Artificial Intelligence Training | AI Certification Course Online

Key Highlights

+1 930 200 4823

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30 Hrs Instructor Led Training

Self-paced Videos

20 Hrs Project & Exercises

Certification

Job Assistance

Flexible Schedule

Lifetime Free Upgrade

Mentor Support

Curriculum

A complete index of
job-ready skills curated
to meet the industrial need.
Explore.

Introduction to Deep Learning & AI

Deep Learning: A revolution in Artificial Intelligence
Limitations of Machine Learning

What is Deep Learning?

Need for Data Scientists
Foundation of Data Science
What is Business Intelligence
What is Data Analysis
What is Data Mining

What is Machine Learning?

Analytics vs Data Science
Value Chain
Types of Analytics
Lifecycle Probability
Analytics Project Lifecycle
Advantage of Deep Learning over Machine learning
Reasons for Deep Learning
Real-Life use cases of Deep Learning
Review of Machine Learning

Data

Basis of Data Categorization
Types of Data
Data Collection Types
Forms of Data & Sources
Data Quality & Changes
Data Quality Issues
Data Quality Story
What is Data Architecture
Components of Data Architecture
OLTP vs OLAP
How is Data Stored?

Big Data

What is Big Data?
5 Vs of Big Data
Big Data Architecture
Big Data Technologies
Big Data Challenge
Big Data Requirements
Big Data Distributed Computing & Complexity
Hadoop
MapReduce Framework
Hadoop Ecosystem

Data Science Deep Dive

What Data Science is
Why Data Scientists are in demand
What is a Data Product
The growing need for Data Science
Large Scale Analysis Cost vs Storage
Data Science Skills
Data Science Use Cases
Data Science Project Life Cycle & Stages
Data Acquisition
Where to source data
Techniques
Evaluating input data
Data formats
Data Quantity
Data Quality
Resolution Techniques
Data Transformation
File Format Conversions
Anonymization

Python

Python Overview
About Interpreted Languages
Advantages/Disadvantages of Python pydoc.
Starting Python
Interpreter PATH
Using the Interpreter
Running a Python Script
Using Variables
Keywords
Built-in Functions
Strings Different Literals
Math Operators and Expressions
Writing to the Screen
String Formatting
Command Line Parameters and Flow Control.
Lists
Tuples
Indexing and Slicing
Iterating through a Sequence
Functions for all Sequences

Operators and Keywords for Sequences

The xrange() function
List Comprehensions
Generator Expressions
Dictionaries and Sets.

Numpy & Pandas

Learning NumPy
Introduction to Pandas
Creating Data Frames
Grouping Sorting
Plotting Data
Creating Functions
Slicing/Dicing Operations.

Deep Dive – Functions & Classes & Oops

Functions
Function Parameters
Global Variables
Variable Scope and Returning Values. Sorting
Alternate Keys
Lambda Functions
Sorting Collections of Collections
Classes & OOPs

Statistics

What is Statistics
Descriptive Statistics
Central Tendency Measures
The Story of Average
Dispersion Measures
Data Distributions
Central Limit Theorem
What is Sampling
Why Sampling
Sampling Methods
Inferential Statistics
What is Hypothesis testing
Confidence Level
Degrees of freedom
what is p Value
Chi-Square test
What is ANOVA
Correlation vs Regression
Uses of Correlation & Regression

Machine Learning, Deep Learning & AI using Python

Introduction
ML Fundamentals
ML Common Use Cases
Understanding Supervised and Unsupervised Learning Techniques

Clustering

Similarity Metrics
Distance Measure Types: Euclidean, Cosine Measures
Creating predictive models
Understanding K-Means Clustering
Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
Case study

Implementing Association rule mining

What is Association Rules & its use cases?
What is Recommendation Engine & it’s working?
Recommendation Use-case
Case study

Understanding the Process flow of Supervised Learning Techniques Decision Tree Classifier

How to build Decision trees
What are Classification and its use cases?
What is Decision Tree?
Algorithm for Decision Tree Induction
Creating a Decision Tree
Confusion Matrix
Case study

Random Forest Classifier

What are Random Forests
Features of Random Forest
Out of Box Error Estimate and Variable Importance
Case study

Naive Bayes Classifier.

Case study

Project Discussion

Problem Statement and Analysis
Various approaches to solving a Data Science Problem
Pros and Cons of different approaches and algorithms.

