Specialization Certification in Artificial Intelligence and Deep Learning

Learn to program in Python, analyze data, and specialize in Artificial Intelligence and Machine Learning.

350+ Hours of Learning | Specialization Certificate | Virtual Lab | Project Works

Limited no. of seats available | Delivery Mode: Recorded Lectures

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For any query related to our course kindly mail at prutor.ai@gmail.com or WhatsApp on 8953463074

Skills you will gain

Artificial Intelligence
Deep Learning
Python Fundamentals
Exploratory Data Analysis
Inferential Statistics
Supervised Learning
Logistic Regression models
Recommendation Engine
Deep Learning- TensorFlow, Keras
Classification Algorithm
Linear Regression
Reinforcement Learning
Time Series Analysis
Machine Learning Algorithms

Programming Languages, Tools & Libraries Covered

About this Specialization

This Specialization builds on the success of the Specialization Course in Artificial Intelligence and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In the Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization.

Who can join?

No Prior Knowledge or Experience is required.

How the Specialization Works

Take Courses

Prutor.ai Specialization is a series of courses that help you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

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Course Fee 60,000 + 18% GST

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Program Syllabus in this Specialization

Module 01

Python Programming (Recorded lesson by Prof. Amey Karkare)

Introduction
In this lesson, you will learn about the process of programming, which involves different steps. This will give you a brief introduction to programming.
The Programming Cycle for Python
In this lesson, you will learn about the programming cycle of Python, which includes different steps. Then you will learn about the Python IDE that is to be used and the I Python shell.
Interacting with Python Programs
In this lesson, you will learn to write simple codes or programs to read some value from the user or print some computed value. In other words, you will learn to interact with some easy Python programs.
Elements of Python
In this lesson, you will learn about the elements of Python program or you can say, the parts of the Python Program. Then you will also learn about the Data types in Python
Type Conversion
In this lesson, you will learn about Type Conversion or Type Casting. You will also learn to convert types or perform typecasting Python.
Expressions
In this lesson, you will learn about the expressions, particularly different types of operators that are available in Python programming language. You will also learn about variables and Identifiers.
Assignment Statement
In this lesson, you will learn about the assignment operators, assignment statements and comments in Python.
Arithmetic Operators
In this lesson, you will learn about the Arithmetic Operators in Python, which are Binary Operators, Unary Operators and // Operator
Operator Precedence
In this lesson, you will learn about the operator precedence in Python.
Boolean Expression
In this lecture, you will learn about the Boolean Expressions, Relational Operators, Logical Operators and Complex Expressions in Python.
Conditionals
In this lesson, you will learn about conditional statement in Python, which is popularly known as if-else statement, its working and execution. You will also learn about the importance of Indentation in writing conditional codes.
Conditionals (Continued)
In this lecture, you will continue to learn about conditional statements. You will learn about nested-if statement and Elif statement in Python along with working example.
Expression Evaluation
In this lecture, you will learn to evaluate the expressions in Python, the short circuit evaluation method, three factors for expression evaluation.
Float Representation
In this lecture, you will learn about floating point representation in more detail along with the cautions about using floats and comparing these floats.
Loops
In this lesson, you will learn about the purpose and working of loops and then you will learn about the While loop including its working.
Loops (Continued)
In this lesson, you will continue to learn about some terminologies associated with Loops followed by the concept of Loop invariant and the common mistakes are committed by the programmers.
For Loop
In this lesson, you will learn about the important function called as range() in Python, then you will learn about the For loop and its working with the help of an example.
Nested Loops
In this lesson, you will learn about the Nested loops in Python. A Nested Loop is nothing but a loop within the loop. You will also understand the working of Nested loop with the help of examples.
Break and Continue
In this lesson, you will learn about the Break and Continue keywords in Python. You will also learn about the working of Break and Continue statements with the help of examples.
Function
In this lesson, you will learn about the Functions, why functions are needed and why functions are used.
Parts of A Function
In this lesson, you will learn about the different parts of a function. Then you will learn about function call in Python Programming.
Execution of A Function
In this lesson, you will learn about the execution of the function, and what a Stack is.
Keyword and Default Arguments
In this lesson, you will learn about the Keyword Arguments and the Default Arguments in Python Programming.
Scope Rules
In this lecture you will learn about the scope of names of the variables in Python Programming. You will also learn about the scope rules in case of variables, functions etc. and Global Variable.
Strings
In this lesson, you will learn about the most important topic in Python Programming, which is Strings. Then you will also learn to find the length of the string and perform Concatenation and Repeat operations in it.
