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Artificial Intelligence Training

By taking Artificial Intelligence (AI) training from Tekslate, you’ll acquire all the skills required to kick start your career in AI. You will master all the concepts in AI like tensorflow, deep learning algorithms, advanced artificial neural networks and programming languages required to design intelligent bots. This course also includes predictive analytics decision-making skills, etc. Our curriculum is designed by industry experts based on real-time scenarios. You will get hands-on experience in AI by working on various real-time applications.
 

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30 Hrs Instructor Led Training
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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
     
  • Need for Data Scientists
  • Foundation of Data Science
  • What is Business Intelligence
  • What is Data Analysis
  • What is Data Mining
     
  • 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
     
  • 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?
     
  • 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
     
  • 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 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
     
  • The xrange() function
  • List Comprehensions
  • Generator Expressions
  • Dictionaries and Sets.
     
  • Learning NumPy
  • Introduction to Pandas
  • Creating Data Frames
  • Grouping Sorting
  • Plotting Data
  • Creating Functions
  • Slicing/Dicing Operations.
     
  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values. Sorting
  • Alternate Keys
  • Lambda Functions
  • Sorting Collections of Collections
  • Classes & OOPs
     
  • 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
     
  • Introduction
  • ML Fundamentals
  • ML Common Use Cases
  • Understanding Supervised and Unsupervised Learning Techniques
     
  • 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
     
  • What is Association Rules & its use cases?
  • What is Recommendation Engine & it’s working?
  • Recommendation Use-case
  • Case study
     
  • 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
     
  • What are Random Forests
  • Features of Random Forest
  • Out of Box Error Estimate and Variable Importance
  • Case study
     
  • Case study
     
  • Problem Statement and Analysis
  • Various approaches to solving a Data Science Problem
  • Pros and Cons of different approaches and algorithms.
     
  • Case study
  • Introduction to Predictive Modeling
  • Linear Regression Overview
  • Simple Linear Regression
  • Multiple Linear 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
     
  • Case Study
  • Introduction to SVMs
  • SVM History
  • Vectors Overview
  • Decision Surfaces
  • Linear SVMs
  • The Kernel Trick
  • Non-Linear SVMs
  • The Kernel SVM
     
  • 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
     
  • 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
     
  • 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
     
  • 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
     
  • Sentimental Analysis
  • Case study
     
  • Introduction to Spark Core
  • Spark Architecture
  • Working with RDDs
  • Introduction to PySpark
  • Machine learning with PySpark – MLLib
     
  • Deep Learning & AI
  • Case Study
  • Deep Learning Overview
  • The Brain vs Neuron
  • Introduction to Deep Learning
     
  • 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 Operation
  • Relu Layers
  • What is Pooling vs Flattening
  • Full Connection
  • Softmax vs Cross Entropy
  • ” Building a real-world convolutional neural network
  • for image classification”
     
  • Recurrent neural networks rnn
  • LSTMs understanding LSTMs
  • long short term memory neural networks lstm in python
     
  • Restricted Boltzmann Machine
  • Applications of RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders
  • Building an Autoencoder model
     
  • 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
     
  • 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
     
  • 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.

Artificial Intelligence Training  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
     
  • 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.
     
  • Robotics engineers
  • Data scientists
  • Business analysts
  • Hadoop developers
  • Aspirants willing to build their career in the field of development
     

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
     

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.
 

contact us
+1 930 200 4823

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

24 Jan 2022, 07:00 AM IST

17 Jan 2022, 07:00 AM IST

 

Self-Paced

30 Hours
No
Weekdays & Working Hours
Accessible through LMS
At your convenience
 

Artificial Intelligence Training Upcoming Batches

Weekday
24 Jan, 2022 - 24 Feb, 2022
01:30 AM IST
Weekday
17 Jan, 2022 - 17 Feb, 2022
01:30 AM IST
Weekend
15 Jan, 2022 - 15 Feb, 2022
02:30 AM IST
Weekend
22 Jan, 2022 - 22 Feb, 2022
02:30 AM IST
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Schedules Doesn't Suit You ?

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

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Customized training options

Tailored curriculum to fit your project needs.

Practical exposure is assured.

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Success Stories from Future Digital Leaders

tekslate-reviews
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-reviews

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

tekslate-reviews
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.

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. 
 

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

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.
 

Join a Free Artificial Intelligence Training  Demo Session

See if this course is a fit for you by joining us for an online info session. You’ll meet our team, get an overview of the curriculum and course objectives, and learn about the benefits of being a student at Tekslate

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