Linear Regression

Case study
Introduction to Predictive Modeling
Linear Regression Overview
Simple Linear Regression
Multiple Linear Regression

Logistic Regression

Case study
Logistic Regression Overview
Data Partitioning
Univariate Analysis
Bivariate Analysis
Multicollinearity Analysis
Model Building
Model Validation
Model Performance Assessment AUC & ROC curves
Scorecard

Support Vector Machines

Case Study
Introduction to SVMs
SVM History
Vectors Overview
Decision Surfaces
Linear SVMs
The Kernel Trick
Non-Linear SVMs
The Kernel SVM

Time Series Analysis

Describe Time Series data
Format your Time Series data
List the different components of Time Series data
Discuss a different kind of Time Series scenarios
Choose the model according to the Time series scenario
Implement the model for forecasting
Explain working and implementation of ARIMA model
Illustrate the working and implementation of different ETS models
Forecast the data using the respective model
What is Time Series data?
Time Series variables
Different components of Time Series data
Visualize the data to identify Time Series Components
Implement ARIMA model for forecasting
Exponential smoothing models
Identifying different time series scenario based on which different Exponential Smoothing model can be applied
Implement respective model for forecasting
Visualizing and formatting Time Series data
Plotting decomposed Time Series data plot
Applying ARIMA and ETS model for Time Series Forecasting
Forecasting for given Time period
Case Study

Feature Selection and Pre-processing

How to select the right data
Which are the best features to use
Additional feature selection techniques
A feature selection case study
Preprocessing
Preprocessing Scaling Techniques
How to preprocess your data
How to scale your data
Feature Scaling Final Project

Which Algorithms perform best

Highly efficient machine learning algorithms
Bagging Decision Trees
The power of ensembles
Random Forest Ensemble technique
Boosting – AdaBoost
Boosting ensemble stochastic gradient boosting
A final ensemble technique

Model selection cross-validation score

Introduction Model Tuning
Parameter Tuning GridSearchCV
A second method to tune your algorithm
How to automate machine learning
Which ML algo should you choose
How to compare machine learning algorithms in practice

Text Mining NLP

Sentimental Analysis
Case study

PySpark and MLLib

Introduction to Spark Core
Spark Architecture
Working with RDDs
Introduction to PySpark
Machine learning with PySpark – MLLib

Deep Learning & AI using Python

Deep Learning & AI
Case Study
Deep Learning Overview
The Brain vs Neuron
Introduction to Deep Learning

Introduction to Artificial Neural Networks

The Detailed ANN
The Activation Functions
How do ANNs work & learn
Gradient Descent
Stochastic Gradient Descent
Backpropagation
Understand the limitations of a Single Perceptron
Understand Neural Networks in Detail
Illustrate Multi-Layer Perceptron
Backpropagation – Learning Algorithm
Understand Backpropagation – Using Neural Network Example
MLP Digit-Classifier using TensorFlow
Building a multi-layered perceptron for classification
Why Deep Networks
Why Deep Networks give better accuracy?
Use-Case Implementation
Understand How Deep Network Works?
How Backpropagation Works?
Illustrate Forward pass, Backward pass
Different variants of Gradient Descent

Convolutional Neural Networks

Convolutional Operation
Relu Layers
What is Pooling vs Flattening
Full Connection
Softmax vs Cross Entropy
” Building a real-world convolutional neural network
for image classification”

What are RNNs – Introduction to RNNs

Recurrent neural networks rnn
LSTMs understanding LSTMs
long short term memory neural networks lstm in python

Restricted Boltzmann Machine (RBM) and Autoencoders

Restricted Boltzmann Machine
Applications of RBM
Introduction to Autoencoders
Autoencoders applications
Understanding Autoencoders
Building an Autoencoder model

Tensorflow with Python

Introducing Tensorflow
Why Tensorflow?
What is tensorflow?
Tensorflow as an Interface
Tensorflow as an environment
Tensors
Computation Graph
Installing Tensorflow
Tensorflow training
Prepare Data
Tensor types
Loss and Optimization
Running tensorflow programs

Building Neural Networks using Tensorflow

Tensors
Tensorflow data types
CPU vs GPU vs TPU
Tensorflow methods
Introduction to Neural Networks
Neural Network Architecture
Linear Regression example revisited
The Neuron
Neural Network Layers
The MNIST Dataset
Coding MNIST NN

Deep Learning using Tensorflow

Deepening the network
Images and Pixels
How humans recognize images
Convolutional Neural Networks
ConvNet Architecture
Overfitting and Regularization
Max Pooling and ReLU activations
Dropout
Strides and Zero Padding
Coding Deep ConvNets demo
Debugging Neural Networks
Visualizing NN using Tensorflow
Tensorboard

We have made a tailored curriculum covering the latest industry-ready concepts to serve every individual’s learning desires.

