About Artificial Intelligence Training

Artificial Intelligence (AI) has a long history but is still properly and actively growing and changing. In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI such as Data Science, Machine Learning, Deep Learning, Statistics, Artificial Neural Networks, Restricted Boltzmann Machine (RBM) and Tensorflow with Python. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination. This Artificial Intelligence course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is going to apply.

 

Introduction to Data Science Deep Learning & Artificial Intelligence

Introduction to Deep Learning & AI

Deep Learning: A revolution in Artificial Intelligence

• Limitations of Machine Learning

What is Deep Learning?

• Need for Data Scientists
• Foundation of Data Science
• What is Business Intelligence
• What is Data Analysis
• What is Data Mining

What is Machine Learning?
Analytics vs Data Science

• Value Chain
• Types of Analytics
• Lifecycle Probability
• Analytics Project Lifecycle
• Advantage of Deep Learning over Machine learning
• Reasons for Deep Learning
• Real-Life use cases of Deep Learning
• Review of Machine Learning

 Data

• Basis of Data Categorization
• Types of Data
• Data Collection Types
• Forms of Data & Sources
• Data Quality & Changes
• Data Quality Issues
• Data Quality Story
• What is Data Architecture
• Components of Data Architecture
• OLTP vs OLAP
• How is Data Stored?

Big Data

• What is Big Data?
• 5 Vs of Big Data
• Big Data Architecture
• Big Data Technologies
• Big Data Challenge
• Big Data Requirements
• Big Data Distributed Computing & Complexity
• Hadoop
• Map Reduce Framework
• Hadoop Ecosystem

Data Science Deep Dive

• What Data Science is
• Why Data Scientists are in demand
• What is a Data Product
• The growing need for Data Science
• Large Scale Analysis Cost vs Storage
• Data Science Skills
• Data Science Use Cases
• Data Science Project Life Cycle & Stages
• Data Acuqisition
• Where to source data
• Techniques
• Evaluating input data
• Data formats
• Data Quantity
• Data Quality
• Resolution Techniques
• Data Transformation
• File format Conversions
• Annonymization

Python

• Python Overview
• About Interpreted Languages
• Advantages/Disadvantages of Python pydoc.
• Starting Python
• Interpreter PATH
• Using the Interpreter
• Running a Python Script
• Using Variables
• Keywords
• Built-in Functions
• StringsDifferent Literals
• Math Operators and Expressions
• Writing to the Screen
• String Formatting
• Command Line Parameters and Flow Control.
• Lists
• Tuples
• Indexing and Slicing
• Iterating through a Sequence
• Functions for all Sequences

Operators and Keywords for Sequences

• The xrange() function
• List Comprehensions
• Generator Expressions
• Dictionaries and Sets.

Numpy & Pandas

• Learning NumPy
• Introduction to Pandas
• Creating Data Frames
• GroupingSorting
• Plotting Data
• Creating Functions
• Slicing/Dicing Operations.

Deep Dive – Functions & Classes & Oops

• Functions
• Function Parameters
• Global Variables
• Variable Scope and Returning Values. Sorting
• Alternate Keys
• Lambda Functions
• Sorting Collections of Collections
• Classes & OOPs

Statistics

• What is Statistics
• Descriptive Statistics
• Central Tendency Measures
• The Story of Average
• Dispersion Measures
• Data Distributions
• Central Limit Theorem
• What is Sampling
• Why Sampling
• Sampling Methods
• Inferential Statistics
• What is Hypothesis testing
• Confidence Level
• Degrees of freedom
• what is pValue
• Chi-Square test
• What is ANOVA
• Correlation vs Regression
• Uses of Correlation & Regression

Machine Learning, Deep Learning & AI using Python

Introduction

• ML Fundamentals
• ML Common Use Cases
• Understanding Supervised and Unsupervised Learning Techniques

 Clustering

• Similarity Metrics
• Distance Measure Types: Euclidean, Cosine Measures
• Creating predictive models
• Understanding K-Means Clustering
• Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
• Case study

Implementing Association rule mining

• What is Association Rules & its use cases?
• What is Recommendation Engine & it’s working?
• Recommendation Use-case
• Case study

Understanding Process flow of Supervised Learning Techniques

Decision Tree Classifier

• How to build Decision trees
• What is Classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Decision Tree
• Confusion Matrix
• Case study

Random Forest Classifier

• What is Random Forests
• Features of Random Forest
• Out of Box Error Estimate and Variable Importance
• Case study

Naive Bayes Classifier.

• Case study

Project Discussion

Problem Statement and Analysis

• Various approaches to solve a Data Science Problem
• Pros and Cons of different approaches and algorithms.

Linear Regression

• Case study
• Introduction to Predictive Modeling
• Linear Regression Overview
• Simple Linear Regression
• Multiple Linear Regression

Logistic Regression

• Case study
• Logistic Regression Overview
• Data Partitioning
• Univariate Analysis
• Bivariate Analysis
• Multicollinearity Analysis
• Model Building
• Model Validation
• Model Performance Assessment AUC & ROC curves
• Scorecard

Support Vector Machines

• Case Study
• Introduction to SVMs
• SVM History
• Vectors Overview
• Decision Surfaces
• Linear SVMs
• The Kernel Trick
• Non-Linear SVMs
• The Kernel SVM

Time Series Analysis

• Describe Time Series data
• Format your Time Series data
• List the different components of Time Series data
• Discuss different kind of Time Series scenarios
• Choose the model according to the Time series scenario
• Implement the model for forecasting
• Explain working and implementation of ARIMA model
• Illustrate the working and implementation of different ETS models
• Forecast the data using the respective model
• Show more