Professional Course in AI and Data Engineering

Different packages can be used to build Deep Learning and Artificial Intelligence models, such as Tensorflow, Keras, OpenCV, and PyTorch. You will learn more about these packages and their applications in detail.

Tensorflow and Keras libraries can be used to build Machine Learning and Deep Learning models. OpenCV is used for image processing and PyTorch is highly useful when you have no idea how much memory will be required for creating a Neural Network Model.

• Introduction to Deep Learning libraries – Torch, Theono, Caffe, Tensorflow, Keras, OpenCV and PyTorch
• Deep dive into Tensorflow, Keras, OpenCV and PyTorch
• Introduction to Anaconda, R, R studio, Jupyter and Spyder
• Environment Setup and Installation Methods of Multiple Packages

Understand the types of Machine Learning Algorithms. Learn about the life cycle and the detailed understanding of each step involved in the project life cycle. The CRISP-DM process is applied in general for Data Analytics /AI projects. Learn about CRISP-DM and the stages of the project life cycle in-depth.

You will also learn different types of data, Data Collection, Data Preparation, Data Cleansing, Feature Engineering, EDA, Data Mining and various Error Functions. Understand about imbalanced data handling techniques and algorithms.

• Introduction to Machine Learning
• Machine Learning and its types - Supervised Learning, Unsupervised Learning, Reinforcement Learning, Semi-supervised Learning, Active Learning, Transfer Learning, Structured Prediction
• Understand Business Problem – Business Objective & Business Constraints
• Data Collection - Surveys and Design of Experiments
• Data Types namely Continuous, Discrete, Categorical, Count, Qualitative, Quantitative and its identification and application
• Further classification of data in terms of Nominal, Ordinal, Interval & Ratio types
• Balanced vs Imbalanced datasets
• Cross-Sectional vs Time Series versus Panel / Longitudinal Data
• Batch Processing versus Real-Time Processing
• Structured vs Unstructured vs Semi-Structured Data
• Big vs Not-Big Data
• Data Cleaning / Preparation - Outlier Analysis, Missing Values Imputation Techniques, Transformations, Normalization / Standardization, Discretization
• Sampling Techniques for Handling Balanced vs Imbalanced Datasets
• Measures of Central Tendency & Dispersion
  • Mean/Average, Median, Mode
  • Variance, Standard Deviation, Range
• Various Graphical Techniques to Understand Data
  • Bar Plot
  • Histogram
  • Boxplot
  • Scatter Plot
• Feature Engineering - Feature Extraction & Feature Selection
• Error Functions - Y is Continuous -  Mean Error, Mean Absolute Deviation, Mean Squared Error, Mean Percentage Error, Root Mean Squared Error, Mean Absolute Percentage Error
• Error Functions - Y is Discrete - Cross Table, Confusion Matrix, Binary Cross Entropy & Categorical Cross-Entropy
• Machine Learning Projects Strategy

Maximize or minimize the error rate using Calculus. Learn to find the best fit line using the linear least-squares method. Understand the gradient method to find the minimum value of a function where a closed-form of the solution is not available or not easily obtained.

Under Linear Algebra, you will learn sets, function, scalar, vector, matrix, tensor, basic operations and different matrix operations. Under Probability one will learn about Uniform Distribution, Normal Distribution, Binomial Distribution, Discrete Random Variable, Cumulative Distribution Function and Continuous Random Variables.

• Optimizations - Applications
• Foundations - Slope, Derivatives & Tangent
• Derivatives in Optimization
• Maxima & Minima - First Derivative Test, Second Derivative Test, Partial Derivatives, Cross Partial Derivatives, Saddle Point, Determinants, Minor and Cofactor
• Linear Regression Ordinary Least Squares using Calculus

You will have a high-level understanding of the human brain, importance of multiple layers in the Neural Network, extraction of features layers wise, composition of the data in Deep Learning using an image, speech and text.

You will briefly understand feature extraction using SIFT/HOG for images, Speech recognition and feature extraction using MFCC and NLP feature extraction using parse tree syntactic.

Introduction to neurons, which are connected to weighted inputs, threshold values, and an output. You will understand the importance of weights, bias, summation and activation functions.

• Human Brain – Introduction to Biological & Artificial Neuron
• Compositionality in Data – Images, Speech & text
• Mathematical Notations
• Introduction to ANN
• Neuron, Weights, Activation function, Integration function, Bias and Output

Learn about single-layered Perceptrons, Rosenblatt’s perceptron for weights and bias updation. You will understand the importance of learning rate and error. Walkthrough a toy example to understand the perceptron algorithm. Learn about the quadratic and spherical summation functions. Weights updating methods - Windrow-Hoff Learning Rule & Rosenblatt’s Perceptron.

