Machine Learning and Deep Learning from Foundations to Applications Excel, R, Python by Narendra Ashar

Concept, Creation, Design, and Delivery by: Narendra K. Ashar.
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Integrated Multi-Tiered Course in Machine Learning, Scaling, and
Applications-ML Shells
Contents1
Introduction to ML-Shells .......................................................................................................................2
Course Outline Foundations of Machine Learning Algorithms (3 days).................................................3
Course Machine Learning Implementation with Python + Project (3 days)...........................................4
Deep Learning Training with Python (5 days).........................................................................................5
Text and Language Processing using Python (10 Days) ..........................................................................6
Machine and Deep Learning for Image and Video Processing (5 days)..................................................7
Hands on Projects Near Full Length Apps using ML and Deep Learning (4 days)...................................8
Course Machine Learning Implementation with R + Project (5 days) ....................................................9
1
Top Outline only-not a detailed description, not a document for commercial terms and conditions.

Introduction to ML-Shells
The Course is Organized as shells of growing knowledge from Core to Language and Lifecycles of ML
applications to Applications like Natural Language Processing, Text Processing Image Processing to
multiple Domains. While covering each of or even a set of domains would be a lot, the classes are
restricted to covering 3 inner shells only. However, the material in the applications shell is organized
to cater to and scale to multiple domains.
1. Foundations of Machine Learning Algorithms (Core)
a. Using White-Board, MS-Excel.
b. This will be a very hands-on course and will give a real feeling of the algorithms, the
algorithms learned here can be further used on any language R, Python, Java, Matlab,
SAS, SPSS, in all this is a foundation course on Machine Learning which is practical and
interactive (less on theory) which scales to all platforms.
c. Applications of Machine Learning, to Retail, Automotive, Customer Relation
Management, basic Finance will be covered in all the examples during explaining the
algorithms itself, Core Model applications in this area would be covered end-to-end.
2. Implementing and Scaling Machine Learning
a. Any one language R or Python.
b. Builds up from the previous course i.e. Foundations of Machine Learning.
c. Scales each of the examples covered in the Foundations course with
i. Larger Datasets
ii. Better and integrated application of the core-models covered in the larger
scenario.
d. Covers end-to-end total development of an entire Machine Learning application in one of
the Bigger (Integrated) Models.
3. Machine Learning Applications
a. Applications of the Scalable Methods to real life examples
i. NLP
ii. Text and document processing
iii. Image and Video processing
4. Advanced Machine Learning, Deep Learning.

Course Outline Foundations of Machine Learning Algorithms (3 days)
 This course cuts through the mathematical talk around machine learning algorithms and
shows you exactly how they work so that you can implement them yourself in a spreadsheet, in code
with your favourite programming language or however you like.
 Once you possess this intimate knowledge, it will always be with you. You can implement the
algorithms repeatedly.
 More importantly, you can translate the behaviour of an algorithm back to the underlying procedure
and really know what is going on and how to get the most from it.
1. Basics of Statistics and Statistical Inference
a. Descriptive Statistics, and properties of
variables, stochastic/deterministic
i. Frequency Distribution
ii. Measure of central Tendency, Dispersion
b. Hypothesis testing and Test of Difference
i. Hypothesis and its Types
ii. Concept and Decision Rules for
Hypothesis Testing
iii. One-Sample T-Test and Independent-
Samples T-Test
iv. Paired-Samples T-Test
2. Algorithms
a. Simple Linear Regression, examples
b. Logistic Function
c. Logistic Regression
d. Linear Discriminant Analysis
e. Gini
f. Classification and Regression Trees (CART)
g. Correlation Analysis
h. Co-variance
i. Non-parametric and Partial Correlation
j. Naïve Bayes
k. Gaussian PDF
l. Gaussian Naïve Bayes
m. Nearest Neighbours
n. Learning Vector Quantization
o. Support Vector Machine
p. Bagged Decision Trees
q. Adaboost
Delivery: Theory 10 %, Learning by Practice/Examples 30 %, Discussion and active participation hands-on 60 %.
At the end of this course students will be able to do all this on
 Paper &
 MS-Excel and
 Compose and make more complex models themselves.
 Be prepared for Machine Learning on any Language.

