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.
Certification
1. All successful participants will be provided with a certificate of completion (except for demo courses).
2. Students who do not complete the course / leave it midway will not be awarded any certificate.
Topics to be covered
Intro / Overview / Definitions
This is only a demo course. The full course can be bought here
| Syllabus | |||||
|---|---|---|---|---|---|
| 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. Sklearn |
| 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 | ||
