Artificial Intelligence Training

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
What is Time Series data?
Time Series variables
Different components of Time Series data
Visualize the data to identify Time Series Components
Implement ARIMA model for forecasting
Exponential smoothing models
Identifying different time series scenario based on which different Exponential Smoothing model can be applied
Implement respective model for forecasting
Visualizing and formatting Time Series data
Plotting decomposed Time Series data plot
Applying ARIMA and ETS model for Time Series forecasting
Forecasting for given Time period
Case Study

Machine Learning Project

Machine learning algorithms Python

Various machine learning algorithms in Python
Apply machine learning algorithms in Python

Feature Selection and Pre-processing

How to select the right data
Which are the best features to use
Additional feature selection techniques
A feature selection case study
Preprocessing
Preprocessing Scaling Techniques
How to preprocess your data
How to scale your data
Feature Scaling Final Project

Which Algorithms perform best

Highly efficient machine learning algorithms
Bagging Decision Trees
The power of ensembles
Random Forest Ensemble technique
Boosting – Adaboost
Boosting ensemble stochastic gradient boosting
A final ensemble technique

Model selection cross validation score

Introduction Model Tuning
Parameter Tuning GridSearchCV
A second method to tune your algorithm
How to automate machine learning
Which ML algo should you choose
How to compare machine learning algorithms in practice

Text Mining& NLP

Sentimental Analysis
Case study

PySpark and MLLib

Introduction to Spark Core
Spark Architecture
Working with RDDs
Introduction to PySpark
Machine learning with PySpark – Mllib

Deep Learning & AI using Python

Deep Learning & AI

Case Study
Deep Learning Overview
The Brain vs Neuron
Introduction to Deep Learning

Introduction to Artificial Neural Networks

The Detailed ANN
The Activation Functions
How do ANNs work & learn
Gradient Descent
Stochastic Gradient Descent
Backpropogation
Understand limitations of a Single Perceptron
Understand Neural Networks in Detail
Illustrate Multi-Layer Perceptron
Backpropagation – Learning Algorithm
Understand Backpropagation – Using Neural Network Example
MLP Digit-Classifier using TensorFlow
Building a multi-layered perceptron for classification
Why Deep Networks
Why Deep Networks give better accuracy?
Use-Case Implementation
Understand How Deep Network Works?
How Backpropagation Works?
Illustrate Forward pass, Backward pass
Different variants of Gradient Descent

Convolutional Neural Networks

Convolutional Operation
Relu Layers
What is Pooling vs Flattening
Full Connection
Softmax vs Cross Entropy
” Building a real world convolutional neural network
for image classification”

What are RNNs – Introduction to RNNs

Recurrent neural networks rnn
LSTMs understanding LSTMs
long short term memory neural networks lstm in python

Restricted Boltzmann Machine (RBM) and Autoencoders

Restricted Boltzmann Machine
Applications of RBM
Introduction to Autoencoders
Autoencoders applications
Understanding Autoencoders
Building a Autoencoder model

Tensorflow with Python

Introducing Tensorflow
Introducing Tensorflow
Why Tensorflow?
What is tensorflow?
Tensorflow as an Interface
Tensorflow as an environment
Tensors
Computation Graph
Installing Tensorflow
Tensorflow training
Prepare Data
Tensor types
Loss and Optimization
Running tensorflow programs

Building Neural Networks using

Tensorflow

Tensors
Tensorflow data types
CPU vs GPU vs TPU
Tensorflow methods
Introduction to Neural Networks
Neural Network Architecture
Linear Regression example revisited
The Neuron
Neural Network Layers
The MNIST Dataset
Coding MNIST NN

Deep Learning using

Tensorflow

Deepening the network
Images and Pixels
How humans recognise images
Convolutional Neural Networks
ConvNet Architecture
Overfitting and Regularization
Max Pooling and ReLU activations
Dropout
Strides and Zero Padding
Coding Deep ConvNets demo
Debugging Neural Networks
Visualising NN using Tensorflow
Tensorboard

Transfer Learning using

Keras and TFLearn

Transfer Learning Introduction
Google Inception Model
Retraining Google Inception with our own data demo
Predicting new images
Transfer Learning Summary
Extending Tensorflow
Keras
TFLearn
Keras vs TFLearn Comparison