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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
 RealLife 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
 Builtin 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
 ChiSquare 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 KMeans Clustering
 Understanding TFIDF, 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 Usecase
 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
 NonLinear 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 Preprocessing
 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 MultiLayer Perceptron
 Backpropagation – Learning Algorithm
 Understand Backpropagation – Using Neural Network Example
 MLP DigitClassifier using TensorFlow
 Building a multilayered perceptron for classification
 Why Deep Networks
 Why Deep Networks give better accuracy?
 UseCase 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
 Softmaxvs 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
 KerasvsTFLearn Comparison
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