Artificial Intelligence and
Data Science Specialisation

Learn Artificial Intelligence and Data Science through our specialised program.  Acquire practical skills and theoretical insights essential for success in this dynamic and high-demand industry.

No Prior Programming Experience Required

Real world, industry-graded projects

Certificate of completion

Placement Assistance

Guest lectures from industry professionals

Learn by doing practical

About the course

Embark on a journey with our AI and Data Science Specialization course, a comprehensive online learning experience designed to equip learners with vital skills in artificial intelligence (AI)machine learning, and data science. This course offers hands-on experience with industry-standard tools and techniques, ensuring readiness to tackle real-world data challenges effectively. Whether you’re a novice looking to start a career in AI or a seasoned professional seeking to enhance your skills, this course offers the essential knowledge to excel in the dynamic fields of AI and data science.

Syllabus

Tenure: 10 months

  • Introduction to the Jupyter Notebook environment. Basics Jupyter notebook Commands.
  • Syntax, variables, and Data types.
  • Data structures in python – List, Tuple, Set, Dictionary
  • Conditional statements – if, Else, Elif.
  • Loops – While & For Loops.
  • Functions – lambda, recursions, map, filter & reduce functions.
  • User-Defined Functions
  • Errors and Exceptions- Try and Except
  • List and Dictionary Comprehension
  • Introduction to DBMS & RDBMS
  • OLAP vs OLTP
  • Database Design
  • Database creation in MYSQL Workbench
  • Single Table Queries – SELECT, WHERE, ORDER BY, Distinct, And, OR.
  • Aggregation functions- Group by, max, min, sum,avg, etc.
  • Multiple Table Queries: INNER, SELF, CROSS, and OUTER, Join, Left Join, Right Join, Full Join, Union, Union All.
  • Subquery
  • Analytics Function- Partition by, rank, dense rank, row number, lag, lead, etc.
  • Pandas
  • Numpy
  • Data Cleaning – null and infinite values, outliers, capping, sanity
    checks, data formatting, etc.
  • Types of Variables – Categorical & Continuous
  • Visualization Libraries: Seaborn and Matplotlib
  • Exploratory Data Analysis: Univariate, Bivariate, and Multivariate analysis.
  • Introduction to Statistics
  • Basics of Probability
  • Discrete Probability Distribution
  • Continuous Probability Distribution
  • Normal Distribution
  • Poisson’s Distribution
  • Bayes’ Theorem
  • Central Limit Theorem
  • Pearson Co-Relation, Co-Variance
  • Creating confidence interval for the population parameter
  • Characteristics of Z-distribution and T Distribution.
  • Concepts of Hypothesis Testing: Null and Alternate Hypothesis
  • Making a Decision and Critical Value Method
  • p-Value Method and Types of Errors
  • Two-Sample T-Test
  • Two sample Z-test
  • ANOVA, Chi-Square, A/B Testing

Supervised Learning – Regression

  • Simple Linear Regression
  • Multiple Linear Regression
  • Ridge & Lasso Regression (L1 & L2)

Supervised Learning – Classification

  • Logistic Regression
  • KNN Algorithm
  • Naive Bayes
  • Decision Trees
  • Random Forest
  • Support Vector Machines

Supervised Learning – Ensemble

  • Bagging and Boosting
  • Xgboost Algorithm
  • Adaboost Algorithm
  • VotingClassifier
  • Gradient Boosting Algorithm

Unsupervised Learning

  • Introduction to Clustering
  • K-Means Clustering: Linkage, Use of Elbow Curve & Silhouette
    Score
  • Hierarchical Clustering
  • Principal Component Analysis (PCA
  • Introduction to Deep Learning and Neural Networks
  • Introduction to Linear Algebra
  • TensorFlow & Keras for Neural Networks
  • Artificial neural Networks (ANN)
  • Image Classification using Convolutional
  • Neural Networks (CNN)
  • Transfer Learning (LeNet 5, Alex Net, VGG 19 & 16, ResNet, Inception V3)
  • Recurrent neural networks – Natural Language processing
  • LSTMs – Long short-term memory
  • GRUs – Gated Recurrent Unit
  • LSTMs Applications- Language Modeling

Machine Learning model deployment with Industry level architecture

  • Data Transformation
  • Data Analysis Expressions (DAX)
  • Data Visualisation
  • Connectivity modes
  • Power BI report server
  • Industry relevant projects
  • Mentorship by industry experts
  • Resume building
  • Mock interviews

Meet our team

Photo of a faculty member

Pranav Malik

SDE-2 - Microsoft

Photo of a faculty member.

Tarun Sachdeva

Senior Data Analyst - AMEX

Photo of a faculty member

Sawan Agarwal

Data Scientist - AdGlobal360

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Frequently asked questions

A question mark

This course is suitable for candidates pursuing graduation in any discipline or working professional. we will cover topics from beginner level to advance.

Fee structure can be divided into instalments. Moreover, we have loan partners which can help you in availing loan.

Yes, candidate have to use their own laptop.

There will be multiple projects for each learning module, although capstone project is the main project at the end of the course.

Yes, certificate will be provided by KRPro Learning PVT LTD.

Candidates are evaluated in the course through a series of tests and quizzes.

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