- Artificial Intelligence is a licit part of a digital computer that gets executed tasks commonly associated with intelligent beings. Well, this specific term is frequently applied to the project of building systems furnish with the intellectual processes characteristic of humans, like the ability to reason, discover meaning, generalize, or learn from past experience. Moreover, studying AI and Machine Learning will surely promise you a bright and uplifting career.
- So, if your interest lies in this field, opting for Artificial Intelligence Online Training will be beneficial for your career. You will get the opportunity to learn a lot of new things, skills and information as well.
- Moreover, getting started with Artificial Intelligence Training in Noida will eventually help you in uplifting your career in various ways. So, associating with us will be beneficial for you if you want to turn into a skilled AI Developer or AI Engineer, and want to have a positive growth.
- By eventually getting started with Artificial Intelligence course, you will come across some important information, its every section, and sub-section in a much better way.
First, you will receive sessions regarding its basic information and definition.
Furthermore, you will also receive sessions concerning 'Python for Data Analysis.
Your information concerning Math for Machine Learning will also get enhanced.
Our team will also help you in imbibing information concerning Data Visualization in Python.
Your overall information regarding Data Analysis using SQL, and advanced SQL will get uplifted.
- The salary package in this field is quite a high right from the initial level. So, you need not have to worry at all as our Artificial Intelligence Training in Noida will help you grab better job opportunities in the industry.
In the initial level, you will earn around Rs 8 to Rs 7.5 Lakh annually.
Later on, you can earn up to Rs. 50 Lakh if you have acquired the latest skills and information.
Post imbibing detailed information regarding AI, you will turn into a pro.
Post having this skill, you will be able to enter into a multi-national company that will also offer you a quite higher salary package.
- Presently, you will find various AI jobs with skilled professionals to fill them, yet the grant is a bit low, and demand is quite high. So, approaching Artificial Intelligence Training in Noida will be fruitful for your career as it will not only enhance your information concerning AI, but will also help you get updated with the latest information, and acquire skills related to its other job roles.
Imbibing in-depth information concerning this technology will eventually turn you into an expert.
Enrolling in this course will allow you to examine this subject right from the scratch.
By knowing every side of Artificial Intelligence Training in Noida, you will end up acquiring a higher position and in a good workplace.
Your basic and core information will be cleared.
You will have various job opportunities in hand.
- An AI Developer is supposed to execute a wide series of tasks. So, if you also want to turn into a skilled AI Developer, getting associated with Artificial Intelligence Training Institute in Noida will be suitable for your career, as it will help you know each role in a much-explained manner.
You will have to co-ordinate with the professionals such as programmers, coders, Data scientists, etc.
You will also have to design and handle the AI development process and overall infrastructure of a product.
You will also have to execute statistical analysis and interpret results to guide your organization's decision-making process.
You will also have to integrate necessary functions and processes for a data science team.
Working as an AI Engineer will indulge you in making Machine Learning models into APIs that other applications can evolve with.
- In the present scenario, you will find numerous reasons to get started with Artificial Intelligence Training in Noida. Moreover, by obtaining information concerning Artificial Intelligence course, you will grow in this field quicker and acquire a higher position as well. By associating with a proper institute, you will get the chance to know some of the main reasons to learn this course.
Your salary structure will positively get increased.
Your information concerning AI will get strengthened.
You will always have numerous jobs offers in hand.
Further, in your career, you will be able to turn into a freelancer and make a good additional income as well.
- Presently, you will find various companies hiring skilled AI Developers. So, if youre aim is to acquire a job post completing the Artificial Intelligence Training in Noida, then you genuinely don't have to concern at all as you will surely end up getting into a well-established as theres genuinely a huge space for skilled candidates, and we will also set your interviews with established companies. So, getting in touch with Artificial Intelligence Training Institute in Noida will only uplift your career graph.
Marj Technologies, Websites, Masters India, etc. are some of the good set-ups hiring skilled candidates.
In fact, our trainers will thoroughly help you passing the interview by often conducting a mock test.
The main aim of this course is to assist you to get settled in a well-established organization.
