Artificial Intelligence + Machine Learning

Artificial Intelligence + Machine Learning


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...

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

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