Course Machine Learning Implementation with Python + Project (3 days)
Basic Workflow used in the Training
Define Problem: Investigate and characterize the problem to better understand the goals of the project.
Analyse Data: Use descriptive statistics and visualization to had better understand the data you have available.
Prepare Data: Use data transforms to better expose the structure of the prediction problem to modelling
algorithms.
Evaluate Algorithms: Design a test harness to evaluate several standard algorithms on the data and select the top
few to investigate further.
Improve Results: Use algorithm tuning and ensemble methods to get the most out of well-performing algorithms
on your data.
Present Results: Finalize the model, make predictions and present results.
1. Python Alignment for the course
a. Python and SciPy Crash Course.
b. Load Datasets from CSV.
c. Python Ecosystem for Machine Learning.
2. Analyse Data
a. Exploratory Data Analysis with Python
b. Review, Distributions, and Skew
c. Relationship in Data
d. Descriptive Statistics
e. Summary and Classes in Data
f. Attributes in Data
g. Dimensions
3. Observing Data
a. Understand Data with Visualization
b. Univariate Plots
c. Multivariate Plots
4. Prepare Data
a. Pre-Process Data.
b. Feature Selection
c. Feature Importance.
d. Principle Component Analysis.
e. Recursive Feature Elimination.
f. Selection.
5. Evaluate Algorithms
a. Resampling Methods
b. Algorithm Evaluation Metrics
c. Spot-Check Classification Algorithms
d. Spot-Check Regression Algorithms
e. Model Selection
f. Pipelines and Automation
3 examples
g. Improve Results
h. Ensemble Methods.
i. Bagging, Random Forest
j. Boosting, AdaBoost
o Voting
k. Algorithm Parameter Tuning.
o Grid Search
o Random Search
6. Present Results
a. Model Finalization.
Delivery: Theory 10 %, Learning by Practice/Examples 30 %, Discussion and active participation
hands-on 60 %. At the end of this course students will be
 Able to do all this on Python
 Envisage and Execute end-to-end projects in Python
 Work with Data and Data sets in Python
 Pre-process and Explore data
 Select, Code, Execute and Evaluate Models in Python, decide on accuracy, tune improve and
load and re-use the model with new data.

Deep Learning Training with Python (5 days)
1. Foundations of Deep Learning Libraries
a. TensorFlow
i. Usage via Examples
ii. Deep Learning Models
b. Theano
i. Usage via Examples
ii. Extensions and Wrappers
iii. Theano Resources
c. Keras
i. Back Ends
ii. TensorFlow
iii. Theano
2. Multilayer Perceptrons (MLP)
a. Overview
b. Build with Keras
c. Evaluating the Performance with Keras
Models
i. Using an automatic verification
dataset.
ii. Using a manual verification dataset.
iii. Using k-fold cross validation.
d. Use Keras Models with Scikit-Learn
i. Learn How to
ii. Tune Keras model hyperparameters
using grid search in Scikit-learn.
iii. Easily evaluate Keras models using
cross validation in Scikit-learn.
iv. Wrap a Keras model for use with
the Scikit-learn machine learning
library.
e. End-to-End example in Multi-Class
Classification
f. End-to-End example in Regression
Problem
g. End-to-End example in Binary
Classification
3. Advanced MLPs with Keras
a. Serialization in different formats
b. Checkpointing
c. Model Behaviour During Training by
Plotting History
i. Access Model Training History
ii. Visualize Model Training History
iii. Reduce Obverting with Dropout
Regularization
iv. Scheduling the Learning Rates as
1. Time Based
2. Drop Based
4. Convolutional Neural Networks CNN
a. Overview
b. Why CNN
c. Building Blocks
i. Convolutional Layers
ii. Pooling Layers
iii. Fully Connected Layers
d. Image Processing Example
e. End-to-End Examples (based on time
available)
f. Character Recognition
g. Object Recognition in Pictures
h. Sentiment Analysis
i. Advanced Image Processing
5. Recurrent Neural Networks RNN
a. Sequences
b. Long Short Term Memory (LSTM)
Networks
c. Time Series Prediction with MLP
d. Time Series Prediction with LSTM RNNs
e. End-To-End Examples
f. Sequence Classifications (If time permits)
Pre-requisites: Team knows Python very well, with all the major libraries.

