Artificial
Intelligence Training
Artificial Intelligence (AI) has a long history but is still properly
and actively growing and changing. In this course, you’ll learn the basics of
modern AI as well as some of the representative applications of AI such as Data
Science, Machine Learning, Deep Learning, Statistics, Artificial Neural
Networks, Restricted Boltzmann Machine (RBM) and Tensorflow with Python. Along
the way, we also hope to excite you about the numerous applications and huge
possibilities in the field of AI, which continues to expand human capability
beyond our imagination. This Artificial Intelligence course will provide a
broad understanding of the basic techniques for building intelligent computer
systems and an understanding of how AI is going to apply.
Introduction to Data Science Deep Learning & Artificial
Intelligence
Introduction to Deep Learning & AI
Deep Learning: A revolution in Artificial Intelligence
- Limitations of
Machine Learning
What is Deep Learning?
- Need for Data
Scientists
- Foundation of
Data Science
- What is Business
Intelligence
- What is Data
Analysis
- What is Data
Mining
What is Machine Learning?
Analytics vs Data Science
- Value Chain
- Types of
Analytics
- Lifecycle
Probability
- Analytics
Project Lifecycle
- Advantage of
Deep Learning over Machine learning
- Reasons for Deep
Learning
- Real-Life use
cases of Deep Learning
- Review of
Machine Learning
Data
- Basis of Data
Categorization
- Types of Data
- Data Collection
Types
- Forms of Data
& Sources
- Data Quality
& Changes
- Data Quality
Issues
- Data Quality
Story
- What is Data Architecture
- Components of
Data Architecture
- OLTP vs OLAP
- How is Data
Stored?
Big Data
- What is Big
Data?
- 5 Vs of Big Data
- Big Data
Architecture
- Big Data
Technologies
- Big Data
Challenge
- Big Data
Requirements
- Big Data
Distributed Computing & Complexity
- Hadoop
- Map Reduce
Framework
- Hadoop Ecosystem
Data Science Deep Dive
- What Data
Science is
- Why Data
Scientists are in demand
- What is a Data
Product
- The growing need
for Data Science
- Large Scale
Analysis Cost vs Storage
- Data Science
Skills
- Data Science Use
Cases
- Data Science
Project Life Cycle & Stages
- Data Acuqisition
- Where to source
data
- Techniques
- Evaluating input
data
- Data formats
- Data Quantity
- Data Quality
- Resolution
Techniques
- Data
Transformation
- File format
Conversions
- Annonymization
Python
- Python Overview
- About
Interpreted Languages
- Advantages/Disadvantages
of Python pydoc.
- Starting Python
- Interpreter PATH
- Using the
Interpreter
- Running a Python
Script
- Using Variables
- Keywords
- Built-in
Functions
- StringsDifferent
Literals
- Math Operators
and Expressions
- Writing to the
Screen
- String
Formatting
- Command Line
Parameters and Flow Control.
- Lists
- Tuples
- Indexing and
Slicing
- Iterating
through a Sequence
- Functions for
all Sequences
Operators and Keywords for Sequences
- The xrange()
function
- List
Comprehensions
- Generator
Expressions
- Dictionaries and
Sets.
Numpy & Pandas
- Learning NumPy
- Introduction to
Pandas
- Creating Data
Frames
- GroupingSorting
- Plotting Data
- Creating
Functions
- Slicing/Dicing
Operations.
Deep Dive – Functions & Classes & Oops
- Functions
- Function Parameters
- Global Variables
- Variable Scope
and Returning Values. Sorting
- Alternate Keys
- Lambda Functions
- Sorting
Collections of Collections
- Classes &
OOPs
Statistics
- What is
Statistics
- Descriptive
Statistics
- Central Tendency
Measures
- The Story of
Average
- Dispersion
Measures
- Data
Distributions
- Central Limit
Theorem
- What is Sampling
- Why Sampling
- Sampling Methods
- Inferential
Statistics
- What is
Hypothesis testing
- Confidence Level
- Degrees of
freedom
- what is pValue
- Chi-Square test
- What is ANOVA
- Correlation vs
Regression
- Uses of
Correlation & Regression
Machine Learning, Deep Learning
& AI using Python
Introduction
- ML Fundamentals
- ML Common Use
Cases
- Understanding
Supervised and Unsupervised Learning Techniques
Clustering
- Similarity
Metrics
- Distance Measure
Types: Euclidean, Cosine Measures
- Creating
predictive models
- Understanding
K-Means Clustering
- Understanding
TF-IDF, Cosine Similarity and their application to Vector Space Model
- Case study
Implementing Association rule mining
- What is
Association Rules & its use cases?
- What is
Recommendation Engine & it’s working?
- Recommendation
Use-case
- Case study
Understanding Process flow of Supervised Learning Techniques
Decision Tree Classifier
- How to build
Decision trees
- What is
Classification and its use cases?
- What is Decision
Tree?
- Algorithm for
Decision Tree Induction
- Creating a
Decision Tree
- Confusion Matrix
- Case study
Random Forest Classifier
- What is Random
Forests
- Features of
Random Forest
- Out of Box Error
Estimate and Variable Importance
- Case study
Naive Bayes Classifier.
Project Discussion
Problem Statement and Analysis
- Various
approaches to solve a Data Science Problem
- Pros and Cons of
different approaches and algorithms.
