Academy of Data Science

Certification Program in Artificial Intelligence (AI)

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Python Full Course Content

Introduction to Python

  1. What is Python?
  2. Why Python?

Variables in Python

  1. What is Variable?
  2. Variables and Constants in Python
  3. Variable, names and Value
  4. Values and Types
  5. What Does “Type” Mean?

String Handling

  1. What is string?
  2. String operations and indices
  3. Basic String Operations
  4. String Functions, Methods
  5. Delete a string
  6. String Multiplication and concatenation
  7. Python Keywords, Identifiers and Literals
  8. String Formatting Operator
  9. Structuring with indentation in Python
  10. Built-in String Methods
  11. Define Data Structure?
  12. Data Structures in PYTHON

Python Operators and Operands

  1. Arithmetic, Relational Operators and Comparison Operators
  2. Python Assignment Operators
  3. Short-hand Assignment Operators
  4. Logical Operators or Bitwise Operators

Python Conditional Statements

  1. How to use “if condition” in conditional structures
  2. if statement (One-Way Decisions)
  3. if .. else statement (Two-way Decisions) 
  4. How to use “else condition”
  5. Logical Operators or Bitwise Operators
  6. if .. elif .. else statement (Multi-way)
  7. How to use “elif” condition
    Nested IF Statement

Python LOOPS

  1. How to use “While Loop” and “For Loop”
  2. How to use For Loop for set of other things besides numbers
  3. Break statements, Continue statements, Enumerate
  4. function for For Loop
  5. Practical Example
  6. How to use for loop to repeat the same statement over and again
  7. Break, continue statements

Learning Python Strings

  1. Strings
  2. Lists
  3. Tuples

Python Lists

  1. Lists are mutable
  2. Getting to Lists
  3. List indices
  4. Traversing a list
  5. List operations, slices and methods
  6. Map, filter and reduce
  7. Deleting elements
  8. Lists and strings

Python TUPLE

  1. Advantages of Tuple over List
  2. Packing and Unpacking
  3. Comparing tuples
  4. Creating nested tuple
  5. Using tuples as keys in dictionaries
  6. Deleting Tuples
  7. Slicing of Tuple

Python Sets

  1. How to create a set?
  2. Iteration Over Sets
  3. Python Set Methods
  4. Python Set Operations
  5. Union of sets
  6. Built-in Functions with Set
  7. Python Frozenset

Variables in Python

  1. What is Variable?
  2. Variables and Constants in Python
  3. Variable, names and Value
  4. Values and Types
  5. What Does “Type” Mean?

Python Dictionary

  1. How to create a dictionary?
  2. Python Dictionary Methods
  3. Copying dictionary
  4. Updating Dictionary
  5. Delete Keys from the dictionary
  6. Dictionary items() Method
  7. Sorting the Dictionary
  8. Python Dictionary in-built Function
  9. Dictionary len() Method
  10. Variable Types
  11. Python List cmp() Method
  12. Dictionary Str(dict)

Python Functions

  1. What is a function?
  2. How to define and call a function in Python
  3.  Types of Functions
  4.  How Function Return Value?
  5. Types of Arguments in Functions
  6. Default Arguments and Non-Default
  7. Keyword Argument and Non-keyword
  8.  Rules to define a function in Python
  9. Scope and Lifetime of variables
  10.  Nested Functions
  11. Call By Value, Call by Reference
  12. Passing functions to function

Python Date and Time

  1. How to Use Date & DateTime Class
  2. How to Format Time Output
  3. How to use Timedelta Objects
  4. Calendar in Python
  5. datetime classes in Python
  6. How to Format Time Output?
  7. The Time Module
  8.  Python Calendar Module
  9. Python Text Calendar, HTML Calendar Class
  10. Unix Date and Time Commands

File Handling

  1. What is a data, Information File?
  2. File Objects
  3. File Different Modes and Object Attributes
  4. How to create a Text Fil and Append Data to a File and Read a File
  5. Closing a file
  6. Read, read line ,read lines, write, write
  7. Renaming and Deleting Files
  8. Directories in Python
  9. Working with CSV files and CSV Module
  10. Handling IO Exceptions

Python Exception Handling

  1. Chain of importance Of Exception
  2. Exception Handling
  3. Try … Except
  4. Try .. Except .. else
  5. Try … finally
  6. Argument of an Exception
  7. Python Custom Exceptions
  8. Ignore Errors
  9. Assertions
  10. Using Assertions Effectively

More Advanced PYTHON

  1. Python Iterators, Generators, Closures,
    Decorators and Python @property

Python Class and Objects

  1. Introduction to OOPs Programming
  2. Object Oriented Programming System
  3. OOPS Principles
  4. Define Classes
  5. Creating Objects
  6. Class variables and Instance Variables
  7. Basic concept of Object and Classes