• Introduction to Perceptron
• Introduction to Multi-Layered Perceptron (MLP)
• Activation functions – Identity Function, Step Function, Ramp Function, Sigmoid Function, Tanh Function, ReLU, ELU, Leaky ReLU & Maxout
• Back Propagation Visual Demonstration
• Network Topology – Key characteristics and Number of layers
• Weights Calculation in Back Propagation

Understand the difference between perception and MLP or ANN. Learn about error surface, challenges related to gradient descent and the practical issues related to deep learning. You will learn the implementation of MLP on MNIST dataset - multi-class problem, IMDB dataset - binary classification problem, Reuters dataset - single labelled multi-class classification problem and Boston Housing dataset - Regression Problem using Python and Keras.

• Error Surface – Learning Rate & Random Weight Initialization
• Local Minima issues in Gradient Descent Learning
• Is DL a Holy Grail? Pros and Cons
• Practical Implementation of MLP/ANN in Python using Real Life Use Cases
• Segregation of Dataset - Train, Test & Validation
• Data Representation in Graphs using Matplotlib
• Deep Learning Challenges – Gradient Primer, Activation Function, Error Function, Vanishing Gradient, Error Surface challenges, Learning Rate challenges, Decay Parameter, Gradient Descent Algorithmic Approaches, Momentum, Nestrov Momentum, Adam, Adagrad, Adadelta & RMSprop
• Deep Learning Practical Issues – Avoid Overfitting, DropOut, DropConnect, Noise, Data Augmentation, Parameter Choices, Weights Initialization (Xavier, etc.)

You will learn image processing techniques, noise reduction using moving average methods, different types of filters - smoothing the image by averaging, Gaussian filter and the disadvantages of correlation filters. You will learn about different types of filters, boundary effects, template matching, rate of change in the intensity detection, different types of noise, image sampling and interpolation techniques.

• Introduction to Vision
• Importance of Image Processing
• Image Processing
• Image Transformation, Image Processing Operations & Simple Point Operations
• Noise Reduction – Moving Average & 2D Moving Average
• Image Filtering – Linear & Gaussian Filtering
• Disadvantage of Correlation Filter
• Introduction to Convolution
• Boundary Effects – Zero, Wrap, Clamp & Mirror
• Image Sharpening
• Template Matching
• Edge Detection – Image filtering, Origin of Edges, Edges in images as Functions, Sobel Edge Detector
• Effect of Noise
• Laplacian Filter
• Smoothing with Gaussian
• LOG Filter – Blob Detection
• Noise – Reduction using Salt & Pepper Noise using Gaussian Filter
• Nonlinear Filters
• Bilateral Filters
• Canny Edge Detector - Non Maximum Suppression, Hysteresis Thresholding
• Image Sampling & Interpolation – Image Sub Sampling, Image Aliasing, Nyquist Limit, Wagon Wheel Effect, Down Sampling with Gaussian Filter, Image Pyramid, Image Up Sampling
• Image Interpolation – Nearest Neighbour Interpolation, Linear Interpolation, Bilinear Interpolation & Cubic Interpolation
• Introduction to the deep neural networks module
• Modules of Self-driving cars

Convolution Neural Networks are the class of Deep Learning networks which are mostly applied on images. You will learn about ImageNet challenge, an overview on ImageNet winning architectures, applications of CNN, problems of MLP with the huge dataset.

You will understand convolution of filter on images, basic structure on the convent, details about Convolution layer, Pooling layer, Fully Connected layer, Case study of AlexNet and few of the practical issues of CNN.

• ImageNet Challenge – Winning Architectures, Difficult Vision Problems & Hierarchical Approach
• Parameter Explosion with MLPs
• Convolution Networks - 1D ConvNet, 2D ConvNet, Transposed Convolution
• Convolution Layers with Filters and Visualizing Convolution Layers
• Pooling Layer, Padding, Stride
• Transfer Learning - VGG16, VGG19, Resnet, GoogleNet, LeNet, etc.
• Practical Issues – Weight decay, Drop Connect, Data Manipulation Techniques & Batch Normalization

You will be able to build the models using Faster RCNN and YOLO. Understand why fast YOLO is a better choice while dealing with object detection. You will also be able to understand the model optimization using OpenVINO

• R-CNN
• Fast R-CNN
• Faster R-CNN
• Mask R-CNN
• YOLO and Types of YOLO
• OpenVINO Toolkit
  • Introduction
  • Model Optimization of pre-trained models
  • Inference Engine and Deployment process

Understand the language models for next word prediction, spell check, mobile auto-correct, speech recognition, and machine translation. You will learn the disadvantages of traditional models and MLP. Deep understanding of the architecture of RNN, RNN language model, backpropagation through time, types of RNN - one to one, one to many, many to one and many to many along with different examples for each type.

