Online And Classroom Artificial Intelligence Course

Description: Artificial Intelligence is a multidisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. In this first module we will introduce to the field of Artificial Intelligence and how it relates to other fields of data like Artificial Intelligence, Machine Learning and Deep Learning.

Introduction to Artificial Intelligence

High level view of Artificial Intelligence,Artificial Intelligence & Machine Learning

Subtle differences between Artificial Intelligence,Artificial Intelligence & Machine Learning

Terms & Terminologies of Artificial Intelligence Machine Learning

Understanding an end to end Artificial Intelligence Pipeline, Implementation cycle

Description: Mathematics is very important in the field of Artificial Intelligence as concepts within mathematics aid in identifying patterns and assist in creating algorithms. The understanding of various notions of Statistics and Probability Theory are key for the implementation of such algorithms in Artificial Intelligence.

Linear Algebra

Matrices, Matrix Operations

Eigenvalues, Eigen Vectors

Scalar, Vector and Tensors

Prior and Posterior Probability

Conditional Probability

Calculus

Differentiation, Gradient and Cost Functions

Graph Theory

Description: This module focuses on understanding statistical concepts required for Artificial Intelligence, Machine Learning and Deep Learning. In this module, you will be introduced to the estimation of various statistical measures of a data set, simulating random distributions, performing hypothesis testing, and building statistical models.

Descriptive Statistics

Types of Data (Discrete vs Continuous)

Types of Data (Nominal, Ordinal)

Measures of Central Tendency (Mean, Median, Mode)

Measures of Dispersion (Variance, Standard Deviation)

Range, Quartiles, Inter Quartile Ranges

Measures of Shape (Skewness and Kurtosis)

Tests for Association (Correlation and Regression)

Random Variables

Probability Distributions

Standard Normal Distribution

Probability Distribution Function

Probability Mass Function

Cumulative Distribution Function

Inferential Statistics

Statistical sampling & Inference

Hypothesis Testing

Null and Alternate Hypothesis

Margin of Error

Type I and Type II errors

One Sided Hypothesis Test, Two-Sided Hypothesis Test

Tests of Inference: Chi-Square, T-test, Analysis of Variance

t-value and p-value

Confidence Intervals

Python for Artificial Intelligence

Numpy

Pandas

Matplotlib & Seaborn

Jupyter Notebook

Numpy

NumPy is a Python library that works with arrays when performing scientific computing with Python. Explore how to initialize and load data into arrays and learn about basic array manipulation operations using NumPy.

Loading data with Numpy

Comparing Numpy with Traditional Lists

Numpy Data Types

Indexing and Slicing

Copies and Views

Numerical Operations with Numpy

Matrix Operations on Numpy Arrays

Aggregations functions

Shape Manipulations

Broadcasting

Statistical operations using Numpy

Resize, Reshape, Ravel

Image Processing with Numpy

Pandas

Pandas is a Python library that provides utilities to deal with structured data stored in the form of rows and columns. Discover how to work with series and tabular data, including initialization, population, and manipulation of Pandas Series and DataFrames.

Basics of Pandas

Loading data with Pandas

Series

Operations on Series

DataFrames and Operations of DataFrames

Selection and Slicing of DataFrames

Descriptive statistics with Pandas

Map, Apply, Iterations on Pandas DataFrame

Working with text data

Multi Index in Pandas

GroupBy Functions

Merging, Joining and Concatenating DataFrames

Visualization using Pandas

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+

Anatomy of Matplotlib figure

Plotting Line plots with labels and colors

Adding markers to line plots

Histogram plots

Scatter plots

Size, Color and Shape selection in Scatter plots.

Applying Legend to Scatter plots

Displaying multiple plots using subplots

Boxplots, scatter_matrix and Pair plots

Seaborn is a data visualization library that provides a high-level interface for drawing graphs. These graphs are able to convey a lot of information, while also being visually appealing.

Basic Plotting using Seaborn

Violin Plots

Box Plots

Cat Plots

Facet Grid

Swarm Plot

Pair Plot

Bar Plot

LM Plot

Variations in LM plot using hue, markers, row and col

Exploratory Data Analysis helps in identifying the patterns in the data by using basic statistical methods as well as using visualization tools to displays graphs and charts. With EDA we can assess the distribution of the data and conclude various models to be used.

