Course Highlights and Why Artificial Intelligence Course at Sedulous
Software?
Our course modules are designed by experts to meet the needs of the global industry.
You will come out with extensive knowledge concerning artificial neural networks and deep learning.
FITA Academy provides the best artificial intelligence course in Chennai at an affordable price.
Flexibly scheduled batches for both offline and online courses throughout weekdays and weekends.
100% placement assurance after training completion for the next level in your career.
An active placement cell assists students in achieving their goals and securing ideal careers.
Artificial Intelligence Course Objectives
Understand simple Python programming, the background, the contrast among Python versions, and how to
establish a Python development environment.
Understand how data extraction, management techniques, and statistical analysis processes are managed in
Python.
Learn exploratory data analysis using NumPy, SciPy, and Pandas libraries.
Get started with machine learning, its evaluation, and the improvement in performance of these models.
Learn about supervised and unsupervised learning.
Understand Hypothesis Testing in Machine Learning, including T-test and ANOVA.
Learn TensorFlow for Deep Learning: neural network architecture, forward and backpropagation, and hands-on
projects like Chatbot and Facial Recognition systems.
Artificial Intelligence Course
Using Python for Machine Learning - Introduction to Data Science
Understanding Data Science and its real-world applications.
Explore the Data Science Life Cycle and its phases.
Introduction to Artificial Intelligence (AI) and its practical implications.
Overview of AI Implementation with real-time use cases.
Basics of Machine Learning and Deep Learning.
Learn Artificial Neural Networks (ANN) and how they work.
Introduction to Natural Language Processing (NLP).
Understand how Python integrates with Machine Learning technologies.
Python as a powerful tool for Machine Learning implementation.
Introduction to Python
What is Python and history of Python
Python-2 and Python-3 differences
Install Python and Environment Setup
Python Identifiers, Keywords, and Indentation
Comments and document interlude in Python
Command-line arguments and Getting User Input
Python Basic Data Types and Variables
Introduction to Python
What is Python and history of Python
Python-2 and Python-3 differences
Install Python and Environment Setup
Python Identifiers, Keywords, and Indentation
Comments and document interlude in Python
Command-line arguments and Getting User Input
Python Basic Data Types and Variables
List, Ranges & Tuples in Python
Understanding Lists in Python
Understanding Iterators
Generators, Comprehensions and Lambda Expressions
Understanding and using Ranges
Python Dictionaries and Sets
Introduction to the section
Python Dictionaries and More on Dictionaries
Sets and Python Sets Examples
Input and Output in Python
Reading and writing text files
Appending to Files
Writing Binary Files Manually and using Pickle Module
Python Functions
Python user defined functions
Python packages functions
The anonymous Functions
Loops and statement in Python
Python Modules & Packages
Python Exceptions Handling
What is Exception?
Handling an exception
try….except…else
try-finally clause
The argument of an Exception
Python Standard Exceptions
Raising an exceptions
User-Defined Exceptions
Python Regular Expressions
What are regular expressions?
The match Function and the Search Function
Matching vs Searching
Search and Replace
Extended Regular Expressions and Wildcard
Useful additions
Collections – named tuples, default dicts
Debugging and breakpoints, Using IDEs
Data Manipulation using Python
Understanding different types of Data
Understanding Data Extraction
Managing Raw and Processed Data
Wrangling Data using Python
Using Mean, Median and Mode
Variation and Standard Deviation
Probability Density and Mass Functions
Understanding Conditional Probability
Exploratory Data Analysis (EDA)
Working with Numpy, Scipy and Pandas
Understanding Machine Learning Models
Understand what is a Machine Learning Model
Various Machine Learning Models
Choosing the Right Model
Training and Evaluating the Model
Improving the Performance of the Model
More on Models
Understanding Predictive Model
Working with Linear Regression
Working with Polynomial Regression
Understanding Multi Level Models
Selecting the Right Model or Model Selection
Need for selecting the Right Model
Understanding Algorithm Boosting
Various Types of Algorithm Boosting
Understanding Adaptive Boosting