• Introduction to Adversaries
• Language Models – Next Word Prediction, Spell Checkers, Mobile Auto-Correction, Speech Recognition & Machine Translation
• Traditional Language model
• Disadvantages of MLP
• Introduction to State & RNN cell
• Introduction to RNN
• RNN language Models
• Back Propagation Through time
• RNN Loss Computation
• Types of RNN – One to One, One to Many, Many to One, Many to Many
• Introduction to the CNN and RNN
• Combining CNN and RNN for Image Captioning
• Architecture of CNN and RNN for Image Captioning
• Bidirectional RNN
• Deep Bidirectional RNN
• Disadvantages of RNN

Understand and implement Long Short-Term Memory, which is used to keep the information intact, unless the input makes them forget. You will also learn the components of LSTM - cell state, forget gate, input gate and the output gate along with the steps to process the information. Learn the difference between RNN and LSTM, Deep RNN and Deep LSTM and different terminologies. You will apply LSTM to build models for prediction.

• Introduction to LSTM – Architecture
• Importance of Cell State, Input Gate, Output Gate, Forget Gate, Sigmoid and Tanh
• Mathematical Calculations to Process Data in LSTM
• RNN vs LSTM - Bidirectional vs Deep Bidirectional RNN
• Deep RNN vs Deep LSTM

Gated Recurrent Unit, a variant of LSTM solves this problem in RNN. You will learn the components of GRU and the steps to process the information.

• Introduction to GRU
• Architecture & Gates - Update Gate, Reset Gate, Current Memory Content, Final Memory at Current Timestep
• Applications of GRUs

You will learn something beyond the sequential models, learn to inspect and monitor deep learning models using Keras call-backs and TensorBorad.

• Introduction to the functional API
• Multi-input Models
• Multi-output Models
• Directed acyclic graphs of layers
• Using callbacks to act on a model during training
• Introduction to TensorBoard: the TensorFlow Visualization Framework
• Hyperparameter Optimization
• Model ensembling

You will understand the advancements in LSTMs, learn about the language translation models, attention mechanism, transformer architecture and its implementation.

• Introduction to LSTMs disadvantages
• Seq2Seq model and its components
• Implementation of Seq2Seq
• Introduction to transformers
• Architecture of Transformers
• Types of Transformers

Learn to Build a chatbot using generative models and retrieval models. We will understand RASA open-source and BERT to build chatbots.

• Introduction to Chatbot
• NLP Implementation in Chatbot
• Integrating and implementing Neural Networks Chatbot
• Train Q and A BERT to build a chatbot

Learn to Build a speech to text and text to speech models. You will understand the steps to extract the structured speech data from a speech, convert that into text. Later use the unstructured text data to convert into speech.

• Speech Recognition Pipeline
• Phonemes
• Pre-Processing
• Acoustic Model
• Deep Learning Models
• Decoding

You will learn Q-learning which is a type of reinforcement learning, exploiting using the creation of a Q table, randomly selecting an action using exploring and steps involved in learning a task by itself.

• Reinforcement Learning
• Deep Reinforcement Learning vs Atari Games
• Maximizing Future Rewards
• Policy vs Values Learning
• Balancing Exploration With Exploitation
• Experience Replay, or the Value of Experience
• Q-Learning and Deep Q-Network as a Q-Function
• Improving and Moving Beyond DQN
• Keras Deep Q-Network

You will learn about the components of Autoencoders, steps used to train the autoencoders to generate spatial vectors, types of autoencoders and generation of data using variational autoencoders. Understanding the architecture of RBM and the process involved in it.

• Intuition
• Introduction to Architecture of Autoencoders
• Comparison with other Encoders (MP3 and JPEG)
• Introduction to Deep Autoencoders
• Deep Autoencoders
• Convolutional Autoencoders
• Variational Autoencoders
• Implementation of Autoencoders

Understanding the generation of data using GAN, the architecture of the GAN - encoder and decoder, loss calculation and backpropagation, advantages and disadvantages of GAN.