Indexing and Slicing of Strings
In this lesson, you will understand the concept of Indexing and Slicing of the Strings. You will learn these with the help of examples.
More Slicing
In this lesson, you will continue to look at Slicing in more detailed manner with the help of example.
Tuples
In this lesson, you will learn about another kind of datatype in Python Programming which is known as Tuple.
Unpacking Sequences
In this lesson, you will learn about unpacking the sequences in Python Programming. The examples of Sequences are Strings and Tuples.
Lists
In this lesson, you will learn about one of the most useful datatype in Python Programming, known as List A List is nothing but an ordered sequence of values.
Mutable Sequences
In this lesson, you will learn about the mutable and immutable types of sequences in Python Programming.
List Comprehension
In this lesson, you will learn the concept of list Comprehension, which is a concise way to build the list in Python programming.
Sets
In this lesson, you will learn about another kind of sequences in Python programming, which is known as Sets. Sets are nothing but an unordered collection of elements with no duplicate elements.
Dictionaries
In this lesson, you will learn about the most used datatype in Python, which is known as Dictionary, which is an unordered set of Key:Value pairs.
Higher Order Functions
In this lesson, you will learn about higher order functions. Python allows you to treat functions as first class Objects. Then you will also learn about the Lambda Expressions.
Sieve of Eratosthenes
In this lesson, you will look at the program to generate prime numbers with the help of an algorithm given by the Greek Mathematician named Eratosthenes, whose algorithm is known as Sieve of Eratosthenes.
File I/O
In this lesson, you will learn about the file input and output operations in Python Programming. Files in computer represent persistent storage, which means that any data you write to the file remain available even after the program exists.
Exceptions
In this lesson, you will learn about the Exceptions in Python Programming. Exceptions are the way of Python to tell the user that something unexpected has happened.
Assertions
In this lesson, you will learn about Assertions in Python. An assertion is a way of validating an assumption or a condition.
Modules
In this lesson, you will learn about the modules in Python. Python allows you to keep the definitions in a file and use it in a script or an interactive instance of the interpreter. This file is known as the module.
Importing Modules
In this lesson, you will learn to import some specific functions from a module and the use of _main_ in Modules in Python Programming.
Abstract Data Types
In this lesson, you will learn about the abstract data types and ADT interface in Python Programming.
Classes
In this lesson, you will learn about the most important concept of Python programming, which is Classes. You will learn about class definition and other operations in the classes.
Special Methods
In this lesson, you will learn about some special methods in Python programming such as _init_, _str_, comparison methods and Arithmetic methods etc.
Class Example
In this lesson, you will understand the concept of classes in Python with the help of an example.
Inheritance
In this lesson, you will learn about the important Object Oriented Programming Concept, which is known as Inheritance.
Inheritance and OOP
In this lesson, you will understand the concept of Inheritance in Python Programming, in more detailed manner by taking an example.
Iterators
In this lesson, you will learn about the most important statement in Python Programming, which is considered as the backbone of for...in statements. These are known as Iterators.
Recursion
In this lesson, you will learn about Recursion, which is an important concept in Python Programming. When a function calls itself, this is termed as Recursion. You will also learn the properties of a Recursive Function.
Simple Search
In this lesson, you will learn to perform a simple search in a given sequence in Python Programming.
Estimating Search Time
In this lesson, you will learn to estimate the time taken by the program in Python Programming.
Binary Search
In this lesson, you will learn to perform faster searching in the sequence with the help of Binary Search in Python Programming.
Estimating Binary Search Time
In this lesson, you will learn to estimate the time taken by the Binary Search program in Python Programming.
Recursive Fibonacci
In this lesson, you will understand the difference between recursion and iteration followed by implementing the recursive function to generate the Fibonacci series in Python Programming.
Tower Of Hanoi
In this lesson, you will learn about the Tower of Hanoi problem and its solution by implementing the Recursive function in Python Programming.
Sorting
In this lesson, you will learn about an important concept in Python Programming, which is known as Sorting.
Selection Sort
In this lesson, you will learn about one of the sorting technique or a sorting algorithm in Python Programming popularly known as Selection Sort.
Merge List
In this lesson, you will learn about merging the two lists which are sorted in Python programming.
Merge Sort
In this lesson, you will understand the working and implementation of another popular sorting algorithm in Python programming known as Merge Sort.
Higher Order Sort
In this lesson, you will learn about Higher Order Functions and how to use these higher order functions to make a generic sorting function. Then you will also learn about Generic Selection Sort Function.
Python Programming - Final Quiz
This is the final part in this course and contains a set of questions for your self evaluation. The course will not be considered as completed successfully, if you ignore this quiz.

