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AI Using Python


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  • .Introduction To Python

    • Why Python
    • Application areas of python
    • Python implementations
    • Cpython
    • Jython
    • Ironpython
    • Pypy
    • Python versions
    • Installing python
    • Python interpreter architecture
    • Python byte code compiler
    • Python virtual machine(pvm)

    Writing and Executing First Python Program

    • Using interactive mode
    • Using script mode
      • General text editor and command window
      • Idle editor and idle shell
    • Understanding print() function
    • How to compile python program explicitly

    Python Language Fundamentals

    • Character set
    • Keywords
    • Comments
    • Variables
    • Literals
    • Operators
    • Reading input from console
    • Parsing string to int, float

    Python Conditional Statements

    • If statement
    • If else statement
    • If elif statement
    • If elif else statement
    • Nested if statement

    Looping Statements

    • While loop
    • For loop
    • Nested loops
    • Pass, break and continue keywords

    Standard Data Types

    • Int, float, complex, bool, nonetype
    • Str, list, tuple, range
    • Dict, set, frozenset

    String Handling

    • What is string
    • String representations
    • Unicode string
    • String functions, methods
    • String indexing and slicing
    • String formatting

    Python List

    • Creating and accessing lists
    • Indexing and slicing lists
    • List methods
    • Nested lists
    • List comprehension

    Python Tuple

    • Creating tuple
    • Accessing tuple
    • Immutability of tuple

    Python Set

    • How to create a set
    • Iteration over sets
    • Python set methods
    • Python frozenset

    Python Dictionary

    • Creating a dictionary
    • Dictionary methods
    • Accessing values from dictionary
    • Updating dictionary
    • Iterating dictionary
    • Dictionary comprehension

    Python Functions

    • Defining a function
    • Calling a function
    • Types of functions
    • Function arguments
    • Positional arguments, keyword arguments
    • Default arguments, non-default arguments
    • Arbitrary arguments, keyword arbitrary arguments
    • Function return statement
    • Nested function
    • Function as argument
    • Function as return statement
    • Decorator function
    • Closure
    • Map(), filter(), reduce(), any() functions
    • Anonymous or lambda function

    Modules & Packages

    • Why modules
    • Script v/s module
    • Importing module
    • Standard v/s third party modules
    • Why packages
    • Understanding pip utility

    File I/O

    • Introduction to file handling
    • File modes
    • Functions and methods related to file handling
    • Understanding with block

    Object Oriented Programming

    • Procedural v/s object oriented programming
    • OOP principles
    • Defining a class & object creation
    • Object attributes
    • Inheritance
    • Encapsulation
    • Polymorphism

    Exception Handling

    • Difference between syntax errors and exceptions
    • Keywords used in exception handling
    • try, except, finally, raise, assert
    • Types of except blocks

    Regular Expressions(Regex)

    • Need of regular expressions
    • Re module
    • Functions /methods related to regex
    • Meta characters & special sequences

    GUI Programming

    • Introduction to tkinter programming
    • Tkinter widgets
    • Tk, label, Entry, Textbox, Button
    • Frame, messagebox, filedialogetc
    • Layout managers
    • Event handling
    • Displaying image

    Multi-Threading Programming

    • Multi-processing v/s Multi- threading
    • Need of threads
    • Creating child threads
    • Functions /methods related to threads
    • Thread synchronization and locking


    Introduction to Database

    • Database Concepts
    • What is Database Package?
    • Understanding Data Storage
    • Relational Database (RDBMS) Concept

    SQL (Structured Query Language)

    • SQL basics
    • DML, DDL & DQL
    • DDL: create, alter, drop
    • SQL constraints:
    • Not null, unique,
    • Primary & foreign key, composite key
    • , default
    • DML: insert, update, delete and merge
    • DQL : select
    • Select distinct
    • SQL where
    • SQL operators
    • SQL like
    • SQL order by
    • SQL aliases
    • SQL views
    • SQL joins
    • Inner join
    • Left (outer) join
    • Right (outer) join
    • Full (outer) join
    • Mysql functions
    • String functions
    • Char_length
    • Concat
    • Lower
    • Reverse
    • Upper
    • Numeric functions
    • Max, min, sum
    • Avg, count, abs
    • Date functions
    • Curdate
    • Curtime
    • Now