Text and Language Processing using Python (10 Days)
1. Introduction: The Basics
a. Introductions
b. What is NLP
c. NLP Applications
d. Course goals
2. Python revisited considering Text
Processing (
a. Python Libraries
b. Related fields
c. Linguistic essentials with Python
3. Basic Language Structure
a. Morphology
b. Stemming
c. Tokenization
d. Segmentation
4. Corpus
a. Corpus-based work
b. Corpora and lexical resources
c. Annotation
5. Word Sense Disambiguation (WSD)
a. Revisit Probability Theory in the
context of language
b. Graphical Models
c. Naïve Bayes
d. Naïve Bayes for WSD
6. Graphical Modeling basics
a. Introduction of Graphical Models
b. Part of speech tagging
7. Modeling using Graphical Models
a. Practical examples of Graphical
Models
b. Language models
c. Sparse data & smoothing
8. Operations on Words
a. Lexical acquisition
b. Collocations
c. Similarity
d. Selectional preferences
9. Classification
10. Information Extraction (1)
a. Detailed Example
11. Information Extraction (2)
a. Detailed Examples
12. Text Clustering
13. Text Summarization
14. Sentence Structure
15. Mini Projects
a. Complaint Log Analysis
b. Opinion Mining
c. Sentiment Analysis
d. E-mail analysis

Machine and Deep Learning for Image and Video Processing (5 days)
1. Learn to capture videos, manipulate
images, and track objects with Python
a. Python Ecosystem for
Image/Video/Machine Learning
i. Setting up OpenCV, The OpenCV
Python Interface, OpenCV Basics,
The Python Imaging Library PiL
b. Handling Files, Cameras, and GUIs
c. Filtering Images
d. Tracking Faces with Haar Cascades
e. Detecting Foreground/Background
Regions and Depth
2. Machine Learning Operations on Images
a. Clustering Images
K-Means Clustering, Hierarchical
Clustering, Spectral Clustering
b. Searching Images
Content-Based Image Retrieval,
Visual Words, Indexing Images,
Searching the Database for Images,
Ranking Results
c. Classifying Image Content
K-Nearest Neighbours, Bayes
Classifier, Support Vector Machines,
Optical Character Recognition
d. Image Segmentation
Graph Cuts, Segmentation Using
Clustering, Variationally Methods
3. Build real-world computer vision
applications
a. Detecting Edges and Applying Image
Filters
2D convolution, Blurring, Kernel,
Operations, Edge detection, Motion
blur, Sharpening, Patterns,
Embossing, Erosion, and dilation
b. Detecting and Tracking Different Body
Parts
Using Haar cascades to detect things,
Integral Images, Detecting and
tracking faces, detecting eyes, bare
eyes and sunglasses, ears, mouth,
moustache, nose, Extracting
Features from an Image, Detecting
the corners, Selecting Features to
Transform, SIFT, SURF, FAST, BRIEF,
ORB
c. Detecting Shapes and Segmenting an
Image
d. Contour analysis and shape
matching, approximating a
contour, Censoring a Shape,
Image segmentation-applied,
Watershed algorithm
e. Object Tracking
Frame differencing, Colo space based
tracking, building an interactive
object tracker, Feature based
tracking, Background subtraction
f. Object Recognition (OR)
Object detection versus object
recognition, Dense feature detector
Visual dictionary, Supervised and
unsupervised learning for OR,
Support Vector Machines for OR,
Data Separation on curves, Build a
Trainer for OR