Project Details

We bring you the best learning experience by delivering all our training sessions practical. Following are the few use cases we shall discuss during the training.

Artificial Intelligence Training Objectives

1. What are the Artificial Intelligence training course objectives?

By the end of Artificial Intelligence training, you will be able to:

Introduction to Artificial Intelligence and intelligent agents, history of Artificial Intelligence
Building intelligent agents (search, games, logic, constraint satisfaction problems)
Machine Learning algorithms
Applications of AI (Natural Language Processing, Robotics/Vision)
Solving real AI problems through programming with Python

2. Why should you learn the Artificial Intelligence course?

According to the recent survey, it is estimated that 84% of MNCs are investing in Artificial Intelligence, creating huge job opportunities worldwide.
AI is the most demand field and top MNCs are hiring certified Artificial Intelligence professionals around the globe.
The average salary of a certified Artificial Intelligence professional is around $172,000 USD per annum.

3. Who should attend Artificial Intelligence training?

Robotics engineers
Data scientists
Business analysts
Hadoop developers
Aspirants willing to build their career in the field of development

4. What are the prerequisites for learning Artificial Intelligence training?

Basic knowledge of statistics and fundamentals of Python programming is required to learn Artificial Intelligence. The following job roles will get benefited by taking up this course:

Robotics engineers
Data scientists
Business analysts
Hadoop developers

5. What projects are included in this Artificial Intelligence training course?

The tutor will take care of handling the projects. We will provide two real-time projects with a highly-skilled guide who can assist you throughout the project.

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Training Options

Different individuals. Different upgrade goals. Different modes of learning.

We got solutions for everyone looking for an AWS Architect course. Opt in for your convenient upgrade option, and we will guide you through.

Duration

One-on-one Session

Support

Resources

Time

Fee

Live Online.

30 Hours

Yes

24x7

Additional tips from the trainer

30 July 2024, 07:00 AM

03 August 2024, 08:00 AM

Self-Paced

30 Hours

Weekdays & Working Hours

Accessible through LMS

At your convenience

Artificial Intelligence Training Upcoming Batches

Weekday

30 July 2024 to 30 August 2024

07:00 AM

Weekend

03 August 2024 to 03 September 2024

08:00 AM

Weekday

06 August 2024 to 06 September 2024

07:00 AM

Weekend

10 August 2024 to 10 September 2024

07:30 AM

Schedules Doesn't Suit You ?

Our Team can set up a batch at your convinient time.

Corporates Training

Experience and witness the express transformation of your workforce from the world-class tech upgrade platform.

Customized training options

Tailored curriculum to fit your project needs.

Practical exposure is assured.

We have got everything covered for any IT upgrade for your organization. We are one click away.

Success Stories from Future Digital Leaders

During the Artificial Intelligence course, I learned a lot and had a great learning experience! and enjoyed it a lot!. Our tutor was so supportive and helpful. Thanks for the great learning experience!

John paul

Tekslate is one of the best online training institutes for beginners who really want to become an AI developer. The training is good compared to other institutes

Geetha

I have recently completed AI training, the course curriculum has some advanced topics with practical implementation. I recommend everyone to join for a great career.

Harsha

Artificial Intelligence Training FAQ'S

Have questions? We’ve got the answers. Get the details on how you can grow in this course.

1. Who are the trainers?

We have a strong team of professions who are experts in their fields. Our trainers are highly supportive and render a friendly working environment to the students positively stimulating their growth.

2. What if I miss a class?

We will share you the missed session from our recordings. We at Tekslate maintains a recorded copy of each live course you undergo.

3. How will I execute the practical?

Our Trainers will provide the student with the Server Access ensuring practical real-time experience and training with all the utilities required for the in-depth understanding of the course.