    Statistics, Probability &Analytics:

    Introduction to Statistics

    • Sample or population
    • Measures of central tendency
    • Arithmetic mean
    • Harmonic mean
    • Geometric mean
    • Mode
    • Quartile
    • First quartile
    • Second quartile(median)
    • Third quartile
    • Standard deviation

    Probability Distributions

    • Introduction to probability
    • Conditional probability
    • Normal distribution
    • Uniform distribution
    • Exponential distribution
    • Right & left skewed distribution
    • Random distribution
    • Central limit theorem ●

    Hypothesis Testing

    • Normality test ●
    • Mean test●
    • T-test●
    • Z-test ●
    • ANOVA test●
    • Chi square test●
    • Correlation and covariance●

    Numpy Package

    • Difference between list and numpy array ●
    • Vector and matrix operations ●
    • Array indexing and slicing ●

    Pandas Package

    Introduction to pandas

    • Labeled and structured data●
    • Series and dataframe objects●

    How to load datasets

    • From excel●
    • From csv●
    • From html table ●

    Accessing data from Data Frame

    • at &iat●
    • loc&iloc●
    • head() & tail()●

    Exploratory Data Analysis (EDA)

    • describe()●
    • groupby()●
    • crosstab()●
    • boolean slicing / query()●

    Data Manipulation & Cleaning

    • Map(), apply()
    • Combining data frames
    • Adding/removing rows & columns
    • Sorting data
    • Handling missing values
    • Handling duplicacy
    • Handling data error

    Handling Date and Time

    Data Visualization using matplotlib and seaborn packages

    • Scatter plot, lineplot, bar plot
    • Histogram, pie chart,
    • Jointplot, pairplot, heatmap
    • Outlier detection using boxplot

    Machine Learning:

    Introduction To Machine Learning

    • Traditional v/s Machine Learning Programming
    • Real life examples based on ML
    • Steps of ML Programming
    • Data Preprocessing revised
    • Terminology related to ML

    Supervised Learning

    • Classification
    • Regression

    Unsupervised Learning

    • Clustering

    KNN Classification

    • Math behind KNN
    • KNN implementation
    • Understanding hyper parameters

    Performance metrics

    • Math behind KNN
    • KNN implementation
    • Understanding hyper parameters


    • Math behind regression
    • Simple linear regression
    • Multiple linear regression
    • Polynomial regression
    • Boston price prediction
    • Cost or loss functions
    • Mean absolute error
    • Mean squared error
    • Root mean squared error
    • Least square error
    • Regularization

    Logistic Regression for classification

    • Theory of logistic regression
    • Binary and multiclass classification
    • Implementing titanic dataset
    • Implementing iris dataset
    • Sigmoid and softmax functions

    Support Vector Machines

    • Theory of SVM
    • SVM Implementation
    • kernel, gamma, alpha

    Decision Tree Classification

    • Theory of decision tree
    • Node splitting
    • Implementation with iris dataset
    • Visualizing tree

    Ensemble Learning

    • Random forest
    • Bagging and boosting
    • Voting classifier

    Model Selection Techniques

    • Cross validation
    • Grid and random search for hyper parameter tuning

    Recommendation System

    • Content based technique
    • Collaborative filtering technique
    • Evaluating similarity based on correlation
    • Classification-based recommendations


    • K-means clustering
    • Hierarchical clustering
    • Elbow technique
    • Silhouette coefficient
    • Dendogram

