Artificial Intelligence Machine Learning (SPT)

Artificial Intelligence Machine Learning (SPT)


18% GST Extra

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

It will be announced soon...

Topics to be covered
  1. Welcome to Machine Learning
    • Introduction To Machine Learning
    • History and Evolution
    • Artificial Intelligence Evolution
    • Find out where Machine Learning is applied in Technology and Science.
  2. Different Forms of Machine Learning
    • Statistics, Data Mining, Data Analytics, Data Science
  3. Machine Learning Categories
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
  4. Machine Learning Python Packages
    • Data Analysis Packages
    • NumPy
    • SciPy
    • Matplotlib
    • Pandas
    • Slkearn
  5. Supervised Learning
    • Regression
    • Classification
    • Generalization, Overfitting, and Underfitting
  6. Classification
    • Classification
  7. Regression
    • Understand how continuous supervised learning is different from discrete learning.
    • Code a Linear Regression in Python with scikit-learn.
    • Understand different error metrics such as SSE, and R Squared in the context of Linear Regressions.
  8. Supervised Machine Learning Algorithms
    • k-Nearest Neighbor
    • Linear models
    • Naive Bayes Classifiers
    • Decision trees
    • Support Vector Machines
  9. Unsupervised Learning and Preprocessing
    • Challenges in unsupervised learning
    • Preprocessing and Scaling
    • Applying data transformations
    • Scaling training and test data the same way
  10. Dimensionality Reduction, Feature Extraction, and Manifold Learning
    • Principal Component Analysis (PCA)
  11. Introduction to Deep Learning
    • A revolution in Artificial Intelligence
    • Limitations of Machine Learning
    • What is Deep Learning?
    • Advantage of Deep Learning over Machine learning
  12. Introduction To Neural Networks with TensorFlow
    • How Deep Learning Works?
    • Activation Functions
    • Training a Perceptron
    • TensorFlow code-basics
    • Tensorflow data types
    • CPU vs GPU vs TPU
    • Tensorflow methods
    • Introduction to Neural Networks
    • Neural Network Architecture
    • Linear Regression example revisited
    • The Neuron
    • Neural Network Layers
    • The MNIST Dataset
    • Coding MNIST NN
  13. Deep dive into Neural Networks with TensorFlow
    • Understand the limitations of a Single Perceptron
    • Deepening the network
    • Images and Pixels
    • How humans recognize images
    • Convolutional Neural Networks
    • ConvNet Architecture
    • Overfitting and Regularization
    • Max Pooling and ReLU activations
    • Dropout
    • Strides and Zero Padding
    • Coding Deep ConvNets demo
    • Debugging Neural Networks
    • Visualizing NN using Tensorflow
    • Tensorboard
  14. Convolutional Neural Networks (CNN)
    • Introduction to CNNs
    • CNNs Application
    • The architecture of a CNN
  15. Keras API
    • How to compose Models in Keras
    • Sequential Composition
    • Functional Composition
    • Predefined Neural Network Layers
  • 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

Summer Online Live Training Program 2020

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