Artificial Intelligence

19,900.00 14,900.00

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

Course Overview


100 Hours (50 Hours LIVE Teaching + 50 Hours of assignments and project work)

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 Merit / Achievement from RCPL INDIA
2. Students who complete the course BUT do not take the final test will be provided with a certificate of participation from RCPL INDIA
3. Students who do not complete the course / leave it midway will NOT be awarded any certificate.


Online Live

Tentative Date & Schedule

1st and 15th of every month


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 limitations of a Single Perceptron
    • Deepening the network
    • Images and Pixels
    • How humans recognise 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
    • Visualising NN using Tensorflow
    • Tensorboard
  14. Convolutional Neural Networks (CNN)
    • Introduction to CNNs
    • CNNs Application
    • Architecture of a CNN
  15. Keras API
    • How to compose Models in Keras
    • Sequential Composition
    • Functional Composition
    • Predefined Neural Network Layers


Knowledge of Machine learning is mandatory.

General Information
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