    Text Analysis

    • Install nltk
    • Tokenize words
    • Tokenizing sentences
    • Stop words customization
    • Stemming and lemmatization
    • Feature extraction
    • Sentiment analysis
    • CountVectorizer
    • TfidfVectorizer
    • Naive bayes algorithms

    Dimensionality Reduction

    • Principal component analysis(PCA)

    Open CV

    • Reading images
    • Understanding gray scale image
    • Resizing image
    • Understanding haar classifiers
    • Face, eyes classification
    • How to use webcam in open cv
    • Building image data set
    • Capturing video
    • Face classification in video
    • Creating model for gender prediction

    Deep Learning & Neural Networks:

    Introduction To Artificial Neural Network

    • What is artificial neural network (ANN)?
    • How neural network works?
    • Perceptron
    • Multilayer perceptron
    • Feedforward
    • Back propagation

    Introduction To Deep Learning

    • What is deep learning?
    • Deep learning packages
    • Deep learning applications
    • Building deep learning environment
    • Installing tensor flow locally
    • Understanding google colab

    Tensor Flow Basics

    • What is tensorflow?
    • Tensorflow 1.x v/s tensorflow 2.x
    • Variables, constants
    • Scalar, vector, matrix
    • Operations using tensorflow
    • Difference between tensorflow and numpy operations
    • Computational graph


    • What does optimizers do?
    • Gradient descent (full batch and min batch)
    • Stochastic gradient descent
    • Learning rate , epoch

    Activation Functions

    • What does activation functions do?
    • Sigmoid function,
    • Hyperbolic tangent function (tanh)
    • ReLU –rectified linear unit
    • Softmax function
    • Vanishing gradient problem

    Building Artificial Neural Network

    • Using scikit implementation
    • Using tensorflow
    • Understanding mnist dataset
    • Initializing weights and biases
    • Gradient tape
    • Defining loss/cost function
    • Train the neural network
    • Minimizing the loss by adjusting weights and biases

    Modern Deep Learning Optimizers and Regularization

    • SGD with momentum
    • RMSprop
    • AdaGrad
    • Adam
    • Dropout layers and regularization
    • Batch normalization

    Building Deep Neural Network Using Keras

    • What is keras?
    • Keras fundamental for deep learning
    • Keras sequential model and functional api
    • Solve a linear regression and classification problem with example
    • Saving and loading a keras model

    Convolutional Neural Networks (CNNs)

    • Introduction to CNN
    • CNN architecture
    • Convolutional operations
    • Pooling, stride and padding operations
    • Data augmentation
    • Building,training and evaluating first CNN model
    • Model performance optimization
    • Auto encoders for CNN
    • Transfer learning and object detection using pre-trained CNN models
    • LeNet
    • AlexNet
    • VGG16
    • ResNet50
    • Yolo algorithm

    Word Embedding

    • What is word embedding?
    • Word2vec embedding
    • CBOW
    • Skipgram
    • Keras embedding layers
    • Visualize word embedding
    • Google word2vec embedding
    • Glove embedding

    Recurrent Neural Networks (RNNs)

    • Introduction to RNN
    • RNN architecture
    • Implementing basic RNN in tensorflow
    • Need for LSTM and GRU
    • Deep RNN/LSTM/GRU
    • Text classification using LSTM
    • Prediction for time series problem
    • Seq-2-seq modeling
    • Encoder-decoder model

    Generative Adversarial Networks (GANs)

    • Introduction to GAN
    • Generator
    • Discriminator
    • Types of GAN
    • Implementing GAN using neural network

    Speech Recognition APIs

    • Text to speech
    • Speech to text
    • Automate task using voice
    • Voice search on web

    Projects(Any Four)

    • Stock Price Prediction Using LSTM
    • Object Detection
    • Attendance System Using Face Recognition
    • Facial Expression and Age Prediction
    • Neural Machine Translation
    • Hand Written Digits& Letters Prediction
    • Number Plate Recognition
    • Gender Classification
    • My Assistant for Desktop
    • Cat v/s Dog Image Classification


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