- For the past few years, Croma Campus has been considered the best Artificial Intelligence Training Institute in Noida. We, at Croma Campus generally aims at delivering qualitative training along with enough study material and numerous instances. So, if you are also looking to acquire detailed information concerning AI, getting associated with Croma Campus will be an ideal move toward your career.
Associating with us will give you enough chances to obtain the latest information concerning the Artificial Intelligence course.
Here, you will obtain information regarding its related course.
Croma Campus will offer you placement assistance.
Well, right from the beginning, our faculty members will give you suggestive tips to clear the interview process.
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Artificial Intelligence Certification training
- With our AZ-900 “Microsoft Azure fundamentals” certification Training you will learn foundational knowledge of cloud services and how those services are provided with Microsoft Azure. The exam is intended for candidates who are just beginning to work with cloud-based solutions and services or are new to Azure.
- In this program you will learn:
Python Statistics for AI
Python - MySQL
Data Science Professional Program
Machine Learning
Live Projects
- Introduction To Python:
Installation and Working with Python
Understanding Python variables
Python basic Operators
Understanding the Python blocks.
- Introduction To Variables:
Variables, expression condition and function
Global and Local Variables in Python
Packing and Unpacking Arguments
Type Casting in Python
Byte objects vs. string in Python
Variable Scope
- Python Data Type:
Declaring and usingNumeric data types
Using stringdata type and string operations
Understanding Non-numeric data types
Understanding the concept of Casting and Boolean.
Strings
List
Tuples
Dictionary
Sets
- Introduction Keywords and Identifiers and Operators
Python Keyword and Identifiers
Python Comments, Multiline Comments.
Python Indentation
Understating the concepts of Operators
- Data Structure
- What is List.
- List Creation
- List Length
- List Append
- List Insert
- List Remove
- List Append & Extend using “+” and Keyword
- List Delete
- List related Keyword in Python
- List Revers
- List Sorting
- List having Multiple Reference
- String Split to create a List
- List Indexing
- List Slicing
- List count and Looping
- List Comprehension and Nested Comprehension
- Dict Creation
- Dict Access (Accessing Dict Values)
- Dict Get Method
- Dict Add or Modify Elements
- Dict Copy
- Dict From Keys.
- Dict Items
- Dict Keys (Updating, Removing and Iterating)
- Dict Values
- Dict Comprehension
- Default Dictionaries
- Ordered Dictionaries
- Looping Dictionaries
- Dict useful methods (Pop, Pop Item, Str , Update etc.)
List
Dictionary
- Sets, Tuples and Looping Programming
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Frozen Sets
- What is Tuple
- Tuple Creation
- Accessing Elements in Tuple
- Changinga Tuple
- TupleDeletion
- Tuple Count
- Tuple Index
- TupleMembership
- TupleBuilt in Function (Length, Sort)
- Loops
- Loops and Control Statements (Continue, Break and Pass).
- Looping techniques in Python
- How to use Range function in Loop
- Programs for printing Patterns in Python
- How to use if and else with Loop
- Use of Switch Function in Loop
- Elegant way of Python Iteration
- Generator in Python
- How to use nested IF and Else in Python
- How to use nested Loop in Python
- Use If and Else in for and While Loop
- Examples of Looping with Break and Continue Statements
- How to use IN or NOTkeywordin Python Loop.
Sets
Tuple
Control Flow
- Exception and File Handling, Module, Function and Packages
- Python Errors and Built-in-Exceptions
- Exception handing Try, Except and Finally
- Catching Exceptions in Python
- Catching Specific Exception in Python
- Raising Exception
- Try and Finally
- Opening a File
- Python File Modes
- Closing File
- Writing to a File
- Reading from a File
- Renaming and Deleting Files in Python
- Python Directory and File Management
- List Directories and Files
- Making New Directory
- Changing Directory
- Python Syntax
- Function Call
- Return Statement
- Write an Empty Function in Python –pass statement.
