 3: Python Basics

• Python Basic Data types
• Lists
• Slicing
• IF statements
• Loops
• Dictionaries
• Tuples
• Functions
• Array
• Selection by position & Labels

Module 4: Python Packages

• Pandas
• Numpy
• Sci-kit Learn
• Mat-plot library

Module 5: Importing Data

• Reading CSV files
• Saving in Python data
• Loading Python data objects
• Writing data to CSV file

Module 6: Manipulating Data

• Selecting rows/observations
• Rounding Number
• Selecting columns/fields
• Merging data
• Data aggregation
• Data munging techniques

Module 7: Statistics Basics

• Central Tendency
  • Mean
  • Median
  • Mode
  • Skewness
  • Normal Distribution
• Probability Basics
  • What does it mean by probability?
  • Types of Probability
  • ODDS Ratio?

• Standard Deviation
  • Data deviation & distribution
  • Variance

• Bias variance Tradeoff
  • Underfitting
  • Overfitting
• Distance metrics
  • Euclidean Distance
  • Manhattan Distance
• Outlier analysis
  • What is an Outlier?
  • Inter Quartile Range
  • Box & whisker plot
  • Upper Whisker
  • Lower Whisker
  • Scatter plot
  • Cook’s Distance
• Missing Value treatment
  • What is NA?
  • Central Imputation
  • KNN imputation
  • Dummification
• Correlation
  • Pearson correlation
  • positive & Negative correlation

Module 8: Error Metrics

• Classification
  • Confusion Matrix
  • Precision
  • Recall
  • Specificity
  • F1 Score
• Regression
  • MSE
  • RMSE
  • MAPE

Machine Learning

Module 1: Supervised Learning

• Linear Regression
  • Linear Equation
  • Slope
  • Intercept
  • R square value
• Logistic regression
  • ODDS ratio
  • Probability of success
  • Probability of failure Bias Variance Tradeoff
  • ROC curve
  • Bias Variance Tradeoff

Module 2: Unsupervised Learning

• K-Means
• K-Means ++
• Hierarchical Clustering

Module 3: SVM

• Support Vectors
• Hyperplanes
• 2-D Case
• Linear Hyperplane

Module 4: SVM Kernel

• Linear
• Radial
• polynomial

Module 5: Other Machine Learning Algorithms

• K – Nearest Neighbour
• Naïve Bayes Classifier
• Decision Tree – CART
• Decision Tree – C50
• Random Forest

Artificial Intelligence

Module 1: AI Introduction

• Perceptron
• Multi-Layer perceptron
• Markov Decision Process
• Logical Agent & First Order Logic
• AL Applications

Deep Learning

Module 1: Deep Learning Algorithms

• CNN – Convolutional Neural Network
• RNN – Recurrent Neural Network
• ANN – Artificial Neural Network

Module 2: Introduction to NLP

• Text Pre-processing
• Noise Removal
• Lexicon Normalization
• Lemmatization
• Stemming
• Object Standardization

Module 3: Text to Features (Feature Engineering)

• Syntactical Parsing
• Dependency Grammar
• Part of Speech Tagging
• Entity Parsing
• Named Entity Recognition
• Topic Modelling
• N-Grams
• TF – IDF
• Frequency / Density Features
• Word Embeddings

Module 4: Tasks of NLP

• Text Classification
• Text Matching
• Levenshtein Distance
• Phonetic Matching
• Flexible String Matching