Artificial Intelligence Online Training Modules Overview

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
• MapReduce 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 Acquisition
• Where to source data
• Techniques
• Evaluating input data
• Data formats
• Data Quantity
• Data Quality
• Resolution Techniques
• Data Transformation
• File Format Conversions
• Anonymization

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
• Strings Different 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
• Grouping Sorting
• 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 p Value
• 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 the Process flow of Supervised Learning Techniques Decision Tree Classifier

• How to build Decision trees
• What are 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 are 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 solving 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 a 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

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
• Backpropagation
• Understand the 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 an Autoencoder model

Tensorflow with Python

• 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 recognize 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
• Visualizing NN using Tensorflow
• Tensorboard

Job Opportunities in Artificial Intelligence

Who wouldn’t prefer a job that assures a fast paced global career, higher than average perks? The job opportunities in the domain of Artificial Intelligence are quite plenty. And with the increase in the colossal demand for the qualified experts across the top industries, more & more number of aspirants are planning towards securing their career in this domain. And also in response to the whooping salary packages for the certified professionals in this domain most of the professionals who are working in other prominent technologies are working towards making a career transition into this domain.

Artificial Intelligence Online Training by SacrosTek Systems will set you on the right career path of achieving success in this domain.

SacrosTek Systems offer certification programs for Artificial Intelligence. Certificates are issues on successful completion of the course and the assessment examination. Students are requested to participate in the real-time project program to get first-hand experience on the usage and application of the Artificial Intelligence. The real-time projects are designed by our team of industry experts to help students get best possible exposure to the Artificial Intelligence and its applications.