 # Artificial Intelligence

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Watch Demo Video  ##### Artificial Intelligence

Python Basics
Python List
Logic and flow control
Sequence List and Tuples
Files and Exception
Object Oriented Approach using Python
Regular Expression
Function and Packages
API development using Python

NumPy
Pandas
MatPlot
Seaborn
Exploratory Data Analysis and Visualization
SciKit Learn
TensorFlow and Keras

Python Basics
Python List
Logic and flow control
Sequence List and Tuples
Files and Exception
Object Oriented Approach using Python
Regular Expression
Function and Packages
API development using Python

NumPy
Pandas
MatPlot
Seaborn
Exploratory Data Analysis and Visualization
SciKit Learn
TensorFlow and Keras

##### MySQL

SQL Queries complete
Indexing
Joins
Stored Procedures
SQL and NoSQL Differences

Matrices and vectors
Mean, Standard Deviation, Median
Matrix vector multiplication
Matrix Multiplication and Properties
Inverse and Transpose
Coordinate systems
Mathematical Representation of a line (2D), plane(3D) and hyperplane (n*D)
Hyper planes and Hyper spaces
Geometric Representation of a circle (2D), sphere (3D) and hypersphere (n*D)
Equation of an ellipse (2D), ellipsoid (3D) and hyper ellipsoid (n*D)
Vector Spaces
Determinants
Eigen Vectors
Correlation, Coefficient intuition
Length and Dot Products
Linear Equations

Introduction to probability
Frequentist Interpretation
Bayesian Interpretation & Bayes Rules and Bayes Theorem
Analyze Dataset for Distributions
Refining the Qualitative and Quantitative Data.
Variation in Datasets (Univariate, Bivariate and Multivariate Data)
Population & Sample.
Gaussian/Normal Distribution and its PDF (Probability Density Function).
CDF (Cumulative Density Function) of Gaussian/Normal Distribution
Symmetric distribution, Skewness, and Kurtosis
Standard normal variate (z) and standardization.
Kernel density estimation.
Hypothesis Testing
Law of large Number?
Joint and Disjoint Outcomes
Probability Distribution
Sample Space and Complements
Probability Casting
Permutation and combination
Markov Decision Process
Discrete Sample Space (Finite and Infinite)
Events, Independence
Joint Probability and Conditional Probability
General Multiplication Rule
Inverting Probabilities
Z Score
Laws of Total Probability
Correlation and causation
Chebyshev’s inequality
Discrete and Continuous Uniform distributions.
Bernoulli and Binomial distribution

##### 1. Supervised Learning :-
###### Regression

Linear Regression
Logistic Regression
Polynomial Regression
Ridge Regression & Lasso Regression
Working
Math behind the Intuition
Learning the concepts of Coefficient and Residuals
Cost function
Feature scaling
Non-Linearity and non-Invertibility
Optimizing Linear Functions
Standard Error
Hypothesis Representation
Regularized Regressions
Regularization
L1 and L2 Regularization
Filter method
Wrapper method
Embedded Method
Decision Boundary
Case study using SciKit Learn

###### Classification

Intuition
Eager and Lazy Classifiers
Other names of KNN classifiers
How to Choose k?
Distance metrics used in KNN
Mathematically Demystifying KNN Algorithm
Weighted KNN
Characteristics of KNN Algorithm
Strength and weakness
Weighted KNN
Improvements of KNN performance
Fuzzy KNN
Case Study using SciKit Learn
Applying cross validation techniques and analyzing the Algorithm behaviour.
Improvisation on the Algorithm

Intuition
Visualize in Vector space
Large Margin Intuition
Significance of Binary Labels [+1,-1]
Inequalities and region
Maximum Margin: Formalization
Linear Support Vector machine
Non-Linear SVM
Hard Margin and Soft Margin
Kernel Tricks
C parameter?
Decision Functions
Multiclass Problem
Challenges on Multiclass classification
Polynomial Kernel
Gaussian RBF Kernel
SVR
Kernelized SVM
Tweak Performance
Upweighting
Drift Problem
Case Study using SciKit Learn
Strength and weakness

