Artificial Intelligence Course in Hyderabad

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 for Windows, R studio 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 versus Imbalanced datasets
• Cross-Sectional versus Time Series versus Panel / Longitudinal Data
• Batch Processing versus Real-Time Processing
• Structured versus Unstructured vs Semi-Structured Data
• Big versus Not-Big Data
• Data Cleaning / Preparation - Outlier Analysis, Missing Values Imputation Techniques, Transformations, Normalization / Standardization, Discretization
• Sampling Techniques for Handling Balanced versus Imbalanced Datasets
• Measures of Central Tendency & Dispersion
  • Population Parameters and Sample Statistics
  • 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 - Mean Error, Mean Absolute Deviation, Mean Squared Error, Mean Percentage Error, Root Mean Squared Error, Mean Absolute Percentage Error, Cross Table, Confusion Matrix, Binary Cross Entropy & Categorical Cross-Entropy
• High-Level Strategy in Handling Machine Learning Projects

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
• Gradient Descent Method / Optimization - Minima, Maxima & Learning Rate

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 neuron
• Compositionality in Data – Images, Speech & text
• Mathematical Notations
• Introduction to ANN
• Components of 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. Walk through 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
• Activation functions – Identity Function, Step Function, Ramp Function, Sigmoid Function, Tanh Function, ReLU, ELU, Leaky ReLU & Maxout
• Back Propagation Demo
• 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?
• Practical Implementation of MLP/ANN in Python – MNIST, IMDB, Reuters & Boston Housing
• Segregation of data set: 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

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

You will understand convolution of filter on images, basic structure on 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 Problem with MLPs
• Convolution Networks
• Convolution Layers with Filters
• Pooling Layer
• Case Study: Alex Net
• Practical Issues – Weight decay, Drop Connect, Data Manipulation Techniques & Batch Normalization

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.

You will also learn about colors and intensity, affine transformation, projective transformation, embossing, erosion & dilation, vignette, histogram equalization, HAAR cascade for object detection, SIFT, SURF, FAST, BRIEF and seam carving.

• Introduction to Vision
• Importance of Image Processing
• Image Processing Challenges – Interclass Variation, ViewPoint Variation, Illumination, Background Clutter, Occlusion & Number of Large Categories
• Introduction to Image – 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

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’s
• 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

You will learn to build an object detection model using Fast R-CNN by using bounding boxes, understand why fast RCNN is a better choice while dealing with object detection. You will also learn by instance segmentation problems which can be avoided using Mask RCNN.

• R-CNN
• Fast R-CNN
• Faster R-CNN
• Mask R-CNN

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
• 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 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.

• Autoencoders
  • Intuition
  • Comparison with other Encoders (MP3 and JPEG)
  • Implementation in Keras
• Deep AutoEncoders
  • Intuition
  • Implementing DAE in Keras
• Convolutional Auto encoders
  • Intuition
  • Implementation in Keras
• Variational Autoencoders
  • Intuition
  • Implementation in Keras
• Introduction to Restricted Boltzmann Machines - Energy Function, Schematic implementation, Implementation in TensorFlow

You will learn the difference between CNN and DBN, architecture of deep belief networks, how greedy learning algorithms are used for training them and applications of DBN.

• Introduction to DBN
• Architecture of DBN
• Applications of DBN
• DBN in Real World

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

• Generative Adversarial Networks (GANS)
• Data Analysis and Pre-Processing
• Building Model
• Model Inputs and Hyperparameters
• Model losses
• Implementation of GANS
• Defining the Generator and Discriminator
• Generator Samples from Training
• Model Optimizer
• Discriminator and Generator Losses
• Sampling from the Generator

You will learn to use SRGAN which uses the GAN to produce the high-resolution images from the low-resolution images. Understand about generators and discriminators.

• Introduction to SRGAN
• Network Architecture - Generator, Discriminator
• Loss Function - Discriminator Loss & Generator Loss
• Implementation of SRGAN in Keras

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 versus 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

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

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

• Introduction to Chatbot
• NLP Implementation in Chatbot
• Integrating and implementing Neural Networks Chatbot
• Generative Chatbot Development
• Building a Retrieval Based Chatbot
• Deploying Chatbot in Various Platforms