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Artificial intelligence is the recreation of human insight through machines. Artificial Intelligence is taking our society by storm. Artificial Intelligence is nowadays applied in self-driving cars, personal assistants, surveillance systems, robotic arms in manufacturing, financial services, cyber security, searching web, video games, machine vision, product recommendations and more.

Course Objective: Toward the finish of this course, you will have the capacity to

This course will help you to understand all nut bolt of AI. This course covers foundational concepts and hands-on learning of leading machine learning tools, such as Python, Linear Regression, Support Vector Machine, Artificial Neural Network and more. This course makes a learner easily land up to a job role of either Machine Learning Engineer, NLP Expert in IT Industry, Predictive Analytics.

Duration :- 40 Hours Top Reasons which makes us best among all others:

We provide video recording tutorials of the training sessions, so in case if candidate missed any class he/she can utilize those video tutorials. All our training programs are based on live industry projects.All our training programs are based on current industry standards. Training based on 70% practical with different use cases You will able to understand algorithm’s mechanism and also their mathematical model. Our training curriculum is approved by our placement partners. Live Project based training with trainers having 5 to 15 years of Industry Experience. Our Labs are very well-equipped with latest version of hardware and software. You will get study material in form of E-Book’s, Online Videos, Certification Handbooks, Certification Dumps and Interview Questions along with Project Source material.

Table of Content



Basics of AI & Introduction

Artificial Intelligence & Machine Learning Introduction
Supervised & Unsupervised Learning
Regression & Classification Problems
What makes a Machine Learning Expert?


Math for Machine Learning – Statistic & Linear Algebra

Matrices and Vectors
Basic Linear Algebra
Differentiation and Integration
Differential Equations
Inverse, Transpose, Eigen Vectors and Eigen Values


Introduction to Python Programming

What is Python?
Installing Anaconda
Understanding the Spyder Integrated Development Environment (IDE)
Python basics and string manipulationlists, tuples, dictionaries, variables
Control Structure – If loop, For loop and while LoopSingle line loops
Writing user defined functions
Object oriented programming
Working with Class & Inheritance

Data Structure & Data Manipulation in Python

Intro to Numpy Arrays
Creating ndarrays
Indexing, Data Processing using Arrays
Mathematical computing basics
Basic statistics
File Input and Output
Getting Started with Pandas
Data Acquisition (Import & Export)Selection and Filtering
Combining and Merging Data Frames
Removing Duplicates & String Manipulation


Visualization in python

Introduction to Visualization
Visualization Importance
Working with Python visualization libraries
Matplotlib
Creating Line Plots, Bar Charts, Pie Charts, Histograms, Scatter Plots
Data visualization Using Seaborn


Linear Regression

Regression Problem Analysis
Mathematical modelling of Regression Model
Gradient Descent AlgorithmUse cases
Regression Table
Model Specification L1 & L2 Regularization
Projects & Case Studies


Logistic Regression

Assumptions
Reason for the Logit Transform
Hypothesis
Variable and Model Significance
Maximum Likelihood Concept
Log Odds and Interpretation
Null Vs Residual Deviance
Chi Square TestROC Curve
Model Specification
Projects & Case Studies


Decision Trees with Case Study

Forming a Decision Tree
Components of Decision Tree
Mathematics of Decision Tree
Decision Tree Evaluation
Entropy, Information Gain
Ggplot Decision Boundary
Accuracy, Precision, Recall, F1 Score of classifer
Confusion Matrix
Projects & Case Studies

Random Forests

Random Forest Mathematics
Design algorithm
Implement using python library
Projects & Case Studies


Artificial Neural Networks

Neurons, ANN & Working
Single Layer Perceptron Model
Multilayer Neural Network
Feed Forward Neural Network
Cost Function Formation
Applying Gradient Descent Algorithm
Backpropagation Algorithm & Mathematical Modelling
Programming Flow for backpropagation algorithm
Use Cases of ANN
Digit Recognition using MLNN
XOR Logic using MLNN & Backpropagation
Diabetes Data Predictive Analysis using ANN
Projects & Case Studies


