Artificial Intelligence (AI) Course Training

Course Description 


About Artificial Intelligence (AI) Training

AArtificial Intelligence (AI) is the big thing in the technology field and a large number of organizations are implementing AI and the demand for professionals in AI is growing at an amazing speed. Artificial Intelligence (AI) course with Neo Academy Pro will provide a wide understanding of the concepts of Artificial Intelligence (AI) to make computer programs to solve problems and achieve goals in the world.

What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) makes computers to perform tasks such as speech recognition, decision-making and visual perception which normally requires human intelligence that aims to develop intelligent machines The basic grounding in the Neo Academy Pro’s practices in AI is likely to become valuable in the field of business, and profession. This course is intended to cover the concepts of Artificial Intelligence from the basics to advanced implementation.

What are the course objectives?

Artificial Intelligence (AI) is becoming smarter day by day in all business functions to elevate performances. AI is used widely in gaming, media, finance, robotics, quantum science, autonomous vehicles, and medical diagnosis. AI technology is a crucial prerequisite of much of the digital transformation taking place today as organizations position themselves to capitalize on the ever-growing amount of data being generated and collected. To build a successful career in Artificial Intelligence (AI), this course is intended to give a complete understanding of Artificial Intelligence concepts. This course offers you get practical, hands-on experience to ensure hassle-free execution of real-life projects. This AI course leverages world-class industry expertise in making you professional data science experts.

What skills will you learn?

In this Artificial Intelligence (AI) course, you will be able to

  • Understand the basics of AI and how these technologies are re-defining the AI industry
  • Learn the key terminology used in AI space
  • Learn major applications of AI through use cases

Who should take this course?

Neo Academy Pro’s course on Artificial Intelligence (AI) gives you the basic knowledge of Artificial Intelligence. This course doesn’t need any programming skills and best suited for:

  • Well-suited for management and non-technical participants
  • Students who want to learn Artificial Intelligence
  • Newbies who are not familiar with AI or its implications
Course Curriculum

Module 1-Introduction TO Machine Learning
  • Train,Test & Validation Distribution
  • ML Strategy
  • Computation Graph
  • Evaluation Metric
  • Human Level Performance
Module 2-Machine Learning

Supervised

  • Linear Regression
  • Logistic Regression
  • Gradient Descent
  • Decision Tree
  • Random Forest
  • Bagging & Boosting
  • KNN

Unsupervised

  • K-Means
  • Hierarichal Clustering
Module 3-Python Programming
  • Basic Programming
  • NLP Libraries
  • OpenCV
Module 4-Mathematics Foundation

Basic Statics

  • Sampling & Sampling Statistics
  • Hypothesis Testing

Calculus

  • Derivatives
  • Optimization

Linear Algebra

  • Function
  • Scalar-Vector-Matrix
  • Vector Operation

Probability

  • Space
  • Probability
  • Distribution
Module 5-Intro To Neural Network&Deep Learning

Introduction

  • Intro
  • Deep Learning Importance [Strength & Limiltation]
  • SP | MLP

Feed Forward & Backward Propagation

  • Neural Network Overview
  • Neural Network Representation
  • Activation Function
  • Loss Function
  • Importance of Non-linear Activation Function
  • Gradient Descent for Neural Network
Module 6-Parameter&Hyperparameter

Practical Aspect

  • Train, Test & Validation Set
  • Vanishing & Exploding Gradient
  • Dropout
  • Regularization

Optimization

  • Bias Correction
  • RMS Prop
  • Adam,Ada,AdaBoost
  • Learning Rate
  • Tuning
  • Softmax
Module 7-Data Processing

Environment

  • Scikit Learn
  • NLTK
  • Spacy & Gensim
  • OpenCV
  • Tensorflow
  • Keras

Text-Processing

  • Representation
  • Data Cleaning
  • Data Preprocessing
  • Similarity

Image Processing

  • Image
  • Image Transformation
  • Filters
  • Noise Removal
  • Correlation & Convolution
  • Edge Detection
  • Non Maximum Suppression & Hysterisis
  • Fourier Domain
  • Video Processing

