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Artificial intelligence and machine learning

Job Opportunities:

Artificial intelligence is exploding and growing at a 30% annual rate. This field offers the highest level of salary among all IT jobs.

Course Benefits:

This is a job-oriented course where a student will learn entry-level job skills for AI, such as training with public or private data sets, using public AI libraries, etc. We are supported by a consortium of companies looking for these skills, and thus upon completion, we will send successful students for various job interviews.

Introduction:

Artificial Intelligence (AI) and Machine Learning (ML) are two rapidly advancing technologies transforming how we interact with technology and the world around us.AI refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and natural language processing.ML is a subset of AI that involves training machines to learn from data and improve their performance on specific tasks without being explicitly programmed.

The scope of AI/ML is vast, and its applications are revolutionizing various industries. Companies are increasingly adopting AI/ML to optimize their operations, improve efficiency, and increase revenue in the current business landscape. Some of the key areas where AI/ML is being used in existing companies include:

  1. Customer Service: Companies use AI/ML to provide personalized customer service through chatbots and virtual assistants, reducing wait times and improving customer satisfaction.

  2. Marketing and Sales: AI/ML analyzes consumer data and predicts buying behaviors, allowing companies to target the right audience with personalized recommendations and offers.

  3. Manufacturing and Logistics: AI/ML is used to optimize supply chain management, production planning, and predictive maintenance, reducing downtime and improving overall efficiency.

  4. Healthcare: AI/ML is used to develop diagnostic tools, personalized treatment plans, and predictive analytics, improving patient outcomes and reducing costs.

  5. Finance: AI/ML is used to detect fraud, automate financial processes, and provide personalized investment advice, improving customer experiences and reducing risks.
Overall, the scope of AI/ML is vast, and its applications are rapidly expanding across industries. As companies continue to adopt these technologies, the possibilities for innovation and growth are endless.

Course Content:


  • Module 1

    1. Basic image processing: image-read, image-write, image transformation, enhancement, edge detection using open CV.
    2. Basic of Neural Networks: Types of Neural Networks (ANN, CNN, RNN), learnings in Neural networks (Supervised Learning, Unsupervised Learning, Reinforcement Learning), Input, Deep Neural Networks (DNN), Weights, Bias, Activation function, Mathematical Operation on one Neuron, Neural Network Design.

  • Module 2

    1. PyTorch: Installation,
    2. Tensor: Attributes of a Tensor, Operations on Tensors, Bridge with NumPy, NumPy array to Tensor.
    3. Datasets & Dataloaders: dataset Loading, Iterating, and Visualizing the Dataset, Creating a Custom data loader, and Data Transformation.
    4. Model Design: the Device for Training, Design ANN and CNN Model, Model Parameters, Model Layers, and torch. Nn API.
    5. Model Training: Optimizing the model parameters, hyperparameters, Optimization Loop, Loss Function, Optimizer, save, and load models.

  • Module 3

    1. Convolutional Neural Network: Introduction, Layers in a Convolutional Neural Network: Convolution layer, ReLU layer, Pooling layer, fully connected layer
    2. Loss Function: Binary Cross Entropy, Categorial Cross Entropy, Connectionist Temporal Classification (CTC), Custom Loss function.
    3. Optimizer: Adam, Adagrad, AdaDelta, Gradient Descent, Stochastic Gradient Descent.

  • Module 4

    1. CNN Architecture: Alex Net, VGG, GoogLeNet, ResNet, Inception Net.
    2. Recurrent Neural Networks: RNN, LSTM, GRU.
    3. Generative Adversarial Network: Deep convolutional GAN, Self-attention GAN (SAGAN), Variational auto-encoder GAN (VAEGAN), Transformer GAN (TransGAN), Flow-GAN.
    4. Transformer: Introduction, Architecture, PyTorch Implementation (BERT, ChatGPT, Vision transformer)

  • Module 5

    1. Classification: K-means classification, MNIST Digit Classification, Dog classifications, Fashion Data classification.
    2. Segmentation: Dataset, UNet, FastFCN, Gated-SCNN, PSPNet, DeepLab, Mask RNN.
    3. Object Detection and Recognition: Dataset, Custom data annotation, Yolov5, Yolov7, Yolov8, Mobile Net, Efficient Net.
    4. Pose Estimation and activity recognition: Dataset, Human Body part segmentation, Skelton Detection.

  • Module 6

    1. Application: Deployment of AI Model in jetson nano,Face Detection, Face Recognition, vehicle detection, License Plate Recognition, Vehicle speed estimation, Virtual Air writing, Hand gesture recognition, and Fashion Style Transfer.

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