Hands on Projects Near Full Length Apps using ML and Deep Learning (4
days)
Project 1: Learning to Recognize Traffic Signs
i. Planning the app
ii. Supervised learning
The training procedure, the testing
procedure, A classifier base class
iii. The understanding the dataset, parsing
the dataset
iv. Feature extraction
v. Support Vector Machine
Using SVMs for Multi-class
classification, Training the SVM,
Testing the SVM
Project 2: Learning to Recognize Emotions on Faces
i. Planning the app
ii. Face detection
Haar-based cascade classifiers, Pre-
trained cascade classifiers, using a pre-
trained cascade classifier, The
FaceDetector class, detecting faces in
grayscale images, Pre-processing
detected faces
iii. Facial expression recognition
Assembling a training set, Running the
screen capture, The GUI constructor
The GUI layout, Processing the current
frame, adding a training sample to the
training set, Dumping the complete
training set to a file
iv. Feature extraction using Deep Learning
Pre-processing the dataset, Principal
component analysis
v. Multi-layer perceptrons
The perceptron, Deep architectures
vi. An MLP for facial expression recognition
Training the MLP, Testing the MLP
Running the script, Putting it all together

Course Machine Learning Implementation with R + Project (5 days)
Basic Workflow used in the Training
Define Problem: Investigate and characterize the problem to better understand the goals of the project.
Analyse Data: Use descriptive statistics and visualization to had better understand the data you have available.
Prepare Data: Use data transforms to better expose the structure of the prediction problem to modelling
algorithms.
Evaluate Algorithms: Design a test harness to evaluate several standard algorithms on the data and select the
top few to investigate further.
Improve Results: Use algorithm tuning and ensemble methods to get the most out of well-performing
algorithms on your data.
Present Results: Finalize the model, make predictions and present results.
Detailed Course Contents
1. Introduction to the R platform
2. Installing and Starting R
a. R Interactive Environment
b. R Scripts
3. Quick R for developers
a. Assignment
b. Data -Structures
c. Flow Control
d. Functions
e. Packages
4. Data Wrangling & Dealing with Data sets in R.
5. Using Descriptive Statistics with R
a. Data exploration and walk-through
b. Dimensions of Data
c. Data -Types
d. Class Distribution
e. Summary Statistics
f. Standard Deviations
g. Skewness
h. Correlations
6. Data Visualization
a. Visualization Packages in R
b. Examples Univariate -Visualization
c. Examples Multivariate-Visualization
d. Data Visualization, guidelines, and tricks.
7. Pre-Processing Data for Machine Learning
a. Data Pre-Processing in R
b. Scaling and Centering Data
c. Standardize Data
d. Normalize Data
e. Box-Cox Transform
f. Yeo-Johnson Transform
g. Principal Component Analysis Transform
h. Independent Component Analysis Transform
8. Estimation of Model Accuracy
a. Data Split
b. Bootstrap
c. k-fold Cross Validation
d. Repeated k-fold Cross Validation
e. Leave One Out Cross Validation
f. Tips for Evaluating Algorithms
9. Metrics for Evaluating Machine Learning Models
a. Model Evaluation Metrics in R
b. Accuracy and Kappa
c. RMSE and R2
d. Area Under ROC Curve
e. Logarithmic Loss
 Able to do all this on R
 Envisage and Execute end-to-end projects in R
 Work with Data and Data sets in R
 Pre-process and Explore data
 Select, Code, Execute and Evaluate Models in R, decide on accuracy, tune improve and load
and re-use the model with new data

Machine Learning and Deep Learning from Foundations to Applications Excel, R, Python by Narendra Ashar

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