AI Courses

 

 

AI Training Courses

Artificial Intelligence and Deep Learning with TensorFlow and Python Training, we will learn about what is AI, explore neural networks, understand deep learning frameworks, implement various machine learning algorithms using Deep Networks. We will also explore how different layers in neural networks do data abstraction and feature extraction using Deep Learning. Deep Learning in TensorFlow training is designed to make you a Data Scientist by providing you rich hands-on training on Deep Learning in TensorFlow with Python. This course is a stepping stone in your Data Science journey using which you will get the opportunity to work on various Deep Learning projects.

Why should you take AI Courses

TensorFlow could be a game-changer in the future of AI – Google

Working with Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)

 

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Like the Course AI Courses ? Enroll Now or Get the free career path

Our Artificial Intelligence course web based utilizing TensorFlow in a joint effort with CCE, IIT Madras assists you with dominating Artificial Intelligence and Machine Learning abilities like Data Science, CNN, perceptron, TensorFlow, Neural Networks, NLP, and so on by means of active tasks. Learn AI by IIT Madras staff and sign up for the best Artificial Intelligence program online to turn into an effective Artificial Intelligence Engineer!

 

 

 

Curriculum
Course Details
Certification
Features

AI Courses Curriculum

Introduction to Deep Learning

  • Introduction Deep Learning
  • Life Cycle of Deep Learning
  • Skills required for Deep Learning
  • Careers Path in Deep Learning
  • Applications of Deep Learning
  • Advantage of Deep Learning over Machine learning
  • 3 Reasons to go for Deep Learning
  • Real-Life use cases of Deep Learning
  • Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization

  • Introduction to Data:
    • Data types
    • Data Collection Techniques
  • Descriptive Statistics:
    • Measures of Central Tendency
    • Measures of Dispersion
    • Measures of Skewness and Kurtosis
    • Visualization
  • Inferential Statistics:
    • Sampling variability and Central Limit Theorem
    • Confidence Interval for Mean
    • Hypothesis ,t- Test,F-Test,Chi-square Test
    • ANOVA
  • Random Sampling and Probability Distribution:
    • Probability and Limitations,Discrete Probability,Continuous Probability
    • Binomial, Poisson Distributions,Normal Distribution

  • Environment Setup
  • Jupyter Notebook Overview
  • Data types:Numbers,Strings,Printing,Lists,Dictionaries,Booleans,Tuples ,Sets
  • Comparison Operators
  • if,elif, else Statements
  • Loops:for Loops,while Loops
  • range()
  • list comprehension
  • functions
  • lambda expressions
  • map and filter
  • methods
  • Programming Exercises.
  • Object-Oriented Programming
  • Modules and packages
  • Errors and Exception Handling
  • Python Decorators
  • Python generators
  • Collections
  • Regular Expression

  • Installing numpy
  • Using numpy
  • NumPy arrays
  • Creating numpy arrays from python list
  • Creating arrays using built in methods(arrange(),zeros(),ones(),linspace(),eye(),rand(),etc.
  • Array attributes :shape, type
  • Array methods: Reshape(),min(),max(),argmax(),argmin(),etc.

  • Introduction to Pandas
  • Series
  • DataFrames
  • Missing Data
  • GroupBy
  • Merging, Joining and Concatenating
  • Operations
  • Data Input and Output

  • Installing Matplotlib,Basic Matplotlib commands
  • Creating Multiplot on same canvas
  • Object Oriented Method:figure(),plot(),add_axes(),subplots(),etc.
  • MatplotlibExercise

  • Categorical plot
  • Distribution plot
  • Regression plot
  • Seaborn Exercise

Pandas built in visualization:

  • Scatter plot
  • Histograms
  • Box plot

  • CAPSTONE PROJECT FOR DATA ANALYSIS

Up and Running with TensorFlow

  • Installation
  • Creating Your First Graph and Running It in a Session
  • Managing Graphs
  • Lifecycle of a Node Value
  • Linear Regression with TensorFlow
  • Implementing Gradient Descent
  • Feeding Data to the Training Algorithm
  • Saving and Restoring Models
  • Visualizing the Graph and Training Curves Using TensorBoard
  • Name Scopes, Modularity
  • Sharing Variables

  • From Biological to Artificial Neurons
  • Training an MLP with TensorFlow’s High-Level API
  • Training a DNN Using Plain TensorFlow
  • Fine-Tuning Neural Network Hyperparameters

  • Vanishing / Exploding Gradients Problems
  • Reusing Pretrained Layers
  • Faster Optimizers
  • Avoiding Overfitting Through Regularization
  • Practical Guidelines

  • The Architecture of the Visual Cortex
  • Convolutional Layer
  • Pooling Layer
  • CNN Architectures
  • Recurrent Neurons
  • Basic RNNs in TensorFlow
  • Training RNNs, Deep RNNs
  • LSTM Cell
  • GRU Cell
  • Natural Language Processing

  • Efficient Data Representations
  • Performing PCA with an Undercomplete Linear Autoencoder
  • Stacked Autoencoders
  • Unsupervised Pretraining Using Stacked Autoencoders
  • DenoisingAutoencoders
  • Sparse Autoencoders
  • Variational Autoencoders

  • Learning to Optimize RewardsPolicy Search
  • Introduction to OpenAI Gym
  • Neural Network Policies
  • Evaluating Actions: The Credit Assignment Problem, Policy Gradients, Markov Decision Processes, Temporal Difference Learning and Q-Learning, Learning to Play Ms. Pac-Man Using Deep Q-Learning

  • Define Keras
  • How to compose Models in Keras
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with Keras
  • Customizing the Training Process
  • Using TensorBoard with Keras
  • Use-Case Implementation with Keras

  • Define TFLearn
  • Composing Models in TFLearn
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with TFLearn
  • Customizing the Training Process
  • Using TensorBoard with TFLearn
  • Use-Case Implementation with TFLearn

Final Project, The topics and scenario according to current trending subjects

 

AI Courses Description

Why Deep Learning ?

  • Developers aspiring to be a ‘Data Scientist’
  • Analytics Managers who are leading a team of analysts
  • Business Analysts who want to understand Deep Learning (ML) Techniques
  • Information Architects who want to gain expertise in Predictive Analytics
  • Analysts wanting to understand Data Science methodologies

Required Pre-requisites

    • Basic programming knowledge in Python
    • Concepts about Machine Learning

AI Courses Features

Real-Time Training Experts

Experienced faculties took the classes and one to one attention, Novice to professional handing capable faculties.

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of the various DevOps concepts.

Assignments

Each class will be followed by practical assignments. Both online and offline with detailed solutions after the exam.

Lifetime Placement Support

You get lifetime access to all SMEC Technologies branches to refresh the course and get the new updates from Instructors.

24 x 7 Expert Support

We have 24×7 online support team to resolve all your technical queries, through ticket based tracking system or above the phone.

Certification

Successfully complete your final course project and SMEC Technologies will certify you as a AI Courses.

 

Be future ready. Start learning – AI Courses ? Enroll Now or Get the free career path

TensorFlow could be a game-changer in the future of AI – Google. TensorFlow could be a game-changer in the future of AI – Google

 

 

 

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