3 Days Live Virtual Training on AI and Deep Learning with Python

Duration: 3 Days
Hours: 15 Hours
Training Level: All Level
Batch Three
Wednesday, November 15, 2023
11:00 AM - 04:00 PM (Eastern Time)
Live Session
Single Attendee
$299.00 $499.00
Live Session
Single Attendee
$349.00 $583.00
6 month Access for Recorded
Single Attendee
$399.00 $666.00
6 month Access for Recorded

About Artificial Intelligence (AI) with Python Course

The AI and Deep Learning with Python Certification course enables you to take your lastest skills like AI and Deep Learning into a variety of companies, helping them to apply these techniques on the data and make more informed business decisions. The course covers predictive analytics techniques with the Python language. You will learn about various Python packages like Tensorflow and Keras. This will give you a deep understanding on algorithms like Artificial Neural Networks, Convolutional Neural Networks and Recurrent neural networks. 

Artificial Intelligence (AI) with Python Course Objective

Install Python, Jupyter Notebook,and learn about the various Python packages

Gain an in-depth understanding of data structure used in Python and learn to import/export data in Python

Define, understand and use the various functions in Python

Learn Python packages like Tensorflow and Keras

Learn indepth knowledge on AI and Deep learning algorithms like ANN, CNN and RNN and its various use cases.

Who is the Artificial Intelligence (AI) with Python Course Audience?

This course is meant for all those students and professionals who are interested in using the Python's powerful ecosystem

What Basic Knowledge Required to Learn Artificial Intelligence (AI) with Python Course?

There are no prerequisites

Total Duration: 15 Hours
Introduction to Python
Introduction to Logistic Regression
Introduction to Artificial Neural Network

History of Neural networks and Deep Learning  

How Biological Neurons work?  

Growth of biological neural networks  

Diagrammatic representation: Logistic Regression and Perceptron  

Multi-Layered Perceptron (MLP)  


Training a single-neuron model  

Training an MLP: Chain Rule  
Training an MLP:Memoization  
Activation functions  
Vanishing Gradient problem  
Bias-Variance tradeoff  

Deep Multi-layer perceptrons

Deep Multi-layer perceptrons:1980s to 2010s  

Dropout layers & Regularization  

Rectified Linear Units (ReLU)  

Weight initialization  

Batch Normalization  

Optimizers:Hill-descent analogy in 2D  

Optimizers:Hill descent in 3D and contours  

SGD Recap  

Batch SGD with momentum  

Nesterov Accelerated Gradient (NAG)  


Optimizers : Adadelta andRMSProp  


Which algorithm to choose when?  

Gradient Checking and clipping  

Softmax and Cross-entropy for multi-class classification  

How to train a Deep MLP?  

Convolutional Neural Network

Biological inspiration: Visual Cortex  

Convolution:Edge Detection on images  

Convolution:Padding and strides  

Convolution over RGB images  

Convolutional layer  


CNN Training: Optimization  

Receptive Fields and Effective Receptive Fields  

ImageNet dataset  

Data Augmentation  

Convolution Layers in Keras  



Residual Network  

Inception Network  

What is Transfer learning  

Recurrent Neural Network

Why RNNs?  

Recurrent Neural Network  

Training RNNs: Backprop  

Types of RNNs  

Need for LSTM/GRU  



Deep RNN  

Bidirectional RNN