Class Details

Price: $2,900

2-Day Course Includes:

  • Class exercises in addition to training instruction
  • Courseware books, notepads, pens, highlighters and other materials
  • Full breakfast with variety of bagels, fruits, yogurt, doughnuts and juice
  • Course retake option
  • Tea, coffee, and soda available throughout the day
  • Freshly baked cookies every afternoon - *only at participating locations


Course Outline

Part I

Section Objectives:

  1. Recognize the advantages and disadvantages of machine learning. 
  2. Define and explain the subjects involved in machine learning. 
  3. Define supervised and unsupervised learning.
  4. Evaluate machine learning models. 
  5. Define neural networks.
  6. Recognize different types of neural networks.
  7. Compare and contrast supervised modes and unsupervised modes. 
  8. Obtain impressive results using deep learning.
  9. Understand how to train deep learning networks using the transfer learning approach. 
  10. Recognize applications of deep learning.

Module 1: The Foundations of Machine Learning

  • Advantages of Machine Learning
  • Disadvantages of Machine Learning
  • Subjects Involved in Machine Learning
    • Statistics
    • Brain Modeling
    • Adaptive Control Theory
    • Psychological Modeling
    • Artificial Intelligence 
    • Evolutionary Models 
  • Programming Languages
    • R
    • Python

Module 2: Machine Learning Models

  • Supervised Machine Learning
  • Unsupervised or Reinforcement Learning 
  • How to Evaluate Machine Learning Models

Module 3: An Introduction to Nueral Networks

  • Historical Background
  • Why Use Neural Networks?
  • Neural Networks vs. Conventional Computers
  • Types of Neural Networks
    • Fully Connected Neural Network
    • Feed-Forward Networks
    • Convolutional Neural Networks
    • Feedback Networks
    • Perceptrons
    • Recurrent Neural Networks
    • Generative Adversarial Network

Module 4: An Introduction to Deep Learning

  • Supervised Modes
  • Unsupervised Modes
  • How to Obtain Impressive Results Using Deep Learning
  • Examples of Deep Learning
  • Automated Driving
  • Defense and Aerospace
  • Medical Research
  • Industrial Automation
  • Electronics
  • How Does Deep Learning Work?
  • Why Deep Learning is Better Than Traditional Learning Methods
  • Choosing Between Deep Learning and Machine Learning

Module 5: How to Create and Train Deep Learning Models

  • Training from Scratch
  • Transfer Learning
  • Feature Extraction

Module 6: Applications of Deep Learning

  • Recognize applications of deep learning.

Module 7: Activation Functions Used to Develop Deep Learning Models

  • Popular Activation Functions
  • Choosing the Right Activation Function

Part II


  1. Install Python. 
  2. Understand and correctly use the Python naming convention.
  3. Correctly use variables and dynamic typing in Python.
  4. Use NumPy and Panda Libraries to clean data. 
  5. Set an index for every field to access that field.
  6. Use applymap() function to clean the complete data set.
  7. Use .str() accessor to clean specific object fields. 
  8. Use NumPy Libraries to manipulate data. 
  9. Set up a Python environment for deep learning. 
  10. Load the CSV data set into Keras and make it available for use. 
  11. Create a neural network model to solve the regression problem.
  12. Use scikit-learn in Keras and evaluate the model using the method of cross-validation. 
  13. Prepare the data set well to improve the accuracy and predictions of the model.
  14. Tune or improve the network topology for different models using Keras. 
  15. Build a neural network using Keras. 
  16. Use various methods to evaluate models built using Keras.
  17. Save a Keras model to disk and load it whenever necessary.
  18. Understand and apply dropout regularization to deep learning models in Python.

Module 8: An Introduction to Python

  • Running Python
  • Installing on Windows
  • Installing on Other Systems
  • Choosing the Right Version
  • Python Keywords
  • Understanding the Naming Convention
  • Creating and Assigning Values to Variables
  • Recognizing Different Types of Variables
  • Working with Dynamic Typing
  • The None Variable
  • Computers Only Take Zeros and ones
  • Deep Learning Libraries in Python.

Module 9: How to Clean Data Using Python

  • Dropping Columns in a Data Frame
  • Changing the Index of a Data Frame 
  • Tidying up Fields in the Data
  • Cleaning the Entire Data Set Using the applymap().

Module 10: How to Manipulate Data Using Python

  • Starting with Numpy
    • Creating Arrays
    • Array Indexing
    • Array Slicing
    • Array Concatenation

Module 11: Python Environment for Deep Learning

  • Installation of Keras, TensorFlow, and Theano

Modul 12: Regression Problem Using Keras

  • Develop the Baseline Model 
  • Modeling a Standardized Data Set
  • Tune the Network Typology
  • Evaluate a Deeper Network Topology
  • Evaluate a Wider Network Topology

Module 13: How to Develop a Neural Network in Python using Keras

  • Load Data
  • Define the Model
  • Compile the Model
  • Fit the Model 
  • Evaluate the Model
  • Tie It All Together
  • Make Predictions

Module 14: How to Evaluate the Performance of a Deep Learning Model

  • Empirically Evaluate Network Configurations
  • Data Splitting
  • Manual K-Fold Cross Validation

Module 15: How to Save and Load Deep Learning Models

  • Save Your Neural Network Model to JSON
  • Save Your Neural Network Model to YAML

Module 16: Reducing Dropouts in Deep Learning Models

  • Dropout Regularization for Neural Networks 
  • Regularizing Dropouts in Keras
    • Using Dropout on Visible Layers
    • Using Dropout on Hidden Layers
  • Tips for Using Dropout


At conclusion of training students will be more proficient with:

  • Basic Python programming
  • How to use Numpy and Matplotlib in the context of deep learning.
  • How to use Jupyter Notebook with a remote server. 
  • The principles and practices of supervised learning and deep learning.
  • How to use neural networks to solve regression and classification problems.
  • How to use unsupervised learning for visualization and dimensionality reduction.
  • How to use convolutional neural networks for image classification. • How to use TensorFlow, TensorBoard, and Keras. 
  • How to optimize and tune the performance of deep neural networks. 
  • How to prepare datasets and manage the process around deep learning.
  • Deep learning concepts and techniques in current use such as gradient descent algorithms, learning curves, regularization, dropout, batch normalization, the Inception architecture, and residual networks.