Naïve Bayes Algorithm

Bayesian Theorem Probabilities - The Prior and Posterior Probabilities
Conditional and Joint Probabilities Notion
Traditional Approach - Extract Important Features
Naive Approach - Independence of Features Assumption
Data Processing - Discretization of Features
Advantages & Disadvantages of Naïve Bayes Models
Projects & Case Studies


Support Vector Machine

Concept and Working Principle
Mathematical Modelling
Optimization Function Formation
The Kernel Method and Nonlinear Hyperplanes
Projects & Case Studies


Clustering

Hierarchical Clustering
K Means Clustering
Use Cases for K Means Clustering
Programming for K Means using Python
Cluster Size Optimization vs Definition Optimization
Projects & Case Studies


Principle Component Analysis

Dimensionality Reduction, Data Compression
Curse of dimensionality
Multicollinearity & Factor Analysis
Concept and Mathematical modelling
Projects & Case Studies


Gradient Boosting Methods

XGBoost LightGBM CatBoost

Introduction to TensorFlow & keras

The Programming Model
Data Model, Tensor Board
Introducing Feed Forward Neural Nets
Softmax Classifier & ReLU Classifier
Dropout Optimization
Deep Learning Applications
Working with Keras
Building Neural Network with keras
Examples and use cases


Convolutional Neural Networks

CNN Architecture
MaxPooling, dropout
Variants of the Basic Convolution Function
Efficient Convolution Algorithms
CIFAR dataset Analysis
MNIST Data Set Analysis
Projects & Case Studies


Recurrent and Recursive Nets

Basic concepts of RNN
Unfolding Recurrent Neural Networks
The Vanishing Gradient Problem
LSTM Networks
Recursive Neural Networks
Deep Belief Networks
Concept and methods
Time Series Analysis
Projects & Case Studies


Autoencoders, RBM

Introducing Autoencoders
Representational Power, Layer Size and Depth
Stochastic Encoders and Decoders
Improving Autoencoders
Restricted Boltzmann Machines
Projects & Case Studies


Natural Language Processing

Natural Language Processing & Generation
Semantic Analysis
Syntactic Analysis
Language Translation
Using NLTK
Using Textblob
Sentiment Analysis
Build simple chat bot
Projects & Case Studies


Reinforcement Learning (RL)

Introduction to RL
What is Reinforcement Learning (RL)
Basic Concepts of RL
Applications of RL
RL Approaches
Key RL Algorithms


Q Learning

What is Q-Learning?
Q-Value, Discount Factor and Learning Rate
Q Table UpdatePolicy
Q-Learning Algorithm
Q-Learning Demo


Sarsa

What is Sarsa?
Q Value Update
Sarsa Algorithm
Sarsa Demo


Deep Q-Network

What is Deep Q Network (DQN)?
DQN Loss Function
Experience Replay
DQN Algorithm
DQN Demo


Policy Gradient

What is Policy Gradient (PG)?
PG Algorithm
PG Demo

During The Course

  • Lecture 1 : AWS Fundamentals pdf file
  • Lecture 1.1 : Identify and recognize cloud architecture considerations, such as fundamental components and effectiveness pdf file
  • Lecture 1.2 : How to design cloud servicespdf file
  • Lecture 1.3 : Database concepts .pdf file
  • Lecture 1.4 : Planning and design. pdf file
  • Lecture 1.5 : Familiarity with architectural trade-off decisions (high availability vs. cost, Amazon Relational Da.pdf file
  • Lecture 1.6 : Amazon S3, Amazon Simple Workflow Service (SWF), and Messaging. pdf file
  • Lecture 1.7 : DynamoDB, AWS Elastic Beanstalk, AWS CloudFormation. pdf file
  • Lecture 1.8 : Elasticity and scalability. pdf file
  • Lecture 2 : Designing and Developing. pdf file
  • Lecture 2.1 : Identify the appropriate techniques to code a cloud solution. pdf file
  • Lecture 2.2 : Configure an Amazon Machine Image (AMI). pdf file
  • Lecture 2.3 : Programming with AWS APIs pdf file
  • Lecture 3 : Deployment and Securitypdf file
  • Lecture 3.1 : Recognize and implement secure procedures for optimum cloud deployment and maintenance.. pdf file
  • Lecture 3.2 : Cloud Security Best Practices.pdf file
  • Lecture 3.3 : Demonstrate ability to implement the right architecture for development, testing, and staging environment. pdf file
  • Lecture 3.4 : Shared Security Responsibility Model.pdf file
  • Lecture 3.5 : AWS Platform Compliance. pdf file
  • Lecture 3.6 : AWS security attributes pdf file
  • Lecture 3.7 : Security Services. pdf file
  • Lecture 3.8 : AWS Identity and Access Management (IAM). pdf file
  • Lecture 3.9 : Amazon Virtual Private Cloud (VPC). pdf file
  • Lecture 3.10 : CIA and AAA models, ingress vs. egress filtering, and which AWS services and features fit. pdf file
  • Lecture 4 : Debuggingpdf file
  • Lecture 4.1 : General troubleshooting information and questions. pdf file