Speech Data Analytics Feature Extraction

  • Image Feature
  • Descriptors

Object Detection

  • Detection & Classification
Module 8-CNNg

CNN

  • Computer Vision
  • Padding
  • Convolution
  • Pooling
  • Why Convolution

Deep Convolution Model

  • Case Studies
  • Classic Networks
  • Inception
  • Open Source Implementation
  • Transfer Learning

Detection Algorithm

  • Object Localization
  • Landmark Detection
  • Object Detection
  • Bounding Box Prediction
  • Yolo

Face Recognition

  • What is Face Recognition
  • One Shot Learning
  • Siamese Network
  • Triplet Loss
  • Face Verification
  • Neural Style Transfer
  • Deep Conv Net Learning
Module 9-RNN
  • Why Sequence Model
  • RNN Model
  • Backpropogation through time
  • Different Type of RNNs
  • GRU
  • LSTM
  • Biderectional LSTM
  • Deep RNN
  • Word Embedding
  • Debiasing
  • Negative Sampling
  • Elmo & Bert
  • Beam Search
  • Attention Model
Module 10-Generative adversial Network
  • Autoencoders & Decoders
  • Adversial Network
  • Active Learning
Module 11-Reinforcement Learning
  • Q Learning
  • Exploration & Exploitation
Module 12-Assignments

Introduction to Machine Learning

  • Business Case evaluation
  • Data requirements and collection
  • Evaluation metrics

Machine Learning

  • Profit of 50_startups data prediction
  • Extra marital affair prediction
  • Fraud data analytics
  • Fabric sales analysis
  • Classification of animals data
  • Crime data analysis using clustering method and airlines data to obtain optimum number of clusters.

Python Programming

  • Resource Information Analysis
  • Text Cleaning of Customer reviews using NLP
  • Image Manipulation (Loading, Rotation etc.)

Mathematics Foundation

  • Sampling & Sampling Statistics
  • Hypothesis Testing
  • Calculus Problems
  • Linear Algebra Problems
  • Probability Problems

Intro to Neural Network & Deep Learning

Parameter & Hyperparameter

  • Risk Evaluation
  • Prediction of claim amount
  • Emotor temp prediction
  • User Behavioural Pattern

Data Processing

  • User review data load and familiriaty with data and environment
  • E commerce Product Similarity
  • Sentiment classification of movie reviews
  • Emotion Mining of user reviews"
  • Vehicle edge detection
  • Cleaning of hand-written digits data
  • Image data Augumentation
  • Facial feature detection
  • Image data wrangling for classification
  • Video Analysis of a short film
  • Speech data Analysis w.r.t emotion

CNN

  • Ecommerce product image classification
  • Disease prediction based on images (2 CNN algorithms)
  • Vehicle identification(Object Detection)
  • Animal Classification(Object Classification
  • Spatial Image classification (Image segmentation)
  • Face detection
  • Face recognition (Attendance using facial recognition)

RNN

  • Next word prediction (Vanilla RNN)
  • Twitter data analysis using Named Entity Recognition(NER)
  • Retail data - Word2vec
  • NER and Forecasting of Oil price prediction
  • Auto text composer (NER language model)
  • Auto text composer (NER language model)
  • Q and A Chatbot
  • Real life voice Recognition

Generative

  • Machine Translation
  • New Image generation based on existing images

Reinforcement Learning

  • Game Intelligence
Module 13-Projects

1.Chatbot project

  • Build end to end chatbot right from data storage schema to final output for a domain

2.Emotion Analytics

  • Identifying and analyzing the full spectrum of human emotions including mood, attitude and emotional personality.

3.Object Detection

  • Detection of objects in images

4.Face detection from CC camera feed

  • Analysis of video feed from CC cameras
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