• Conditional
• Conditionals (Continued)
• Expression Evaluation
• Float Representation
• Dictionaries
• User input and loops
• Break and Continue
• Function
• Parts of A Function
• Execution of A Function
• Keyword and Default Arguments
• Scope Rules
• String
• Indexing and Slicing of Strings
• More Slicing
• Higher Order Functions
• Sieve of Eratosthenes
• Abstract Data Types
• Classe
• Modules
• Importing Modules
• Classes

• Special Methods
• Class Example
• Inheritanc
• Inheritance and OOPS
• Files and Exceptions
• File I/O
• Exceptions
• Testing your code
• Assertion
• Iterators
• Recursion
• Simple Search
• Estimating Search Time
• Binary Search
• Estimating Binary Search Time
• Recursive Fibonacci
• Tower Of Hanoi
• Sorting
• Selection Sort
• Merge List
• Merge Sor
• Higher-Order Sort

Django: A web framework

• Introduction
• What is Django..?
• Installing Django
• The MVC framework
• Why use Django..?
• Creating a Django Project
• Starting a project with Django
• Creating an application
• Configuring the application
• Hello World with Django
• Creating our first URL
• Creating our first view
• Testing our application
• Working with Templates
• Template Basic
• Injecting the data from view to the template
• Built-in Tags & filters
• Integrating variables in templates
• Extending the templates
• Using static files in templates
• Models
• Define models
• Creating a simple model
• Save a model into a Database

• Inserting & Editing Data
• Getting a Model data with Queryset
• Deleting Objects
• Implementing Foreign key
• Extending a model
• Django Admin Interface
• Enabling admin interface
• Creating an admin user
• Django Forms
• Forms in Django
• Searching Query
• GET & POST methods
• Form fields in Django
• Building a form in Django
• Model-based form
• Custom validation
• The Authentication Module
• How to use the authentication module
• Adding a user
• Login and logout
• Built-in Decorators
• Restricting access to unknown users

Project

• Calculator with Tkinter
• A Web App using Django

4: Text analysis using NLTK

• What is NLP?
• NLP libraries
• NLP Applications
• Cleaning text data
• Tokenization
• Removal Stop words
• Stemming and Lemmatization
• part-of-speech(POS) tagging

B) Predictive Modeling using scikit-learn

1: scikit-learn

• Regression
  • Introduction
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Evaluate Performance of a linear regression model
  • Overfitting and underfitting
  • Regularization
• Logistic Regression
  • Logistic Regression theory
  • Implementing Logistic regression with scikit-learn
  • Logistic Regression Parameters
  • MNIST digit dataset with Logistic Regression
  • Predictive modeling on adult income dataset

• Naive Bayes Classification
  • Theory Naive Bayes Algorithm
  • Features Extraction
    • Countvectorizer
    • TF-IDF
  • Email Spam filtering
  • Sentiment analysis
• Decision Tree and Random Forest
  • The theory behind the decision tree
  • Implementing a decision tree with scikit-learn
  • Decision tree parameters
  • Combining multiple decision trees via Random forest
  • How random forest works..?
• Model Evaluation and Parameter Tuning
  • Cross-validation via K-Fold
  • Tuning hyperparameters via grid search
  • Confusion matrix
  • Recall and Precision
  • ROC and AUC
• Clustering and Dimension Reduction
  • K-means Clustering
  • Elbow method
  • Principal components analysis(PCA)
  • PCA step by step
  • Implementing PCA with scikit-learn

• k-Nearest Neighbor
• Linear models
• Naive Bayes Classifiers
• Decision trees
• Support Vector Machines
• Challenges in unsupervised learning
• Preprocessing and Scaling
• Applying data transformations
• Scaling training and test data the same way
•  Principal Component Analysis (PCA)
• A revolution in Artificial Intelligence
• Limitations of Machine Learning
• What is Deep Learning?
• Advantage of Deep Learning over Machine learning
• How Deep Learning Works?
• Activation Functions
• Training a Perceptron
• TensorFlow code-basics
• Tensorflow data types
• Tensorflow methods
• Introduction to Neural Networks
• Neural Network Architecture

• Linear Regression example revisited
• The Neuron
• Neural Network Layers
• The MNIST Dataset
• Coding MNIST NN
• Understand the limitations of a Single Perceptron
• Deepening the network
• Convolutional Neural Networks
• ConvNet Architecture
• Overfitting and Regularization
• Max Pooling and ReLU activations
• Dropout
• Strides and Zero Padding
• Coding Deep ConvNets demo
• Visualizing NN using Tensorflow
• Tensorboard
• How to compose Models in Keras
• Sequential Composition
• Functional Composition
• Predefined Neural Network Layers

• k-Nearest Neighbor
• Linear models
• Naive Bayes Classifiers
• Decision trees
• Support Vector Machines
• Challenges in unsupervised learning
• Preprocessing and Scaling
• Applying data transformations
• Scaling training and test data the same way
•  Principal Component Analysis (PCA)
• A revolution in Artificial Intelligence
• Limitations of Machine Learning
• What is Deep Learning?
• Advantage of Deep Learning over Machine learning
• How Deep Learning Works?
• Activation Functions
• Training a Perceptron
• TensorFlow code-basics
• Tensorflow data types
• Tensorflow methods
• Introduction to Neural Networks
• Neural Network Architecture

• Linear Regression example revisited
• The Neuron
• Neural Network Layers
• The MNIST Dataset
• Coding MNIST NN
• Understand the limitations of a Single Perceptron
• Deepening the network
• Convolutional Neural Networks
• ConvNet Architecture
• Overfitting and Regularization
• Max Pooling and ReLU activations
• Dropout
• Strides and Zero Padding
• Coding Deep ConvNets demo
• Visualizing NN using Tensorflow
• Tensorboard
• How to compose Models in Keras
• Sequential Composition
• Functional Composition
• Predefined Neural Network Layers