DataScience–Artificial Intelligence

/DataScience–Artificial Intelligence
DataScience–Artificial Intelligence 2021-04-11T16:27:10+00:00

Multiple steps of algorithms, programming and scientific methods to process vast data and extract meaningful information is the primary object of Data Science. Meaningful and valuable information is the outcome of using various tools and techniques in data science.

Why DataScience–AI?

It provides meaningful predictions using large amount of raw and unstructured data.

5K + satisfied learners. Reviews

We can get better understanding of dat aby using various technologies and algorithms.

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Upcoming Batches

07
Nov

Mon – Fri ( Weekdays )

06:00 AM – 07:00 AM ( IST )

08
Nov

Mon – Fri ( Weekdays )

8:00 PM – 09:00 PM ( IST )

Can’t find convenient schedule? Let us know

Upcoming Batches

Upcoming Batches

07
Nov

Mon – Fri ( Weekdays )

06:00 AM – 07:00 AM ( IST )

08
Nov

Mon – Fri ( Weekdays )

8:00 PM – 09:00 PM ( IST )

Can’t find convenient schedule? Let us know

Fast Track

14 Jan, 2021 – 28 Jan, 2021

07:00 AM IST

 

Fast Track

14 Jan, 2021 – 28 Jan, 2021

07:00 AM IST

 

Fast Track

14 Jan, 2021 – 28 Jan, 2021

07:00 AM IST

 

Fast Track

14 Jan, 2021 – 28 Jan, 2021

07:00 AM IST

 

Timings Doesn’t Suit You ?

We can set up a batch at your Convenient time.

Let us know

Can’t find convenient schedule? Let us know

Upcoming Batches

07
Nov

Mon – Fri ( Weekdays )

06:00 AM – 07:00 AM ( IST )

08
Nov

Mon – Fri ( Weekdays )

8:00 PM – 09:00 PM ( IST )

Can’t find convenient schedule? Let us know

Modes of Delivery

Self-Paced Training

Classroom Training

Well structured and equipped classroom training with excellent lab facility.

IT Online Training

Online Training

Online training provided at flexible timings which can be attended across the globe with 24*7 server facility.

IT Online Training

Corporate Training

Training provided to employees to sharpen their skills to increase their productivity to work for various projects.

About Course

Data science uses tools and technologies to manipulate raw and unstructured data to provide more meaningful data. It helps in visualizing and reporting of data to take decisions and solve problems, for this it scans large amount of data looking for patterns, relations and so on.

It helps data to be presented in a visual format so that it can be more appealing to users. It uses statistical methods and tools to work on vast numerical data to find meaningful data. It looks into hidden insights and patterns to bring out very big business making decisions.

Artificial Intelligence (AI) as the name suggests is intelligence that is artificial i.e not humans. Machines and software are programmed in such a way that they start behaving, work, learn and behave like humans. Human intelligence will be artificially developed in computers and software thus reducing human interaction with systems.

AI can be used to build expert systems and intelligence like humans .Its technology is based on multiple modules like mathematics, science and linguistics. Its been used in different domains like medical, education, engineering, robotics. In future more and more areas of human interaction are going to be impacted by AI.

Training objectives of DataScience–Artificial Intelligence Course

After successful completion of DataScience–Artificial Intelligence at Data Labs Training, the student is expected to :

  • Gain in-depth knowledge in all basic and fundamental concepts of Data Science.
  • Understand the relevance of Data Science in todays world.
  • How large data provided to algorithms can predict outcomes.
  • Gain expertise in Data Science tools.
  • Understand how Data science is used in various domains like science and engineering
  • Data Science is mostly used by social media platforms and OTT platforms.
  • Gain indepth knowledge in alll basic and fundamental concepts of AI.
  • Understand the relevance of AI in todays world.
  • How mundane and repetitive tasks can be automated by AI
  • Gain expoertise in AI toools.
  • Understand how AI is used in various domains like science and engineering
  • Algebra and calculus are integral as it is needed for learning AI concepts.
  • Learning Phyton programming as it is key to mastering AI

How is the DataScience–Artificial Intelligence from DataLabs Training beneficial to you?