Intuition
Demystifying Probability
Conditional Probability
Bayes Theorem
Estimation of probability for the Dataset
Likelihoods
Gaussian, Bernoulli, Multinomial.
Discriminant Functions
Expectation Maximization Algorithm –EM
Case Study using SciKit Learn
Strength and weakness

Intuition
Training and Visualization
Predictions
Estimating Class Probabilities
Computational Complexity
CART Algorithm
HUNTS Algorithm
Gini Index, Entropy and Classification Error
Bagging and Bootstrapping
Regularization Hyperparameters
Case Study using SciKit Learn
Data Fragmentation
Tree Replication

Intuition
Voting Classifiers
Bagging and Pasting in Scikit-Learn
Out-of-Bag Evaluation
Random Patches and Random Subspaces
Random Forests
Boosting
Stacking
XGBoost
Feature Importance
Performance Evaluation
Case Studies using Scikit-Learn

##### 2. Unsupervised Learning :-

Introduction to clustering
Types of Clustering
Optimizing Objective
Data Characteristics
Prototype Based Approach
o K Means
Improvised K-Means Paper Implementations
Graph Based Approach
o Hierarchical Clustering
Density Based Approach
o DBSCAN

Intuition
Prototype Based Approach
Mathematically Demystifying KNN Algorithm
Expoloring K
Elbow Method
Characteristics of K-Means Clustering
Random Initialization
Data compression techniques
Distance Metrics for K Means
Strength and Weakness
Time and Space Complexity
Performance Evaluation
Improvised K-Means Implementations
Case Study using SciKit-Learn

Intuition
Graph based approach
Agglomerative and Divisive
Dendrograms
Proximity Methods
Strength and Weakness
Time and Space Complexity

Intuition
Density Based Approach
Mathematically Demystifying DBSCAN Algorithm
Analyzing Core points Border Point and Noise Points
Clustering Tendency
Cluster Evaluation Metrics
Cohesion and Separation
Silhouette coefficient
Time and space complexity
Strength and Weakness
Case Study using SciKit-Learn

Euclidian Distances
Squared Euclidian Distances
Manhattan Distance
Minkowski distance
Cosine measure
Jaccard distance

Association Analysis –Apriori Algorithm
Anomaly Detection

Python Texts
Working with Text PDF and Other Files
Spacy Basics, NLTK
Stemming, Lemmatization, Stop Words
Speech Tagging and Named Entity Recognition
Text Classification
Semantics and Sentimental Analysis
Topics Modeling’s
Case Study using TensorFlow

###### Case studies and Implementation of NLP:

Sentimental Analysis
Chabots

Transforming Biological neuron to Artificial Neurons
Logical Computations with Neurons
Single Layer Perceptron
Sequential Modelling
Multi-Layer Perceptron
Activation Functions
Loss functions
Batch Normalization
Learning Rates
Train Test and Validation
Overfitting and Underfitting Problems
Dealing with Data Augmentation.
One Hot Encoding
Dropout

Intuition
Convolution Layers
Max Pooling
Back Propagation
Weights and its Importance
Classification MLPs
Backpropagation
Dealing with Augmented Data
Reusing Pretrained Layers
Transfer Learning with Keras
Unsupervised Pretraining
Faster Optimizers
Momentum Optimization
Batch Size
Max-Norm Regularization
Fine Tuning
CNN Architectures
Self-Organizing Maps
Boltzmann Mechanism
Autoencoders

Intuition
RNN
Bidirectional RNN’S
LSTM
Memory Requirements

Discussion on LeNet-5
Discussion on AlexNet
Discussion on VGGNet
Discussion on ResNet
Using Pretrained Models from Keras
Pretrained Models for Transfer Learning
Classification and Localization
Object Detection
Fully Convolutional Networks (FCNs)
You Only Look Once (YOLO)
Time Series Analysis
Deploying Deep Learning Models using Django

TensorFlow and Keras Initation
Tensors and Operations
Tensors with NumPy
Placeholders
Type Conversions
Variables
Data Structures Indepth
TensorFlow Functions and Graphs
Building an Image Classifier
Using Sequential API to build Regression MLP
Using Sequential API to build Complex Models
Using the Sub classing API Saving and Restoring a Model
Implementing Callbacks
Visualization Using Tensor Board
Fine-Tuning Neural Network Hyperparameters
Hidden Layers
Learning Rate, Batch Size and Other Hyperparameters
Customizing Metrics, Layers, Training Loops , Models and Training Algorithms
Custom Loss Functions
Autograph and Tracing
TF Function Rules