- Lamda/ Anonymous Function
- *argsand **kwargs
- Help function in Python
- Scope and Life Time of Variable in Python Function
- Nested Loop in Python Function
- Recursive Function and Its Advantage and Disadvantage
- Organizing python codes using functions
- Organizing python projects into modules
- Importing own module as well as external modules
- Understanding Packages
- Programming using functions, modules & external packages
- Map, Filter and Reduce function with Lambda Function
- More example of Python Function
Python Exception Handling
Python File Handling
Python Function, Modules and Packages
- Data Automation (Excel, SQL, PDF etc)
- Concept of Class, Object and Instances
- Constructor, Class attributes and Destructors
- Real time use of class in live projects
- Inheritance, Overlapping and Overloading operators
- Adding and retrieving dynamic attributes of classes
- Programming using Oops support
- SQL Database connection using
- Creating and searching tables
- Reading and Storing configinformation on database
- Programming using database connections
- Reading an excel file usingPython
- Writing toan excel sheet using Python
- Python| Reading an excel file
- Python | Writing an excel file
- Adjusting Rows and Column using Python
- ArithmeticOperation in Excel file.
- Plotting Pie Charts
- Plotting Area Charts
- Plotting Bar or Column Charts using Python.
- Plotting Doughnut Chartslusing Python.
- Consolidationof Excel File using Python
- Split of Excel File Using Python.
- Play with Workbook, Sheets and Cells in Excel using Python
- Creating and Removing Sheets
- Formatting the Excel File Data
- More example of Python Function
- Extracting Text from PDFs
- Creating PDFs
- Copy Pages
- Split PDF
- Combining pages from many PDFs
- Rotating PDF’s Pages
- Check Dirs. (exist or not)
- How to split path and extension
- How to get user profile detail
- Get the path of Desktop, Documents, Downloads etc.
- Handle the File System Organization using OS
- How to get any files and folder’s details using OS
Python Object Oriented Programming—Oops
Python Database Interaction
Reading an excel
Working with PDF and MS Word using Python
Complete Understanding of OS Module of Python
- Data Analysis & Visualization
- Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to files
- How to get record specific records Using Pandas Adding & Resetting Columns, Mapping with function
- Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
- Exploring the Data Plotting, Correlations, and Histograms
- Getting statistical information about the data Analysis Concepts, Handle the None Values
- Reading files with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
- Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
- Applying formulas on the columns Basic Grouping, Concepts of Aggregate Function
- Complete Understanding of Pivot Table Data Slicing using iLocand Locproperty (Setting Indices)
- Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
- Exporting the results to Excel Joins:
- Python | Pandas Data Frame Inner Join
- Under sting the properties of Data Frame Left Join (Left Outer Join)
- Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
- Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
- Pandas | Find Missing Data and Fill and Drop NA Appending DataFrameand Data
- Pandas | How to Group Data How to apply Lambda / Function on Data Frame
- Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
- Introduction to NumPy: Numerical Python
- Importing NumPy and Its Properties
- NumPy Arrays
- Creating an Array from a CSV
- Operations an Array from aCSV
- Operations with NumPy Arrays
- Two-Dimensional Array
- Selecting Elements from 1-D Array
- Selecting Elements from 2-D Array
- Logical Operation with Arrays
- Indexing NumPy elements using conditionals
- NumPy’sMean and Axis
- NumPy’sMode, Median and Sum Function
- NumPy’sSort Function and More
- Bar Chart using Python MatPlotLib
- Column Chart using Python MatPlotLib
- Pie Chart using Python MatPlotLib
- Area Chart using Python MatPlotLib
- Scatter Plot Chart using Python MatPlotLib
- Play with Charts Properties Using MatPlotLib
- Export the Chart as Image
- Understanding plt. subplots () notation
- Legend Alignment of Chart using MatPlotLib
- Create Charts as Image
- Other Useful Properties of Charts.
- Complete Understanding of Histograms
- Plotting Different Charts, Labels, and Labels Alignment etc.
