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 modeGeneral 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 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
• Nested function
• Function return statement
• Function as argument
• Function as return statement
• Decorator function
• Closure
• Map(), filter(), reduce(), any() functions
• Anonymous or lambda function

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

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 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

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

Convolutional Neural Networks (CNNs)

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

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

1. What is AI?

Ans: AI, or Artificial Intelligence, refers to the simulation of human intelligence in machines that are programmed to think, learn, and problem-solve like humans. It encompasses various subfields such as machine learning, natural language processing, and computer vision.

2. Why use Python for AI?

Ans:Python is a popular programming language for AI due to its simplicity, readability, and extensive libraries. Libraries like TensorFlow, PyTorch, and scikit-learn provide powerful tools for AI development.

3. What are some popular Python libraries for AI?

Ans:Some popular Python libraries for AI include TensorFlow, PyTorch, Keras, scikit-learn, and OpenCV. These libraries offer various tools and functionalities for machine learning, deep learning, and computer vision tasks.

4. What is machine learning?

Ans:Machine learning is a subset of AI that involves training algorithms to recognize patterns in data and make predictions or decisions based on that data. It can be supervised, unsupervised, or reinforcement learning, depending on the type of data and the learning task.

5. How do I start learning AI with Python?

Ans:To start learning AI with Python, you can begin with basic programming knowledge and then delve into libraries like NumPy and Pandas for data manipulation. Afterward, explore machine learning concepts and libraries like scikit-learn. Online courses and tutorials can provide structured learning paths.

6. What is deep learning?

Ans:Deep learning is a subset of machine learning where artificial neural networks with multiple layers learn to extract features from data. It's particularly useful for tasks like image and speech recognition. Libraries like TensorFlow and PyTorch are popular choices for deep learning in Python.

7. Can I build AI applications without a strong background in mathematics?

Ans:While a basic understanding of mathematics, especially linear algebra and calculus, can be helpful in grasping some AI concepts, there are high-level libraries and tools available that abstract complex mathematical implementations. Beginners can start building AI applications with these tools and gradually deepen their understanding of the underlying mathematics.

8. What is natural language processing (NLP)?

Ans:Natural Language Processing (NLP) is a field of AI that focuses on enabling machines to understand, interpret, and generate human language. Python libraries like NLTK (Natural Language Toolkit) and spaCy provide tools for NLP tasks such as text analysis, sentiment analysis, and language translation.

9. Are there any ethical considerations in AI development?

Ans:Yes, AI development raises ethical concerns related to bias in algorithms, privacy, security, and job displacement. It's essential for AI practitioners to be aware of these issues and work towards developing responsible and unbiased AI systems.

10. Where can I find AI projects to work on using Python?

Ans: You can find AI project ideas and datasets on platforms like GitHub, Kaggle, and various online AI communities. These platforms offer a wealth of resources and projects that you can contribute to or use for learning and practice.