Machine Learning Using Prutor
(for Institute of Management Studies, Ghaziabad)
Benefits:
- Time-saving & Cost-effective
- Get trained via industry experts
- Full of hands-on practical exposure for better understanding
- Adding super solid value in your professional career
- Internship with project letter
Batch Details
| Class | Training Duration | No of days |
| Daily | 1 Hour | 30 |
Batch starting from
1st May, 2020
*Rolling enrollment also available
Machine Learning
Before University Opens:
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)
After University Opens:
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