Academy of Data Science

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Certification Program in Artificial Intelligence (AI)

Course Duration

  • 120 Hours
  • 5 sessions/week

Tools Covered

  • Python
  • Machine Learning
  • Deep Learning
  • Artificial Intelligence (AI)

Learning Mode

  • Online Live Classes
  • One on one Interactive session

We provide

  • Special sessions for doubt solving
  • Sessions recordings (on request)
  • Sessions documents (on request) 
  • Assignments Mock interview
  • Live Projects

Python Full Course Content

  1. What is Python?
  2. Why 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?

  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

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

  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

  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

  1. Strings
  2. Lists
  3. Tuples

  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

  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

  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

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

  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)

  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
    Arguments
  7. Keyword Argument and Non-keyword
    Arguments
  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

  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

  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
    lines…!!
  7. Renaming and Deleting Files
  8. Directories in Python
  9. Working with CSV files and CSV Module
  10. Handling IO Exceptions

  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

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

  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
    Constructors
  7. Basic concept of Object and Classes

Machine Learning Full Course Syllabus

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

  1. Supervised Learning
  2. Unsupervised Learning

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

  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

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

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

  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)

  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

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

  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. 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

  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
    Local
  5.  Working with the NLTK Corpora

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

  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

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

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

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

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

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

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