Artificial Intelligence And Machine Learning Specialization Program

Artificial Intelligence decision cycle.
Describe Machine Learning.
Work with real-time data.
Understand Concepts of Neural Network.
Understand and Validate Machine Learning algorithms.
Understand Natural Language Processes using Python.
Understand Computer Vision with Deep Learning process using Python.
Learn tools and techniques for predictive modelling.
Discuss Machine Learning algorithms and their implementation.
Gain expertise to handle business in the future, living the present.

Applicants should have 60% or above in Xth, XIIth and Bachelor's degree.
The program is open for candidates in their final semester and recent graduates with 0-5 years of experience.
The Artificial Intelligence & Machine Learning Specialization Program is ideal for working professionals/candidates with graduation in a quantitative discipline like engineering, mathematics, commerce, sciences, statistics, economics, etc.

Artificial Intelligence and Machine Learning Program covers all deep learning part from scratch to advance level. This program topic includes: Python Programming for Deep learning, Supervised Learning, Supervised Classification, Unsupervised Learning, Tensor Flow, Convolutional and Recurrent Neural Network, Natural Language Processing, Computer Vision Technique and much more. It includes training on the latest advancements and technical approaches in Deep Learning such as Autoencoder, Gradient Boosting, Object Detection, Object Segmentation, Object labeling, Sentiment Analysis, use cases of NLP, Text summarization and many more. The program provides access to 72 hours of interactive online learning, 2 industry-based use cases and specific assessments and other resources.

Just go ahead and subscribe to this course! If you don't acquire these skills now, you will miss an opportunity to separate yourself from the others. Don't risk your future success! Let's start learning together now!

Developers aspiring to be a NLP Expert, Artificial Intelligence Engineer, And Machine Learning Engineer.
Team Leads who are leading a team.
Freshers who what to enter the world of AI.
Professionals who want to design predictive models.
Developers aspiring to be a NLP Expert, Artificial Intelligence Engineer, And Machine Learning Engineer.
Team Leads who are leading a team.
Freshers who what to enter the world of AI.
Professionals who want to design predictive models.

What is Machine Learning?
Machine Learning Use-Cases •
Machine Learning Process Flow
Types Of Machine Learning
Difference Between Machine Learning & Traditional Programming Language

Introduction To Supervised Learning
Introduction To Regression
Linear Regression Algoritham
Multiple Regression
R Score
Regression Using SKlearn

Introduction To Classification
Logistic Regression
Confusion Matrix
F1 Score
Classification Report
K Nearest Neighbors ( KNN )
Working Of KNN
Naive Bayes Classifier
Working Of Naive Bayes Classifier
Decision Tree Algorithm
Working of Decision Tree
Random Forest Algorithm
Working Of Random Forest Algorithm

Introduction To Clustering
K- Means Clustering
Elbow Method for optimal value of k in KMeans
Hierarchical Clustering

Difference Between Deep Learning and Machine Learning
Deep learning Process
Types of Deep Learning
Why is Deep Learning Important
Limitation of Deep Learning
Deep Learning Use Cases

Quickstart for Begineers and Experts
About TensorFlow platform
Use Tensorflow for classification and regression
Neural Network
Advantages of Neural Network
Introduction To Perceptrons
Constant
Placeholder
Variable

Unit Step Function
Sigmoid Function
Tanh Function
Relu Function
Softmax Function

Gradient Descent
Forword Prapogation
Cost Function
Backword Prapogation
Weight Updation Technique
Variants Of Gradient Descent
Vanishing Gradient Descent
Leaky ReLU

Introduction to CNN
Filters of CNN
Neural Network
Advantage Of CNN
Architecture of a CNN
Convolutional and ReLU Layer CNN null
Pooling layers in a CNN
Softmax & Cross-Entropy
Understanding and Visualizing a CNN
Implimentation Of CNN

Introduction to RNN Model
Application use cases of RNN
Modelling sequences
Training RNNs with Backpropagation
Long Short-Term memory (LSTM)
Recursive Neural Tensor Network Theory