Our training program on Data Science focuses on helping students get on boarded to the idea of working on the data science applications and technologies. Focus on understanding and processing Data is done to enable students work on data mining frameworks.

Programming language like Python and Machine learning are dealt to help data scientist to design and develop applications and algorithms for working on data processing and analytical applications.

Multiple cases studies and assignment are worked upon to get practical real-time understanding of the science of data processing and to make students market ready by the time they finish the course.

Alegbra and various other mathematical concepts are needed to work on AI . Our training provides indepth study on these mathematical concepts and how these concepts are dealt in reference to AI before commencing the computer programming.

Students will be provided indepth analysis of AI technology that are neeeded and how these appplications can be used in various sectors and industries.Our Artificial Intelligence Training program focuses on constant upgrade of technology and programming in order to at par with the industry.

Who can Learn?

The DataScience–Artificial Intelligence course is ideal for an individual who falls under any of the following categories:

  • Software professional working in It industry and loooking for a change in their careeer
  • Fresher Graduates who want to work in IT field.
  • Professional who are working in other areas of software like development, testing.
  • Working IT professional in the field of DBA and web development.
  • Business analysts who want to venture into AI
  • System Administrators.
  • Call a Course Adviser for Career Counselling
  • +91 70951 67689

Pre-requisites

Anyone with sound engineering background who have good programming skills . Experience in web development and other core programming languages would be an added advantage.

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Curriculum

  • What is data science?
  • How is data science different from BI and Reporting?
  • What is difference between AI, Data Science, Machine Learning, Deep Learning
  • Job Land scape and Preparation Time
  • Who are data scientists?
  • What is day to day job of Data Scientist
  • End to End Data Science Project Life Cycle
  • Data Science roles – functions, pay across domains, experience
1. Introduction to statistics
2. Summarizing Data

  • Central Tendency measures – Mean, Median and Mode
  • Measures of Variability – Range, Interquartile Range, Standard Deviation and Variance
  • Measures of Shape – Skewness and Kurtosis
  • Covariance, Correlation

3. Data Visualization

  • Histograms
  • Pie charts
  • Bar Graphs
  • Box Plot
  • Scatter plot

4. Probability basics
5. Parametric and Non parametric Statistical Tests

  • ‘f’ Test
  • ‘z’ Test
  • ‘t’ Test
  • Chi-Square test

6. Probability Distributions

  • Expected value and variance
  • Discrete and Continuous
  • Bernoulli Distribution
  • Binomial Distribution
  • Normal Distribution
  • Uniform Distribution
  • Empirical Rule
  • Chebyshev’s Theorem

7. Sampling methods and Central Limit Theorem

  • Overview
  • Random sampling
  • Stratified sampling
  • Cluster sampling
  • Central Limit Theorem

8. Hypothesis Testing

  • Type I error
  • Type II error
  • Null and Alternate Hypothesis
  • Reject or Acceptance criterion
  • P-value

9. Confidence Intervals
10. ANOVA

  • Assumptions
  • One way
  • Two way
Introduction to Machine Learning

  • What is Machine Learning?
  • Statistics (vs) Machine Learning
  • Types of Machine Learning
  • Types of Machine Learning
1. Classification

  • Nearest Neighbor Methods (knn)
  • Logistic

2. Tree based Models – Decision Tree

  • Basics
  • Classification Trees

3. Probabilistic methods

  • Bayes Rule
  • Naïve Bayes

4. Regression Analysis

  • Simple Linear Regression
  • Assumptions
  • Model development and interpretation
  • Sum of Least Squares
  • Model validation
  • Multiple Linear Regression

5. Regression Shrinkage Methods

  • Lasso
  • Ridge

6. Advanced Models – Black Box

  • Support Vector Machine
  • Neural Networks

7. Ensemble Models

  • Random Forest
  • Gradient Boosting

8. Optimization

  • Gradient Descent (Batch and Stochastic)
1. Association Rules (Market Basket Analysis)