Feature Engineering and Model Selection
Underfitting and overfitting
Confusion matrix
Accuracy metrics
Univariate, Bivariate, Multivariate Dataset
Evaluating machine learning model
ROC Curves
Hyper parameter tuning
Importance of Data and its quality
Attributes Types
Feature selection and Feature extraction
Stepwise Selection
Loss Function
Curse of Dimensionality
ChiSquare Test
Impact on Outliers
Cohen’s D Statistics
Error Analysis
General Distance metrics
Graph analysis on Datasets
Regularization
MSE, RMSE, MSE
Feature Slicing
Correlation and Causation
Training /Validation /Testing Data
Learning Rate
Confidence Intervals
Degree of Freedom
Coefficients and Collinearity
P value

1. PCA
2. LDA
3. QDA
Intuition on Dimensionality Reduction
Geometrical intuition.
Alternative formulation of PCA: distance minimization
Eigenvalues and eigenvectors.
PCA for dimensionality reduction and visualization.
Visualize MNIST dataset.
Limitations of PCA
Ts-SNE Estimator for Dimensionality Reduction
Impact on Algorithm

Holdout Method
K-Fold Cross Validation
Stratified K-Fold Cross Validation
Leave-One-Out Cross Validation

What is a web framework?
MVT design pattern
Importance of Django framework
Creating and running a Django project
Creating multiple applications
Defining URL patterns inside an application

Creating a template based application
Defining template tags
Application to display employee information
Inserting static files
Developing a blog application using static files

Configuring the database with sqlite3
Configuring the database with mysql
Configuring the database with mongodb
Importance make migrations and migrate
Creating a Bank database
Creating a Student database Module -4: Django Forms
Difference between HTML forms and Django forms
Form handling process
Form fields and validation
Model Forms
Implementing custom validators
Template inheritance and template filters
Creating a course registration form
Creating an employee information form

Django session framework
Important session methods
Deleting a session
Developing an online purchase application
Developing customer details management application

Creating views at class level
Creating a template file for ListView
Developing an online movie booking application
Developing an employee profile application
Developing a customer database application

Setting up of Django Rest framework
Rest Framework views
Creating custom action
PUT,POST,PATCH,DELETE methods
Working with Serializers classes
JWT Authentication
Handling relationships
Consuming third party API

Iris predictions
Wine Quality Prediction
Boston House Price predictions
Spam Collection
Car Evaluation
User Comment Analysis
Stock Prediction Analysis
Image Classification
Drug Review
Health Monitoring System

Recommendation System (Tensor Flow Keras)
Handwritten Number Recognition Recognition
Display Machine Learning Models To Android
Taxi Fare Prediction System
Face Detection using Deep Learning (Tensor Flow and Keras)
Transfer Learning on Pretrain Models
Object Detection using YOLO
Topic Classifier using Machine Learning (Tensor Flow and Keras)
Traffic Signal predictions using Machine Learning (Tensor Flow Keras)

Building an e-commerce application with rest API using Django Framework

Problems on Trains
Numbers and Ages
Percentage problems
Boats and Streams
Ratio & Proportion
Pipes and Cistern
Interest
Heights and Distances
Profit and Loss
Discount
Permutations and Combination
Mixture and Allegation
Time and distance Series
Time & Work
Volume & Surface Areas
Calendar
Clocks
Stocks & Shares
Permutations & Combinations
Probability
Heights & Distances
Odd Man Out & Series

Alphanumeric series
Analogies
Artificial Language
Blood Relations
Cause and Effect
Coding-Decoding
Critical path
Cubes and cuboids
Data Sufficiency
Decision Making
Deductive Reasoning/Statement Analysis
Dices
Directions
Embedded Images
Figure Matrix
Mirror and Water Images
Odd One Out
Picture Series and Sequences
Pattern Series and Sequences
Seating Arrangements
Statement and Assumptions
Statement and Conclusions
Syllogism