Module 02

Module 03

Module 04

Artificial Intelligence + Machine Learning

Welcome to Machine Learning

Introduction To Machine Learning
History and Evolution
Artificial Intelligence Evolution
Find out where Machine Learning is applied in Technology and Science

Machine Learning Categories

Supervised Learning
Unsupervised Learning

Machine Learning Python Packages

Data Analysis Packages
NumPy
SciPy
Matplotlib
Pandas
Slkearn

Supervised Learning

Regression
Classification
Generalization, Overfitting, and Underfitting

Classification

Classification

Regression

Understand how continuous supervised learning is different from discrete learning.
Code a Linear Regression in Python with scikit-learn.
Understand different error metrics such as SSE, and R Squared in the context of Linear Regressions.

Supervised Machine Learning Algorithms

k-Nearest Neighbor
Linear models
Naive Bayes Classifiers
Decision trees
Support Vector Machines

Unsupervised Learning and Preprocessing

Challenges in unsupervised learning
Preprocessing and Scaling
Applying data transformations
Scaling training and test data the same way

Dimensionality Reduction and Feature Extraction

Principal Component Analysis (PCA)

Introduction to Deep Learning

A revolution in Artificial Intelligence
Limitations of Machine Learning
What is Deep Learning?
Advantage of Deep Learning over Machine learning

Introduction To Neural Networks with TensorFlow

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

Introduction to Convolutional Neural Networks (CNN) with TensorFlow

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

Keras API

How to compose Models in Keras
Sequential Composition
Functional Composition
Predefined Neural Network Layers

Module 05

Deep Learning

1. Introduction to Machine Learning using scikit-learn days (1-8)

Overview of Machine Learning
Difference between AI, ML, and DL
Applications of ML and DL
Types of Machine Learning
Linear Regression
Logistic Regression
Overfitting and underfitting
K-Nearest Neighbor
Cross-validation and Hyper-parameter tuning
Confusion Matrix, Recall, Precision
K-Means Clustering

2. Introduction To Artificial Neural Network days (9-13)

What is Artificial Neural Network (ANN)?
How Neural Network Works?
Perceptron
Multilayer Perceptron
Feed Forward
Gradient Descent and Stochastic Gradient Descent
Back propagation

3. Introduction To Deep Learning days (14-15)

What is Deep Learning?
Deep Learning Packages
Deep Learning Applications
Building Deep Learning Environment
- Installing TensorFlow Locally
- Working with Google Colab

4. Indtroduction to TensorFlow days (16-18)

What is TensorFlow?
TensorFlow 1.x V/S TensorFlow 2.x
Placeholder, Variables, Constants
Operations using TensorFlow
Difference between TensorFlow and NumPy operations
Computational Graph
Visualizing Graph using Tensorboard

5. Activation Functions days (19-20)

What are Activation Functions?
Sigmoid Function,
Hyperbolic Tangent Function (tanh)
ReLU – Rectified Linear Unit
Softmax Function
Vanishing Gradient Problem

6. Building an Artificial Neural Network: A Case Study / An Example days (21-23)

Understanding MNIST Dataset
Initializing weights and biases
Defining loss/cost Function
Train the Neural Network
Minimizing the loss by adjusting weights and biases