  • Apriori

2. Cluster Analysis

  • Hierarchical clustering
  • K-Means clustering

3. Dimensionality Reduction

  • Principal Component Analysis
  • Confusion Matrix and its metrics
  • ROC Curve (AUC)
  • R Squared
  • Adjusted R Squared
  • Root Mean Square Error (RMSE)
  • K-fold Cross Validation
1. Introduction to Natural Language Processing
2. Sentiment Analysis
3. Text Similarity
4. Text Preprocessing

  • Tokenization
  • Stemming
  • Lemmatization

5. Text Modelling

  • POS tagging
  • TFIDF and classification
  • Recurrent Neural Networks
  • Convolutional networks
1. Introduction

  • R Overview
  • Installation of R and RStudio software
  • Important R Packages
  • Datatypes in R – Vectors, Lists, Matrices, Arrays, Data Frames

2. Decision making & Loops

  • If-else, while, for
  • Next, break. try-catch

3. Functions

  • Writing functions
  • Nested functions

4. Built-in functions

  • Apply, Sapply , Lapply etc.

5. Data Preparation/Manipulation

  • Reading and Writing Data
  • Summarize and structure of data
  • Exploring different datasets in R
  • Sub Setting Data Frames
  • String manipulation in Data Frames
  • Handling Missing Values, Changing Data types, Data Binning Techniques, Dummy Variables

6. Data Visualization

  • Basic charts – Histograms, Bar plots, Line graphs, Scatter plots etc.
1. Introduction

  • How is Python different from R
  • Installing Anaconda- Python
  • Setting up with spyder

2. Datatypes in Python
3. Importing modules
4. Introduction to Strings
5. String manipulation
6. Control loops:
7. Numpy
8. Pandas
9. Scikit-Learn – Machine Learning in Python
10. Matplotlib

Faq’s

DataLabs training is one of the most sought out training institutes which has mentored more than 50,000 students to achieve their career goals. Our industry experienced and well qualified trainers have nurtured students to achieve their dreams. With instructor led practical sessions make the students face the practicality of real time approach and make them market ready.
The students can enroll for sessions only after they are satisfied by the free demo video that is available on the landing page of this course. It would be ideal if you take note that the demo video is just for an applicant reference and just enables the student to comprehend the look and feel of our course interface.
We give access to all our students the materials and assignments for a period of 365 days from the day of their enrollment. They can refer the same if they miss any.
Students can interact with our trainers through email, the details of which will be given toward the beginning of the course and anyplace in the middle. You can rely on us for any questions that you look all through the course, till you finish it.
We are more than willing to help the students as far as we can; however, at the present time we don’t give any placement assistance. In any case, we are relying on the administration to start this facility sooner than later. We will update you as often as possible as and when we present the situation help include.
Trainers at Data Labs Training have experience of over 15 years in industry with vast knowledge.
Yes, yet the student must pay an additional expense for this change to happen. The charge contrast would be the same as given at the time of the application.
Yes. Our trainers can be contacted through email, the details of which will be given toward the beginning of the course and anywhere throughout the course. You can rely on us for any questions that you look all through the course, till you finish it.

Projects

System requirements for DataScience–Artificial Intelligence Course

Students willing to learn DS need to have a sytem with muti core CPU with high cache. A minimum of 16 GB is required . Windows 7 or windows 10 as the operating system with a processor of i3 and above.

Practice sessions for the DataScience–Artificial Intelligence Course

Our well experienced trainers would mostly work on real time assignments to help students market ready. Online server access will be provided to students who can access it from anywhere and will be given 24*7 customer support in case of any issues.

Certification

The course is designed in such a way that the students by the end of the course are well versed in all concepts that are need to kick start their career.

But before that , the students should each know about their capabilities , that is the reason we conduct online examinations and practical assignments to assess the students.

After successful completion of the test , the students will be provided with a course completion certificate.

DataLabs training certificate is valued by many organizations and will also help students to clear interviews.

Training Highlights

Reviews



Sunil
Trainee

I took part in the DataScience–Artificial Intelligence training program recently and it was conducted very well. The faculty was very good and helped us understanding all the topics nicely.



Sunil
Trainee

The DataScience–Artificial Intelligence training program at DataLabs is simply superb. I underwent the program recently and I’m very satisfied with the high quality delivery.

View all Reviews

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