Artificial Intelligence + Deep Learning (with Project Letter)

Artificial Intelligence + Deep Learning (with Project Letter)


Course Fee Including 18% GST

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Target Audience

  • B.Tech/MCA/BCA/M.Tech Students
  • Working Professionals from Corporate

Test & Evaluation

1. During the program, the participants will have to take all the assignments given to them for better learning.

2. At the end of the program, a final assessment will be conducted.


1. All successful participants will be provided with a certificate of completion.

2. Students who do not complete the course / leave it midway will not be awarded any certificate.

Tentative Date & Schedule

New batch starting from 29th June 2020 for the live online tutorial session and doubt clearing sessions

Topics to be covered

1. Introduction to Machine Learning using scikit-learn days (1-8)

  • Overview of Machine Learning
  • Difference between AI, ML, and DL
  • Applications of ML and DL
  • Types of Machine Learning
  • Linear Regression
  • Logistic Regression
  • Overfitting and underfitting
  • K-Nearest Neighbor
  • Cross-validation and Hyper-parameter tuning
  • Confusion Matrix, Recall, Precision
  • K-Means Clustering

2. Introduction To Artificial Neural Network days (9-13)

  • What is Artificial Neural Network (ANN)?
  • How Neural Network Works?
  • Perceptron
  • Multilayer Perceptron
  • Feed Forward
  • Gradient Descent and Stochastic Gradient Descent
  • Back propagation

3. Introduction To Deep Learning days (14-15)

  • What is Deep Learning?
  • Deep Learning Packages
  • Deep Learning Applications
  • Building Deep Learning Environment
    • Installing TensorFlow Locally
    • Working with Google Colab

4. Indtroduction to TensorFlow days (16-18)

  • What is TensorFlow?
  • TensorFlow 1.x V/S TensorFlow 2.x
  • Placeholder, Variables, Constants
  • Operations using TensorFlow
  • Difference between TensorFlow and NumPy operations
  • Computational Graph
  • Visualizing Graph using Tensorboard

5. Activation Functions days (19-20)

  • What are Activation Functions?
  • Sigmoid Function,
  • Hyperbolic Tangent Function (tanh)
  • ReLU – Rectified Linear Unit
  • Softmax Function
  • Vanishing Gradient Problem

6. Building an Artificial Neural Network: A Case Study / An Example days (21-23)

  • Understanding MNIST Dataset
  • Initializing weights and biases
  • Defining loss/cost Function
  • Train the Neural Network
  • Minimizing the loss by adjusting weights and biases

7. Modern Deep Learning Optimizers and Regularization days (24-30)

  • SGD with Momentum
  • RMSprop
  • AdaGrad
  • Adam
  • Dropout Layers and Regularization
  • Batch Normalization

8. Building Deep Neural Network Using Keras days (31-33)

  • What is Keras?
  • Keras Fundamental For Deep Learning
  • Keras Sequential Model and Functional API
  • Solve a Linear Regression and Classification Problem with Example
  • Saving and Loading a Keras Model

9. Convolutional Neural Networks (CNNs) days (34-40)

  • Introduction to CNN
  • CNN Architecture
  • Convolutional Operations
  • Pooling, Stride, and Padding Operations
  • Data Augmentation
  • Building, Training and Evaluating First CNN Model
  • Model Performance Optimization
  • Autoencoders for CNN
  • Transfer Learning

10. Recurrent Neural Networks (RNNs) days (41-46)

  • Introduction to RNN
  • RNN Architecture
  • Types of RNN
  • Implementing basic RNN in TensorFlow
  • Need for LSTM and GRU
  • Deep RNN
  • Text Classification Using LSTM

11. Projects days (47-50)

  • Sentiment analysis using RNN
  • MNIST Handwritten digits classification using CNN
  • Cat vs Dog Image classification
  • Objects Detection from Yolov3

Sample Project:

(Hand Detection)

(Cat Dog Classification)

(MNIST Digit Prediction)

(Autocomplete search query in Tensorflow)

(ChatBot Using Tensorflow)

(Neural Machine Translation NMT Using Tensorflow)

(Text Extraction from Image)

No experience is required, But fundamental knowledge of C/C++ would be helpful.

  1. Upto six weeks (or till submission of the final quiz) access to the course
  2. To get access to the certificate - you need to take the online MCQ exam (minimum 60%) at the end of the course
  • Time-saving & Cost-effective
  • Get trained via industry experts (having 10+ years of experience in the same field, corporate trainers)
  • Full of hands-on practical exposure for better understanding
  • Adding super solid value in your professional career
  • Weekend Doubt clearing sessions.

For inquiry call:  9910043510

Online Live Training Program 2020