- Introduction to Seaborn
- Making a scatter plot with lists
- Making a count plot with a list
- Using Pandas with seaborn
- Tidy vs Untidy data
- Making a count plot with a Dataframe
- Adding a third variable with hue
- Hue and scattera plots
- Hue and count plots
- Introduction to relational plots and subplots
- Creating subplots with col and row
- Customizing scatters plots
- Changing the size of scatter plot points
- Changing the style of scatter plot points
- Introduction to line plots
- Interpreting line plots
- Visualizing standard deviation with line plots
- Plotting subgroups in line plots
- Current plots and bar plots
- Count plots
- Bar plot with percentages
- Customizing bar plots
- Box plots
- Create and interpret a box plot
- Omitting outliers
- Adjusting the whiskers
- Point plots
- Customizing points plots
- Point plot with subgroups
- Changing plot style and colour
- Changing style and palette
- Changing the scale
- Using a custom palette
- Adding titles and labels: Part 1
- Face Grids vs. Axes Subplots
- Adding a title to a face Grid object
- Adding title and labels: Part 2
- Adding a title and axis labels
- Rotating x-tics labels
- Putting it all together
- Box plot with subgroups
- Bar plot with subgroups and subplots
Pandas
NumPy
MatPlotLib
Introduction to Seaborn
Visualizing Two Quantitative Variables
Visualizing a Categorical and a Quantitative Variable
Customizing Seaborn Plots
- Python - MySQL
- Single Row Functions
- Character Functions, Number Function, Round, Truncate, Mod, Max, Min, Date
Introduction to MySQL
What is the MySQLdb
How do I Install MySQLdb
Connecting to the MYSQL
Selecting a database
Adding data to a table
Executing multiple queries
Exporting and Importing data tables.
SQL Functions
- General Functions
Count, Average, Sum, Now etc.
- Joining Tables
Obtaining data from Multiple Tables
Types of Joins (Inner Join, Left Join, Right Join & Full Join)
Sub-Queries Vs. Joins
- Operators (Data using Group Function)
Distinct, Order by, Group by, Equal to etc.
- Database Objects (Constraints & Views)
Not Null
Unique
Primary Key
Foreign Key
- Structural & Functional Database Testing using TOAD Tool
- SQL Introduction
- SQL Syntax
- SQL Select
- SQL Distinct
- SQL Where
- SQL And & Or
- SQL Order By
- SQL Insert
- SQL Update
- SQL Delete
- SQL Like
- SQL Wildcards
- SQL In
- SQL Between
- SQL Alias
- SQL Joins
- SQL Inner Join
- SQL Left Join
- SQL Right Join
- SQL Full Join
- SQL Union
- SQL Avg()
- SQL Count()
- SQL First()
- SQL Last()
- SQL Max()
- SQL Min()
- SQL Sum()
- SQL Group By
SQL Basic
SQL Advance
SQL Functions
- Introduction to Data Science
What is Analytics & Data Science
Common Terms in Analytics
What is data
Classification of data
Relevance in industry and need of the hour
Types of problems and business objectives in various industries
How leading companies are harnessing the power of analytics
Critical success drivers
Overview of analytics tools & their popularity
Analytics Methodology & problem-solving framework
List of steps in Analytics projects
Identify the most appropriate solution design for the given problem statement
Project plan for Analytics project & key milestones based on effort estimates
Build Resource plan for analytics project
Why Python for data science
- Accessing/Importing and Exporting Data
Importing Data from various sources (Csv, txt, excel, access etc)
Database Input (Connecting to database)
Viewing Data objects - sub setting, methods
Exporting Data to various formats
Important python modules: Pandas
- Data Manipulation: Cleansing - Munging Using Python Modules
Cleansing Data with Python
Filling missing values using lambda function and concept of Skewness.
Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.
Normalizing data
Feature Engineering
Feature Selection
Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.
Label encoding/one hot encoding
- Data Analysis: Visualization Using Python
Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc.)
Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)
- Introduction to Statistics
Descriptive Statistics
Sample vs Population Statistics
Random variables
Probability distribution functions
Expected value
Normal distribution
Gaussian distribution
Z-score
Central limit theorem
Spread and Dispersion
Inferential Statistics-Sampling
Hypothesis testing
Z-stats vs T-stats
Type 1 & Type 2 error
Confidence Interval
ANOVA Test
Chi Square Test
T-test 1-Tail 2-Tail Test
Correlation and Co-variance
- Introduction to Predictive Modelling
Concept of model in analytics and how it is used
Common terminology used in Analytics & Modelling process
Popular Modelling algorithms
Types of Business problems - Mapping of Techniques
Different Phases of Predictive Modelling
- Data Exploration for Modelling
Need for structured exploratory data
EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
Identify missing data
Identify outliers’ data
Imbalanced Data Techniques
- Data Pre-Processing & Data Mining
Data Preparation
Feature Engineering
Feature Scaling
Datasets
Dimensionality Reduction
Anomaly Detection
Parameter Estimation
Data and Knowledge
Selected Applications in Data Mining
- Introduction to Machine Learning
- AI overview
- Meaning, scope, and 3 stages of AI
- Decoding AI
- Features of AI
- Applications of AI
- Image recognition
- Effect of AI on society
- AI for industries
- Overview of machine learning
- ML and AI relationship
Artificial Intelligence
Machine Learning
Techniques of Machine Learning
Machine Learning Algorithms
Algorithmic models of Learning
Applications of Machine Learning
Large Scale Machine Learning
Computational Learning theory
Reinforcement Learning
- Supervised Machine Learning
- What is Supervised Learning
- Algorithms in Supervised learning
- Regression & Classification
- Regression vs classification
- Computation of correlation coefficient and Analysis
- Multivariate Linear Regression Theory
- Coefficient of determination (R2) and Adjusted R2
- Model Misspecifications
- Economic meaning of a Regression Model
- Bivariate Analysis
- Naive Bayes classifier, Model Training
- ANOVA (Analysis of Variance)
Supervised Learning
Semi-supervised and Reinforcement Learning
Bias and variance Trade-off
Representation Learning
- Regression
Regression and its Types
Logistic Regression
Linear Regression
Polynomial Regression
- Classification
Meaning and Types of Classification
Nearest Neighbor Classifiers
K-nearest Neighbors
Probability and Bayes Theorem
Support Vector Machines
Naive Bayes
Decision Tree Classifier
Random Forest Classifier
- Unsupervised Learning: Clustering
About Clustering
Clustering Algorithms
K-means Clustering
Hierarchical Clustering
Distribution Clustering
- Model optimization and Boosting
Ensemble approach
K-fold cross validation
Grid search cross validation
Ada boost and XG Boost
- Introduction to Deep Learning
What are the Limitations of Machine Learning
What is Deep Learning
Advantage of Deep Learning over Machine learning
Reasons to go for Deep Learning
Real-Life use cases of Deep Learning
- Deep Learning Networks
What is Deep Learning Networks
Why Deep Learning Networks
How Deep Learning Works
Feature Extraction
Working of Deep Network
Training using Backpropagation
Variants of Gradient Descent
Types of Deep Networks
Feed forward neural networks (FNN)
Convolutional neural networks (CNN)
Recurrent Neural networks (RNN)
Generative Adversal Neural Networks (GAN)
Restrict Boltzman Machine (RBM)
- Deep Learning with Keras
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
Intuitively building networks with Keras
- Convolutional Neural Networks (CNN)
Introduction to Convolutional Neural Networks
CNN Applications
Architecture of a Convolutional Neural Network
Convolution and Pooling layers in a CNN
Understanding and Visualizing CNN
Transfer Learning and Fine-tuning Convolutional Neural Networks
- Recurrent Neural Network (RNN)
Intro to RNN Model
Application use cases of RNN
Modelling sequences
Training RNNs with Backpropagation
Long Short-Term Memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model
Time Series Forecasting
- Natural Language Processing
NLP with python
Bags of words
Stemming
Tokenization
Lemmatization
TF-IDF
Sentiment Analysis
Overview of Tensor Flow
- What is Tensor Flow
Tensor Flow code-basics
Graph Visualization
Constants, Placeholders, Variables
Tensor flow Basic Operations
Linear Regression with Tensor Flow
Logistic Regression with Tensor Flow
K Nearest Neighbor algorithm with Tensor Flow
K-Means classifier with Tensor Flow
Random Forest classifier with Tensor Flow
- Neural Networks Using Tensor Flow
Quick recap of Neural Networks
Activation Functions, hidden layers, hidden units
Illustrate & Training a Perceptron
Important Parameters of Perceptron
Understand limitations of A Single Layer Perceptron
Illustrate Multi-Layer Perceptron
Back-propagation – Learning Algorithm
Understand Back-propagation – Using Neural Network Example
TensorBoard
- Introduction to Big Data Hadoop and Spark
What is Big Data
Big Data Customer Scenarios
Understanding BIG Data: Summary
Few Examples of BIG Data
Why BIG data is a BUZZ
How Hadoop Solves the Big Data Problem
What is Hadoop
Hadoop’s Key Characteristics
Hadoop Cluster and its Architecture
Hadoop: Different Cluster Modes
Why Spark is needed
What is Spark
How Spark differs from other frameworks
Spark at Yahoo!