Linear Regression
- Case study
- Introduction to
Predictive Modeling
- Linear Regression
Overview
- Simple Linear
Regression
- Multiple Linear
Regression
Logistic Regression
- Case study
- Logistic
Regression Overview
- Data
Partitioning
- Univariate
Analysis
- Bivariate
Analysis
- Multicollinearity
Analysis
- Model Building
- Model Validation
- Model Performance
Assessment AUC & ROC curves
- Scorecard
Support Vector Machines
- Case Study
- Introduction to
SVMs
- SVM History
- Vectors Overview
- Decision
Surfaces
- Linear SVMs
- The Kernel Trick
- Non-Linear SVMs
- The Kernel SVM
Time Series Analysis
- Describe Time
Series data
- Format your Time
Series data
- List the
different components of Time Series data
- Discuss
different kind of Time Series scenarios
- Choose the model
according to the Time series scenario
- Implement the
model for forecasting
- Explain working
and implementation of ARIMA model
- Illustrate the
working and implementation of different ETS models
- Forecast the
data using the respective model
- What is Time
Series data?
- Time Series
variables
- Different
components of Time Series data
- Visualize the
data to identify Time Series Components
- Implement ARIMA
model for forecasting
- Exponential
smoothing models
- Identifying
different time series scenario based on which different Exponential
Smoothing model can be applied
- Implement
respective model for forecasting
- Visualizing and formatting
Time Series data
- Plotting
decomposed Time Series data plot
- Applying ARIMA
and ETS model for Time Series forecasting
- Forecasting for
given Time period
- Case Study
Machine Learning Project
Machine learning algorithms Python
- Various machine
learning algorithms in Python
- Apply machine
learning algorithms in Python
Feature Selection and Pre-processing
- How to select
the right data
- Which are the
best features to use
- Additional
feature selection techniques
- A feature
selection case study
- Preprocessing
- Preprocessing
Scaling Techniques
- How to
preprocess your data
- How to scale
your data
- Feature Scaling
Final Project
Which Algorithms perform best
- Highly efficient
machine learning algorithms
- Bagging Decision
Trees
- The power of
ensembles
- Random Forest
Ensemble technique
- Boosting –
Adaboost
- Boosting
ensemble stochastic gradient boosting
- A final ensemble
technique
Model selection cross validation score
- Introduction
Model Tuning
- Parameter Tuning
GridSearchCV
- A second method
to tune your algorithm
- How to automate
machine learning
- Which ML algo
should you choose
- How to compare
machine learning algorithms in practice
Text Mining& NLP
- Sentimental
Analysis
- Case study
PySpark and MLLib
- Introduction to
Spark Core
- Spark
Architecture
- Working with
RDDs
- Introduction to
PySpark
- Machine learning
with PySpark – Mllib
Deep Learning & AI using Python
Deep Learning & AI
- Case Study
- Deep Learning
Overview
- The Brain vs
Neuron
- Introduction to
Deep Learning
Introduction to Artificial Neural Networks
- The Detailed ANN
- The Activation
Functions
- How do ANNs work
& learn
- Gradient Descent
- Stochastic
Gradient Descent
- Backpropogation
- Understand
limitations of a Single Perceptron
- Understand
Neural Networks in Detail
- Illustrate
Multi-Layer Perceptron
- Backpropagation
– Learning Algorithm
- Understand Backpropagation
– Using Neural Network Example
- MLP
Digit-Classifier using TensorFlow
- Building a
multi-layered perceptron for classification
- Why Deep
Networks
- Why Deep
Networks give better accuracy?
- Use-Case
Implementation
- Understand How
Deep Network Works?
- How
Backpropagation Works?
- Illustrate
Forward pass, Backward pass
- Different
variants of Gradient Descent
Convolutional Neural Networks
- Convolutional
Operation
- Relu Layers
- What is Pooling
vs Flattening
- Full Connection
- Softmax vs Cross
Entropy
- ” Building a real
world convolutional neural network
- for image
classification”
What are RNNs – Introduction to RNNs
- Recurrent neural
networks rnn
- LSTMs
understanding LSTMs
- long short term
memory neural networks lstm in python
Restricted Boltzmann Machine (RBM) and Autoencoders
- Restricted
Boltzmann Machine
- Applications of
RBM
- Introduction to
Autoencoders
- Autoencoders
applications
- Understanding
Autoencoders
- Building a
Autoencoder model
Tensorflow with Python
- Introducing
Tensorflow
- Introducing
Tensorflow
- Why Tensorflow?
- What is
tensorflow?
- Tensorflow as an
Interface
- Tensorflow as an
environment
- Tensors
- Computation
Graph
- Installing
Tensorflow
- Tensorflow
training
- Prepare Data
- Tensor types
- Loss and
Optimization
- Running
tensorflow programs
Building Neural Networks using
Tensorflow
- Tensors
- 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
Deep Learning using
Tensorflow
- Deepening the
network
- Images and
Pixels
- How humans
recognise 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
- Visualising NN
using Tensorflow
- Tensorboard
Transfer Learning using
Keras and TFLearn
- Transfer
Learning Introduction
- Google Inception
Model
- Retraining
Google Inception with our own data demo
- Predicting new
images
- Transfer Learning
Summary
- Extending
Tensorflow
- Keras
- TFLearn
- Keras vs TFLearn
Comparison