Pipeline ideas

Exploratory Data Analysis

Feature Creation

Evaluation Measures

Data Analytics Cycle ideas

Data Acquisition

Data Preparation

Model Planning & Model Building

Data Inputting

Reading and writing data to text files

Reading data from a csv

Reading data from JSON

Data preparation

Selection and Removal of Columns

Transform

Rescale

Standardize

Normalize

Binarize

One hot Encoding

Imputing

Train, Test Splitting

In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. This module on Machine Learning is a deep dive to Supervised, Unsupervised learning and Gaussian / Naive-Bayes methods. Also you will be exposed to different classification, clustering and regression methods.

Introduction to Machine Learning

Applications of Machine Learning

Supervised Machine Learning

Unsupervised Machine Learning

Reinforcement Learning

Latest advances in Machine Learning

Model Representation

Model Evaluation

Hyper Parameter tuning of Machine Learning Models.

Evaluation of ML Models.

Estimating and Prediction of Machine Learning Models

Deployment strategy of ML Models.

Supervised learning is one of the most popular techniques in machine learning. In this module, you will learn about more complicated supervised learning models and how to use them to solve problems.

Classification methods & respective evaluation

K Nearest Neighbors

Decision Trees

Naive Bayes

Stochastic Gradient Descent

SVM –

Random Forest

Gradient Boosting Machines

XGboost

Logistic regression

Ensemble methods

Combining models

Bagging

Boosting

Voting

Choosing best classification method

Model Tuning

Train Test Splitting

K-fold cross validation

Variance bias tradeoff

L1 and L2 norm

Overfit, underfit along with learning curves variance bias sensibility using graphs

Hyper Parameter Tuning using Grid Search CV

Respective Performance measures

Different Errors (MAE, MSE, RMSE)

Accuracy, Confusion Matrix, Precision, Recall

Regression is a type of predictive modelling technique which is heavily used to derive the relationship between variables (the dependent and independent variables). This technique finds its usage mostly in forecasting, time series modelling and finding the causal effect relationship between the variables. The module discusses in detail about regression and types of regression and its usage & applicability

Regression

Linear Regression

Variants of Regression

Multi Linear Regression

Logistic Regression (effectively, classification only)

Regression Model Improvement

Polynomial Regression

Random Forest Regression

Support Vector Regression

Respective Performance measures

Different Errors (MAE, MSE, RMSE)

Mean Absolute Error

Mean Square Error

Root Mean Square Error

Unsupervised learning can provide powerful insights on data without the need to annotate examples. In this module, you will learn several different techniques in unsupervised machine learning.

Clustering

K means

Hierarchical Clustering

DBSCAN

Association Rule Mining

Association Rule Mining.

Market Basket Analysis using Apriori Algorithm

Dimensionality reduction using Principal Component analysis (PCA)

Natural language is essential to human communication, which makes the ability to process it an important one for computers. In this module, you will be introduced to natural language processing and some of the basic tasks.

Text Analytics

Stemming, Lemmatization and Stop word removal.

POS tagging and Named Entity Recognition

Bigrams, Ngrams and colocations

Term Document Matrix

Count Vectorizer

Term Frequency and TF-IDF

Advanced Analytics covers various areas like Time series Analysis, ARIMA models, Recommender systems etc.

Time series

Time series Analysis.

ARIMA example

Recommender Systems

Content Based Recommendation

Collaborative Filtering

Reinforcement learning is an area of Machine Learning which takes suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.

Basic concepts of Reinforcement Learning

Action

Reward

Penalty Mechanism

Feedback loop

Deep Q Learning

Deep learning is part of a broader family of machine learning methods based on the layers used in artificial neural networks. In this module, you’ll deep dive in the concepts of Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Auto Encoders and many more.

Deep Learning

Tensorflow & keras installation

More elaborate discussion on cost function

Measuring accuracy of hypothesis function

Role of gradient function in minimizing cost function

Explicit discussion of Bayes models

Hidden Markov Models (HMM)

Optimization basics

Sales Prediction of a Gaming company using Neural Networks

Build an Image similarity engine.

Deep Learning with Convolutional Neural Nets

Architecture of CNN

Types of layers in CNN

Different Filters and Kernels

Building an Image classifier with and without CNN

Recurrent neural nets

Fundamental notions & ideas

Training a sequence classifier

Training to predict Time series

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow

Mapping the human mind with deep neural networks (DNNs), several building blocks of artificial neural networks (ANNs), the architecture of DNN and its building blocks, reinforcement learning in DNN concepts, various parameters, layers, and optimization algorithms in DNN, and activation functions.

The power of chatbots is not lost on the business world. As brands focus on promoting personalized experiences, more and more intelligent chatbots are being built to engage users and improve brand image. That said it is a rarity to find a live intelligent chatbot, also called as AI chatbot

Learn the basic methodologies and tools in robotics research and applications to move forward and experiment further in the robotics field.