- BIG Data Analytics and why it’s a Need Now
What is BIG data Analytics
Why BIG Data Analytics is a ‘need’ now
BIG Data: The Solution
Implementing BIG Data Analytics – Different Approaches
- Traditional Analytics vs. BIG Data Analytics
The Traditional Approach: Business Requirement Drives Solution Design
The BIG Data Approach: Information Sources drive Creative Discovery
Traditional and BIG Data Approaches
BIG Data Complements Traditional Enterprise Data Warehouse
Traditional Analytics Platform v/s BIG Data Analytics Platform
- Big Data Technologies
- What is Scala
- Scala in other Frameworks
- Introduction to Scala REPL
- Basic Scala Operations
- Variable Types in Scala
- Control Structures in Scala
- Understanding the constructor overloading,
- Various abstract classes
- The hierarchy types in Scala,
- For-each loop, Functions and Procedures
- Collections in Scala- Array
- Overview to Spark
- Spark installation, Spark configuration,
- Spark Components & its Architecture
- Spark Deployment Modes
- Limitations of Map Reduce in Hadoop
- Working with RDDs in Spark
- Introduction to Spark Shell
- Deploying Spark without Hadoop
- Parallel Processing
- Spark MLLib - Modelling Big Data with Spark
Scala
Spark
- Apache Kafka and Flume
What is Kafka Why Kafka
Configuring Kafka Cluster
Kafka architecture
Producing and consuming messages
Operations, Kafka monitoring tool
Need of Apache Flume
What is Apache Flume
Understanding the architecture of Flume
Basic Flume Architecture
- Live Projects
Managing credit card Risks
Bank Loan default classification
YouTube Viewers prediction
Super store Analytics (E-commerce)
Buying and selling cars prediction (like OLX process)
Advanced House price prediction
Analytics on HR decisions
Survival of the fittest
Twitter Analysis
Flight price prediction
+ More Lessons
Mock Interviews
Projects
Phone (For Voice Call):
+91-971 152 6942WhatsApp (For Call & Chat):
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FAQ's
Croma Campus provides amazing Artificial Intelligence Training in Noida as per the current industry standards. Our training tactic will enable professionals to secure placements in MNCs. Croma Campus is one of the most recommended Artificial Intelligence Training Institute in Noida that offers hands-on practical information / practical implementation on live projects and will ensure the job with the help of advance level Artificial Intelligence training courses.
Artificial Intelligence (AI) assistance for a job - Like Data Scientists, Software Analysts and Developers, Computer Engineers and Computer Scientists, Algorithm Specialists, Research Scientists and Engineering consultants, Maintenance Technicians, and Mechanical Engineers.
Croma Campus: Artificial Intelligence Training Institute in Noida is conducted by specialist working certified corporate professionals having 8+ years of experience in implementing real-time Artificial Intelligence projects. Candidates will implement the following concepts under Artificial Intelligence: Fundamentals of Reinforcement Learning, Q-Learning Intuition, Building a Self-Driving Car, Creating the environment, Building an AI on real-time projects along with Artificial Intelligence Placement Training modules like aptitude test preparation, etc.
There are four types of Artificial Intelligence: Reactive Machines, Limited Memory, Theory of Mind and Self-Awareness.
The ways to connect Croma Campus.
- Phone number: +91-120-4155255, +91-9711526942
- Email: info@cromacampus.com
- Address: G-21, Sector-03, Noida (201301)
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- - Get Tips from Trainer to Clear Interviews
- - Attend Mock-Up Interviews with Experts
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Case studies based on top industry frameworks help you to relate your learning with real-time based industry solutions.
Assignment
Adding the scope of improvement and fostering the analytical abilities and skills through the perfect piece of academic work.
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With no limits to learn and in-depth vision from all-time available support to resolve all your queries related to the course.
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