Machine Learning Full Course Syllabus

1. Introduction to Machine Learning

  1. Machine Learning
  2. Machine Learning Algorithms
  3.  Algorithmic models of Learning
  4. Applications of Machine Learning
  5. Large Scale Machine Learning

2. Techniques of Machine Learning

  1. Supervised Learning
  2. Unsupervised Learning

3. Regression

  1. Regression and its Types
  2. Logistic Regression
  3. Linear Regression
  4. Polynomial Regression

4. Classification

  1. Meaning and Types of Classification
  2.  Nearest Neighbor Classifiers
  3. K-nearest Neighbors
  4. Probability and Bayes Theorem
  5. Support Vector Machines
  6. Naive Bayes
  7. Decision Tree Classifier
  8. Random Forest Classifier

5. Unsupervised Learning: Clustering

  1. About Clustering
  2. Clustering Algorithms
  3. K-means Clustering
  4.  Hierarchical Clustering
  5.  Distribution Clustering

6. Model optimization and Boosting

  1. Ensemble approach
  2. K-fold cross validation
  3. Grid search cross validation
  4. Ada boost and XG Boost

Deep Learning Full Course Syllabus

1. Introduction to Deep Learning

  1. • What are the Limitations of Machine Learning?
  2.  What is Deep Learning?
  3.  Advantage of Deep Learning over Machine
  4. Reasons to go for Deep Learning
  5. Real-Life use cases of Deep Learning

2. Deep Learning Networks

  1. What is Deep Learning Networks?
  2. Why Deep Learning Networks?
  3.  How Deep Learning Works?
  4. Feature Extraction
  5. Working of Deep Network
  6. Training using Back propagation
  7. Types of Deep Networks
  8.  Feed forward neural networks (FNN)
  9. Convolutional neural networks (CNN)
  10. Recurrent Neural networks (RNN)

3. Deep Learning with Keras

  1. Define Keras
  2.  How to compose Models in Keras?
  3. Predefined Neural Network Layers
  4.  What is Batch Normalization?
  5. Saving and Loading a model with Keras
  6. Customizing the Training Process
  7. Intuitively building networks with Keras

4. Convolutional Neural Networks (CNN)

  1. Introduction to Convolutional Neural
  2. CNN Applications
  3.  Architecture of a Convolutional Neural
  4. Convolution and Pooling layers in a CNN
  5. Understanding and Visualizing CNN
  6. Transfer Learning and Fine-tuning
    Convolutional Neural Networks 

5. Recurrent Neural Network (RNN)

  1. Intro to RNN Model
  2. Application use cases of RNN
  3.  Modelling sequences
  4. Training RNNs with Back propagation
  5.  Long Short-Term Memory (LSTM)
  6. Recursive Neural Tensor Network Theory
  7. Recurrent Neural Network Model
  8. Time Series Forecasting

Artificial Intelligence (AI) Full Course Syllabus

1. Introduction to Text Mining and NLP

  1. Overview of Text Mining
  2.  Need of Text Mining
  3.  Natural Language Processing (NLP) in Text Mining
  4.  Applications of Text Mining
  5.  OS Module
  6.  Reading, Writing to text and word files
  7.   Setting the NLTK Environment
  8.  Accessing the NLTK Corpora

2. Hands-On/Demo

  1. Install NLTK Packages using NLTK Downloader
  2.  Accessing your operating system using the OS
    Module in Python
  3.  Reading & Writing .txt Files from/to your Local
  4.  Reading & Writing .docx Files from/to your
  5.  Working with the NLTK Corpora

3. Extracting, Cleaning and Pre-processing Text

  1. Tokenization
  2.  Frequency Distribution
  3.  Different Types of Tokenizers
  4.  Bigrams, Trigrams & Ngrams
  5.  Stemming
  6.  Lemmatization

4. Hands-On/Demo

  1. Tokenization: Regex, Word, Blank line,
    Sentence Tokenizers
  2.  Bigrams, Trigrams & Ngrams
  3.  Stopword Removal
  4.  POS Tagging
  5.  Named Entity Recognition (NER)
  6.  Stopwords
  7.  POS Tagging
  8.  Named Entity Recognition

5. Text Classification – I

  1. Bag of Words
  2. Count Vectorizer
  3.  Term Frequency (TF)
  4.  Inverse Document Frequency (IDF)

6. Hands-On/Demo

  1. Demonstrate Bag of Words Approach
  2. Working with CountVectorizer()
  3.  Using TF & IDF

7. Text Classification - II

  1. Converting text to features and labels
  2.  Multinomial Naive Bayes Classifier
  3.  Leveraging Confusion Matrix

8. Hands-On/Demo

  1. Converting text to features and labels
  2.  Demonstrate text classification using
    Multinomial NB Classifier
  3.  Leveraging Confusion Matrix

9. In Class Project

  1. Implement all the text processing
    techniques starting with tokenization
  2.  Express your end to end work on Text
  3.  Implement Machine Learning along
    with Text Processing

10. Hands-On

Sentiment Analysis