Artificial intelligence course syllabus

Introduction

Our artificial intelligence course syllabus is a powerful learning medium that could help you reach your professional objectives and help you climb up the career ladder faster. Our artificial intelligence course syllabus is designed in a way that would enhance your ability to investigate complex problems, break them into smaller components and come up with effective solutions. As a part of our artificial intelligence course syllabus, we help you practice applying your analytical thinking and logic to different situations.

By getting deep into our artificial intelligence course syllabus, you can be all set to successfully navigate the ever-changing software field and bring many new opportunities for career growth and success. Just enrol in our artificial intelligence course in Chennai to achieve your career objectives.

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Artificial intelligence course syllabus

Foundation of AI & ML

• Introduction to Data Science, AI&ML
• R Essentials
• Statistical Analysis

Python for Artificial Intelligence

• Python Essentials
• Python Environment Setup
• Python Data Types
• Python Looping and Control Statements
• Object-Oriented Programming Concepts
• Database Connection
• 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

Python Libraries for Artificial Intelligence

• Numpy
• Scipy
• Pandas
• MatPlot

Data Management

• Data Acquisition
• Data Pre-processing and Preparation
• Data Transformation and Quality
• Handling Text Data
• Big Data Fundamentals
• Big Data Frameworks(Spark, Hadoop, NoSQL)

SAS-Data Analytics

• SAS Introduction
• SAS Functions
• SAS Operators
• SAS Procedures
• SAS Graphs
• SAS Macros
• SAS Format

Statistical Decision Making

• Data Visualisation
• Sampling and Estimation
• Inferential Statistics

Predictive Analytics

• Linear Regression
• Multiple Linear Regression
• Non-Linear Regression
• Forecasting Models

Artificial Intelligence

• Foundations of AI
• Convolution Neural Networks
• Recurrent Neural Networks

Deep Learning with Keras and TensorFlow

• Deep Learning Libraries
• Keras API
• TensorFlow
• Deep Learning Algorithms

Advanced Deep Learning and Computer Vision

• Distributed and Parallel Computing
• Deploying Deep Learning Models
• Reinforcement Learning
• Generating Images with Neural Style
• Object Detection through Convolutional Neural Networks

Cloud Computing and AWS

• Introduction to Cloud Computing and AWS
• Storage Volumes and Elastic Compute
• Virtual Private Cloud
• Simple Storage Services
• AWS Lambda and Amazon Machine Learning

Tableau 10

• Introduction to Data Visualisation
• Tableau Architecture
• Working with Data Blending
• Creation of Sets
• Calculations, Expression, and Parameters
• Dashboards, Stories, and Filters
• Tableau Prep

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

 Conclusion

Getting deep into our artificial intelligence course syllabus could help advance your skills and gain a comprehensive understanding of the industry. Our artificial intelligence course syllabus is more effective to supercharge your skills and help you get ahead in your field. No prior programming knowledge is required, you just connect with us through our artificial intelligence training in Chennai, and we will do the rest for your development in your career.

Our exceptional artificial intelligence course syllabus will help you get access to the latest tools, techniques, algorithms and relevant frameworks used in AI to solve real-world challenges. Our artificial intelligence course syllabus will help you grow as a successful AI engineer for sure.

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