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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

  SQL

  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

  Regression

  • 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

  Clustering

  • 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

  Optimizers

  • 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