ARTIFICIAL INTELLIGENCE

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ARTIFICIAL INTELLIGENCE Exams & Certification

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SYLLABUS

Deep Learning: A revolution in Artificial Intelligence
  • Limitations of Machine Learning
  • Need for Data Scientists
  • Foundation of Data Science
  • What is Business Intelligence?
  • What is Data Analysis?
  • What is Data Mining
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
  • 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?
  • 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
  • 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 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
  • The xrange() function
  • List Comprehensions
  • Generator Expressions
  • Dictionaries and Sets.
  • Learning NumPy
  • Introduction to Pandas
  • Creating Data Frames
  • GroupingSorting
  • Plotting Data
  • Creating Functions
  • Slicing/Dicing Operations.
  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values. Sorting
  • Alternate Keys
  • Lambda Functions
  • Sorting Collections of Collections
  • Classes & OOPs
  • 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
  • Introduction
  • ML Fundamentals
  • ML Common Use Cases
  • Understanding Supervised and Unsupervised Learning Techniques
  • Similarity Metrics
  • Distance Measure Types: Euclidean, Cosine Measures
  • Creating predictive models
  • Understanding K-Means Clustering
  • Understanding of TF-IDF, Cosine Similarity and their application to Vector Space Model
  • Case study
  • What is Association Rules with its use cases?
  • What is Recommendation Engine & it’s working?
  • Recommendation Use-case
  • Case study
  • 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
  • What is Random Forests
  • Features of Random Forest
  • Out of Box Error Estimate & its Variable Importance
  • Case study
  • Case study
  • What are the various approaches to solve a Data Science Problem
  • Pros and Cons of different approaches and algorithms.
  • Case study
  • Introduction to Predictive Modeling
  • Linear Regression Overview
  • Simple Linear Regression
  • Multiple Linear 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
  • Case Study
  • Introduction to SVMs
  • SVM History
  • Vectors Overview
  • Decision Surfaces
  • Linear SVMs
  • The Kernel Trick
  • Non-Linear SVMs
  • The Kernel SVM
  • Describing Time Series data
  • Formatting your Time Series data
  • Listing the different components of Time Series data
  • Discussing different kind of Time Series scenarios
  • Choosing the model according to the Time series scenario
  • Implementing the model for forecasting
  • Explaining working and implementation of ARIMA model
  • Illustrating the working and implementation of different ETS models
  • Forecasting the data using the respective model
  • What is Time Series data?
  • Time Series variables
  • Different components of Time Series data
  • Visualizing the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identification of 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
  • Application of ARIMA and ETS model for Time Series forecasting
  • Forecasting for given Time period
  • Case Study
  • Various machine learning algorithms in Python
  • Apply machine learning algorithms in Python
  • 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
  • Introduction Model Tuning
  • Parameter Tuning GridSearchCV
  • A second method to tune your algorithm
  • How to automate machine learning
  • Which ML algo should you choose
  • A study on how to compare machine learning algorithms in practice
  • Sentimental Analysis
  • Case study
  • Introduction to Spark Core
  • Spark Architecture
  • Working with RDDs
  • Introduction to PySpark
  • Machine learning with PySpark – Mllib

Deep Learning & AI

  • Case Study
  • Deep Learning Overview
  • The Brain vs Neuron
  • Introduction to Deep Learning
  • 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 Operation
  • Relu Layers
  • What is Pooling vs Flattening
  • Full Connection
  • Softmax vs Cross Entropy
  • Build a real world convolution neural network for image classification
  • Recurrent neural networks rnn
  • LSTMs understanding LSTMs
  • Python’s Long short term memory neural networks lstm
  • Restricted Boltzmann Machine
  • Applications of RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders
  • Building a Autoencoder model
  • 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
  • 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
  • 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 Introduction
  • Google Inception Model
  • How to retrain Google Inception with our own data demo
  • Predicting new images
  • Transfer Learning Summary
  • Extending Tensorflow
  • Keras
  • TFLearn
  • Keras vs TFLearn Comparison

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