General IT Knowledge

  • You have to experience in systems administrator in a system operation role of 1-2 years.
  • You need the experience of understanding virtualization technology.
  • Experience in auditing systems and monitoring.
  • Some knowledge of networking concepts.
  • Templates and other configurable items to enable automation
  • Deployment tools and techniques in a distributed environment
  • Basic monitoring techniques in a dynamic environment.

Candidate Overview

  • One or more years of hands-on experience operating AWS-based applications.
  • Experience provisioning, operating, and maintaining systems running on AWS.

Exam Overview

  • Multiple choice and multiple answer questions.
  • 80 minutes to complete the exam.
  • Available in English, Japanese, Simplified Chinese, and Brazilian Portuguese.
  • Practice Exam Registration fee is USD 20.
  • Exam Registration fee is USD 150.

FAQ's

The AWS Certified DevOps Engineer Professional certification helps IT professionals validate expertise in provisioning, operating, and managing distributed application systems on the AWS platform. Intended as the next step for AWS Certified Developers and SysOps Administrators, this beta exam is open to anyone who has achieved AWS Certified Developer Associate or AWS Certified SysOps Administrator Associate.
AWS Certifications enable you to gain recognition for your knowledge and skills in working with AWS services. Additionally, preparing for certification requirements provides an opportunity to expand your expertise working with AWS services.
You must earn a passing score via a proctored exam to earn an AWS Certification. Upon receiving a passing score, you will receive your certification credentials.
To register for an exam, log into aws.training and click Certification in the top navigation. Next, click AWS Certification Account followed by Schedule New Exam.
AWS Certification passing scores are set by using statistical analysis and are subject to change. AWS does not publish exam passing scores because exam questions and passing scores are subject to change without notice.
Within 5 business days of passing your exam, your AWS Certification Account will be updated with an e-certificate stating that you have successfully passed your certification exam. You will also get access to logos, digital badging, transcript sharing, and additional benefits. Benefits also include access to our AWS Certified store where you can purchase branded merchandise.
You will be required to update your certification (or “recertify”) every two (2) years. View the AWS Certification Recertification page for more details.
Yes, we do have an option of group discount. To know more about group discount, contact info@learnnlead.com
Certification is a credential that you can earn upon successfully passing an exam.
A credential is a logo badge and title that you may use on your business cards and other professional collateral to designate yourself as AWS Certified.
We offer online video classes with the facility of two-way communication between trainer and students which allows students to communicate through chat or microphone or screen share facility.
Use of reference materials and/or electronic devices is not allowed at any time during the exam.



































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Alfina Rojisa



I have been trying to be regular with my studies but unfortunately, with the hectic travelling involved in my job, studies have always been neglected. And thus comes learn N lead to the rescue for all the working marketing specialist. The learning pedagogy varies from downloadable reading mode to online video mode.
To become an AWS developer associate, you need to register for the certification exam at any Kryterion testing center from over 750 locations around the globe.
The registration fee for the AWS Developer Associate exam is $150.
To apply for AWS Developer Associate certification, you must have fulfilled the following prerequisites:
Completed AWS Technical Essentials course or significant hands-on professional experience with various AWS services
Understanding of stateless and loosely coupled distributed applications
Familiarity developing API interfaces
Basic understanding of relational and non-relational databases
Familiarity with messaging and queuing services
Online Classroom:

Complete batch.
Complete one project and one simulation test with a minimum score of 60 percent.