Artificial Intelligence Training

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Artificial Intelligence Course Overview

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.  
Course Duration 30 hrs
Live Projects 2
Next Batch 15 Dec, 2019
19 Dec, 2019
22 Dec, 2019

Artificial Intelligence Course Curriculum

  • 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
     
  • 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
     
For Individuals
For Corporates

Artificial Intelligence Upcoming Batches

  • Weekend

    15 Dec - 14 Jan

    7:00 AM IST
  • Weekday

    19 Dec - 18 Jan

    7:00 AM IST
  • Weekend

    22 Dec - 21 Jan

    7:00 AM IST
  • Weekday

    24 Dec - 23 Jan

    7:00 AM IST
  • Weekend

    28 Dec - 27 Jan

    7:00 AM IST
  • Weekday

    2 Jan - 01 Feb

    12:00 AM IST
  • Schedules Doesn't Suit You ?

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

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    Artificial Intelligence 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
       
    • 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.
     

    Have More Questions

    Contact us

    Artificial Intelligence Course 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.
     

    We provide all the training sessions LIVE using either GoToMeeting or WebEx, thus promoting one-on-one trainer student Interaction.
     

    Live training uncovers distinct benefits as they are mighty to reach your desired audience converting your prospects into customers in less time. Pre-recorded videos offer plenty of advantages for entrepreneurs to educate entertain and inspire your audience as long as you want.
     

    You can contact our Tekslate support team, or you can send an email to info@tekslate.com for your queries.
     

    Yes. We provide the course materials available after course completion.
     

    There exist some discounts for weekend batches and group participants if the joiners are more than 2.
     

    If you are enrolled in classes and have paid fees but want to cancel the registration for any reason, we will attain you in 48 hours will be processed within 30 days of prior request.
     

    Have More Questions. Reach our Support Team

    Contact us

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