7. Modern Deep Learning Optimizers and Regularization days (24-30)

SGD with Momentum
RMSprop
AdaGrad
Adam
Dropout Layers and Regularization
Batch Normalization

8. Building Deep Neural Network Using Keras days (31-33)

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

9. Convolutional Neural Networks (CNNs) days (34-40)

Introduction to CNN
CNN Architecture
Convolutional Operations
Pooling, Stride, and Padding Operations
Data Augmentation
Building, Training and Evaluating First CNN Model
Model Performance Optimization
Autoencoders for CNN
Transfer Learning

10. Recurrent Neural Networks (RNNs) days (41-46)

Introduction to RNN
RNN Architecture
Types of RNN
Implementing basic RNN in TensorFlow
Need for LSTM and GRU
Deep RNN
Text Classification Using LSTM

11. Projects days (47-50)

Sentiment analysis using RNN
MNIST Handwritten digits classification using CNN
Cat vs Dog Image classification
Objects Detection from Yolov3

Project Work

Course Fee 60,000 + 18% GST

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After finishing this course projects you can build

Hand Detection

Cat Dog Classification

MNIST Digit Prediction

ChatBot Using TensorFlow

Neural Machine Translation NMT Using TensorFlow

Autocomplete search query in TensorFlow

Text Extraction from Image

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Areas Where AI Can Be Used

AI in Virtual Assistance

Alexa, Siri, and Google Assistance are the major examples of virtual assistance while automated bots answering the questions of customers are the example of AI-based chatbots working with the best performance.

AI in Agriculture

In the agriculture sector, autonomous tractors and AI-based drones monitoring are used to enhance the productivity and crop yield of farmlands.

AI in Automotive

Self-driving cars are the other examples of AI, fully integrated into such a system to make the machine work automatically while understanding the nearby surroundings and real-world scenario.

AI in E-commerce

E-commerce backed automated warehousing and supply chain management is reducing the manpower and helping storage companies to manage the huge amount of stock or inventory with proper management and supply system.

AI in Healthcare

AI in healthcare sector is playing a vital role in empowering the machines to diagnosis, analyze and predict the various types of diseases, and monitor the patient’s health conditions.

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What You Benefit from This Program

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Job Assistance Program

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Register today to build a career in Artificial intelligence and Deep Learning

350+ hours / 6 months of learning
Online recorded lectures + virtual lab
Certificates of completion
Preparation for Interview questions and project works
Top 10 performers will get an Internship opportunity with IIT Kanpur incubated companies
Mentoring sessions with industry experts and IITK professors

Enroll and Pay Now

For any query related to our course kindly mail at prutor.ai@gmail.com or WhatsApp on 8953463074

Specialization Certification in Artificial Intelligence and Deep Learning

Introduction to SQLite database

Regular Expression

GUI Programming with Tkinter

Django: A web framework

Project - Day's

Welcome to Machine Learning

Machine Learning Categories

Machine Learning Python Packages

Supervised Learning

Classification

Regression

Supervised Machine Learning Algorithms

Unsupervised Learning and Preprocessing

Dimensionality Reduction and Feature Extraction

Introduction to Deep Learning

Introduction To Neural Networks with TensorFlow

Introduction to Convolutional Neural Networks (CNN) with TensorFlow

Keras API

1. Introduction to Machine Learning using scikit-learn days (1-8)

2. Introduction To Artificial Neural Network days (9-13)

3. Introduction To Deep Learning days (14-15)

4. Indtroduction to TensorFlow days (16-18)

5. Activation Functions days (19-20)

6. Building an Artificial Neural Network: A Case Study / An Example days (21-23)

7. Modern Deep Learning Optimizers and Regularization days (24-30)

8. Building Deep Neural Network Using Keras days (31-33)

9. Convolutional Neural Networks (CNNs) days (34-40)

10. Recurrent Neural Networks (RNNs) days (41-46)

11. Projects days (47-50)

AI in Virtual Assistance

AI in Agriculture

AI in Automotive

AI in E-commerce

AI in Healthcare