• Introduction to Generative Adversarial Networks (GANS)
• Building Model
• Model Inputs and Hyperparameters
• Defining the Generator and Discriminator
• Model Training
• Model Optimizer
• Discriminator and Generator Losses
• Implementation of GAN
• Types of GANs
  • Deep Convolution GAN
  • Conditional GAN
  • Info GAN
  • SRGAN
  • 3D-GAN

Understand the unsupervised neural network models and the process involved in it.

• Introduction to Boltzmann Machines
• Energy Function
• Introduction to Restricted Boltzmann Machine
• Training process of RBM
• Loss functions
• Introduction to Deep Belief Networks
• Applications of DBN

Learn the tools which automatically analyzes your data and generates candidate model pipelines customized for your predictive modeling problem.

• AutoML Methods
  • Meta-Learning
  • Hyperparameter Optimization
  • Neural Architecture Search
  • Network Architecture Search
• AutoML Systems
  • MLBox
  • Auto-Net 1.0 & 2.0
  • Hyperas
• AutoML on Cloud - AWS
  • Amazon SageMaker
  • Sagemaker Notebook Instance for Model Development, Training and Deployment
  • XG Boost Classification Model
  • Training Jobs
  • Hyperparameter Tuning Jobs
• AutoML on Cloud - Azure
  • Workspace
  • Environment
  • Compute Instance
  • Compute Targets
  • Automatic Featurization
  • AutoML and ONNX
• AutoML on Cloud - GCP
  • AutoML Natural Language
  • Performing Document Classification
  • AutoML Version API's for Image Classification
  • Performing Sentiment Analysis using AutoML Natural Language API
  • Tensor-Flow Models Using Cloud ML Engine
  • Cloud ML Engine and Its Components
  • Training and Deploying Applications on Cloud ML Engine
  • Choosing Right Cloud ML Engine for Training Jobs

Learn the methods and techniques which can explain the results and the solutions obtained by using deep learning algorithms.

• Introduction to XAI - Explainable Artificial Intelligence
• Why do we need it?
• Levels of Explainability
  • Direct Explainability
    • Simulatability
    • Decomposability
    • Algorithmic Transparency
  • Post-hoc Explainability
    • Model-Agnostic Algorithms
      • Explanation by simplification (Local Interpretable Model-Agnostic Explanations (LIME))
      • Feature relevance explanation
        • SHAP
        • QII
        • SA
        • ASTRID
        • XAI
      • Visual Explanations
• General AI vs Symbolic Al vs Deep Learning

• Intro to Data Engineering
• Data Science vs Data Engineering
• Building Data Engineering Infrastructure
• Working with Databases and various File formats (Data Lakes)
  • SQL
    • MySQL
    • PostgreSQL
  • NoSQL
    • MongoDB
    • HBase
    • Apache Cassandra
  • Cloud Sources
    • Microsoft Azure SQL Database
    • Amazon Relational Database Service
    • Google Cloud SQL
    • IBM Db2 on Cloud
• Extra-Load, Extract-Load-Transform, or Extract-Transform-Load paradigms
• Preprocessing, Cleaning, and Transforming Data
• Cloud Data Warehouse Service
  • AWS: Amazon Redshift
  • GCP: Google Big Query
  • IBM: Db2 Warehouse
  • Microsoft: Azure SQL Data Warehouse
• Distributed vs. Single Machine Environments
• Distributed Framework - Hadoop
  • Various Tools in Distributed Framework to handle BigData
    • HBase
    • Kafka
    • Spark
    • Apache NiFi
  • Distributed Computing on Cloud
    • ML and AI platforms on Cloud
• Databases and Pipelines
  • Data Pipeline
    • Features of Pipelines
    • Building a pipeline using NiFi
• Installing and Configuring the NiFi Registry
  • Using the Registry in NiFi
  • Versioning pipelines
  • Monitoring pipelines
  • Monitoring NiFi using GUI
  • Using Pything with the NiFi REST API
• Building pipelines in Apache Airflow
  • Airflow boilerplate
  • Run the DAG
  • Run the data pipelines
• Deploy and Monitor Data Pipelines
  • Production Data Pipeline
  • Creating Databases
• Data Lakes
  • Populating a data lake
  • Reading and Scanning the data lake
  • Insert and Query a staging database

• Building a Kafka Cluster
• Setup Zookeeper and Kafka Cluster
• Configuring and Testing Kafka Cluster
• Streaming Data with Apache Kafka
• Data Processing with Apache Spark
• Real-Time Edge Data with MiNiFi, Kafka, and Spark