Introduction to Autoencoders
Autoencoders applications
Understanding Autoencoders
Training an AutoencoderTraining an Autoencoder
Types Of Autoencoder

Introduction to NLP
Natural Language Tool Kit(NLTK)
Text Preprocessing Steps
Stop Words
Bag-of-words
N-gram Modeling
Part of Speech Tagging (POS)
Topic Modeling
LDA (Latent Dirichlet Allocation)
F-IDF (Term Frequency - Inverse Document Frequency)
Bag-of-words and N-gram Modeling

Introduction(Text Classification)
Types of Model(Sequence Model, CNN, RNN)
Tuning the Hyperparameter
Introduction(Text Matching)
Types of Framework
Framework for Matching with Text.
Distance Metrics
Evaluation of text matching methods
Named Entity Recognition (NER)
Machine Translation

Introduction to OpenCV
GUI Features in OpenCV
Image processing
Image Conversion
Drawing Function
Blur Methods
Distance Metrics
Filtering
Thresholding •
Machine Translation

Edge Detection
Feature Detection
Video Analysis
Object Detection
Histogram
Object Labeling
Video object detection
Distance Metrics
Application

Introduction To Flask
Routing In Flask
Templates Linking in Flask
Url Building
Sign Up Form
WTF Module
Field Using Flask
Session in Flask
SQL Connection In Flask

Application deadline - 15 January

Apply Now Download Brochure

Duration 9 Months
Lectures Weekend / Weekday
Resources Online Instructor Led
Quizzes 20
Preview Lessons Yes
Language English
Skill level All Levels
Students 223
Certificate Yes

Fireblaze Exclusive

Capstone Portfolio Projects

Security

Human Face Detection

This project will get you started with object detection and you will learn how to detect any object in an image.

Entertainment

Flappy Bird

This project demonstrates how to use the Deep-Q Learning algorithm with Keras together to play FlappyBird.

Automobiles

Traffic Sign Classification

This project will get idea about traffic sign classification (image classification). This project is useful for all autonomous vehicles.

Entertainment

Sarcasm Detector

This project detect the emotion from text i.e.Sarcasm detection using TensorFlow. You will learn how text important in real life.

Entertainment

Music Genre Classification

This project we will build a model that can classify the music genre using neural networks.

Assistance

Chatbot

This project will get you started with text identification and matching using deep learning.

Public Safty

Driver Drowsiness

The driver drowsiness detection is a project which can detect whether a person is sleeping or not.

What is Python ?

Can I learn Data Science Specialization Course online ?

The rapid evolution of learning methodologies, thanks to the influx of technology, has increased the ease and efficiency of online learning, making it possible to learn at your own pace. Simplilearn's Python Data Science course provides live classes and access to study materials from anywhere and at any time. Our extensive (and growing) collection of blogs, tutorials, and YouTube videos will help you get up to speed on the main concepts. Even after your class ends, we provide a 24/7 support system to help you with any questions or concerns you may have.

What are the system requirements?

To run Python, your system must fulfill the following basic requirements: 32 or 64-bit Operating System 1GB RAM The instruction uses Anaconda and Jupyter notebooks. The e-learning videos provide detailed instructions on how to install them.

Who are our instructors and how are they selected ?

All of our highly qualified Data Science trainers are industry experts with at least 5-6 years of relevant teaching experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

What are the mods of training offered for this course?

Live Virtual Classroom or Online Classroom: In online classroom training, you have the convenience of attending the Python Data Science course remotely from your desktop via video conferencing to enhance your productivity and reduce the time spent away from work or home.

Can I Cancel my enrollment? Will I get a refund?

Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our Refund Policy.

Are there any group discounts for classroom training program?

Yes, we have group discount packages for classroom training programs. Contact Help & Support to learn more about group discounts.a

How do I enroll into this course?

You can enroll for this Python for Data Science training on our website and make an online payment using any of the following options: Visa Credit or Debit Card MasterCard American Express Diner�s Club PayPal Once payment is received you will automatically receive a payment receipt and access information via email.

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