Understanding Machine Learning Algorithms
Understanding the Machine Learning Algorithms
Importance of Algorithms in Machine Learning
Exploring different types of Machine Learning Algorithms
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Exploring Supervised Learning Algorithms
Understanding the Supervised Learning Algorithm
Understanding Classifications
Working with different types of Classifications
Learning and Implementing Classifications
Logistic Regression
Naïve Bayes Classifier
Nearest Neighbour
Support Vector Machines (SVM)
Decision Trees
Boosted Trees
Random Forest
Time Series Analysis (TSA)
Understanding Time Series Analysis
Advantages of using TSA
Understanding various components of TSA
AR and MA Models
Understanding Stationarity
Implementing Forecasting using TSA
Exploring Un-Supervised Learning Algorithms
Understanding Unsupervised Learning
Understanding Clustering and its uses
Exploring K-means
What is K-means Clustering
How K-means Clustering Algorithm Works
Implementing K-means Clustering
Exploring Hierarchical Clustering
Understanding Hierarchical Clustering
Implementing Hierarchical Clustering
Understanding Dimensionality Reduction
Importance of Dimensions
Purpose and advantages of Dimensionality Reduction
Understanding Hypothesis Testing
What is Hypothesis Testing in Machine Learning
Advantages of using Hypothesis Testing
Basics of Hypothesis
Normalization
Standard Normalization
Parameters of Hypothesis Testing
Null Hypothesis
Alternative Hypothesis
The P-Value
Types of Tests
T Test
Z Test
ANOVA Test
Chi-Square Test
Overview Reinforcement Learning Algorithm
Understanding Reinforcement Learning Algorithm
Advantages of Reinforcement Learning Algorithm
Components of Reinforcement Learning Algorithm
Exploration Vs Exploitation tradeoff
Hands on Projects
Implementing Deep Learning Using TensorFlow
Introduction to Deep Learning
Understanding Artificial Intelligence
Understanding Machine Learning
Understanding the need for Deep Learning for Machines
Understanding Deep Learning
Understanding the Importance of Neural Network
Understanding how Artificial Intelligence, Machine Learning and Deep Learning are related
Introduction to Deep Learning Frameworks
Introduction to Tensorflow and Keras
Setting Up Deep Learning Environment
Setting Up Deep Learning Environment
Installing Tensorflow
Installing Keras
Understanding Deep Learning Environment in Cloud Platform with AWS
Executing Tensorflow Code
Executing Tensorflow in AWS
Exploring Tensorflow
Exploring Tensorflow
Understanding Placeholders
Creating Placeholders
Updating Placeholders with Data
Understanding Variables and Constants
Understanding Computation Graph
Exploring Tensor Board
Understanding Functions in Tensorflow
Exploring various Key Functions
Activation Functions
Sigmoid Functions and Softmax Functions
Understanding Rectified Linear Units - ReLu and Hyperbolic Tangent Functions
Building Neural Network
Understanding a Neural Network
Understanding the Components of a Neural Network
Input Layers
Computational Layers
Output Layers
Understanding Forward Propagation and Back-Propagation
Understanding the Hyper Parameters
Understanding Perceptron
Understanding Inputs and Weights
Understanding Outputs
Understanding Multi Layered Perceptron (MLP)
Understanding and implementing Regularization
Training Neural Networks
Understanding Training Data Sets
Understanding and using the MNIST Data Set
Application Areas of MLP
Working examples for MLP using Tensorflow and Keras
Understanding Convolutional Neural Networks (CNN)
Understanding what is Convolutional Neural Networks
Understanding the Architecture of CNN
Understanding the Convolutional Layers
Understanding the Pooling Layer
Understanding the Normalization Layer
Understanding the Fully-Connected Layer
Understanding various Popular CNN Architectures and Models
Exploring the Imagenet Dataset
Understanding MLP Vs CNN
Application Areas of CNN
Working Examples for CNN using Tensorflow and Keras
Understanding Recurrent Neural Networks (RNN)
Understanding Sequences
Need for Neural Networks to Handle Sequences
Understanding Recurrent Neural Networks – RNN
Understanding the Recurrent Neuron
Managing Forward Propagation and Back Propagation in a RNN
Exploring various RNN Architectures
Application Areas of RNN
Working Examples for RNN using Tensorflow and Keras
Understanding Recursive Neural Networks
Understanding Recursive Neural Networks
Understanding the differences between Recurrent and Recursive Neural networks
Application areas of Recursive Neural Networks
Working Examples for Recursive Neural Networks using Tensorflow and Keras