Artificial Intelligence (AI )
Course Description
This Artificial Intelligence (AI) course offers a deep dive into AI fundamentals and advanced techniques. Learn to build intelligent systems using machine learning, neural networks, and deep learning. Gain hands-on experience with real-world AI applications, including natural language processing and computer vision.
Whether you're a beginner or looking to enhance your skills, this course will provide you with the tools to excel in AI development.
What You’ll Learn From This Course
- Understand the core principles of Artificial Intelligence
- Master machine learning algorithms and techniques
- Explore deep learning concepts and architectures
- Learn natural language processing and computer vision applications
- Gain hands-on experience with real-world AI projects
- Work with neural networks and reinforcement learning
Certification
Upon successful completion of the Artificial Intelligence (AI) course, you will receive a professional certification that validates your skills in AI technologies. This certification demonstrates your expertise in machine learning, deep learning, natural language processing, computer vision, neural networks, and reinforcement learning. It shows that you are proficient in applying AI techniques to real-world problems and building intelligent applications. This certificate will enhance your resume, boost your career prospects, and open up job opportunities in industries that are adopting AI solutions. It is a valuable credential for anyone seeking to advance their career in the AI field.
About Master Data Science
Course Content
The Master Data Science course offers a comprehensive curriculum that covers essential topics in data science, including Python programming, data structures, machine learning, deep learning, and data visualization. Students will gain hands-on experience with tools like Django, TensorFlow, Keras, and PyTorch. The course also focuses on advanced techniques like natural language processing, time series analysis, and model evaluation, preparing graduates for a successful career in data science and machine learning.
1. Introduction to Data Science and Python
Course Content
- Case study based discussion and problem solving thought process initiation
- Different Analytical Domain
- Industry mentors guest talk
2.Python
Course Content
- Python, Anaconda and relevant packages installations
- Structure of python program (comments, indentations)
- Operators
- Data type, variable
- User input, string methods
3. Data Structure
Course Content
- Mutable / Immutable
- List
- Tuple
- Sets
- Dictionary
- List, sets and dictionary comprehension
4. Loop & Control Statements
Course Content
- If & Else and Nested if else
- While and For loop
- Break, Continue and Pass
- Keywords in python
- Pattern making
5. Functions
Course Content
- Basic functions
- Built-in
- User-defined functions
- *args, **kwargs
6. Advanced Functions
Course Content
- Maps, filter, reduce
- Iterators and iterables
- Closures / decorators
- Generators
- File Handling and Exception Handling
7. Object Oriented Programming
Course Content
- Class & Object
- Data abstraction, encapsulation, Inheritance, Dunder methods
- Customized modules
8. Django
Course Content
- Starting your First Web Application
- Developing Standard Web Template
- Django Admin
- Models
- Views and URLconfs
- Forms
9. Numpy
Course Content
- Indexing/slicing
- Appending /Inserting on axis
- Mathematical and statistical operations
- Sort/Condition
- Transpose Operations
- Joining/splitting
10. Pandas
Course Content
- Data Extraction
- Series Dataframe and Plane
- Indexing and Slicing
- Conditions/Grouping/Imputations
- Append/concat/merge/join
- Date time functionalities and resampling
- Excel functions
11. Data Visualization with Matplotlib and Seaborn
Course Content
- Customization of matplotlib and seaborn
- Scatterplots/barplot/histogram/density plots
- Box Plot and outlier detection
- Visualization Linear relationship
- Univariate, Bivariate and Multivariate analysis
12. Database
Course Content
- MySQL
13. Statistics
Course Content
- Linear algebra
- Matrix operation and properties
- Introduction to calculus
- Theory of optimization
- Probability
- Conditional Probability
- Dependent and Independent events
- Bayes Theorem
- Descriptive / Inferential
- Variance / standard deviation
- Covariance And correlation
- Central Limit theorem
- Types of distributions
- pdf, cdf , pmf
- Confidence Intervals
- Hypothesis testing
- Z Test, t test, chi-2 test
- F-test/Anova
14. Introduction to Machine Learning
Course Content
- Introduction to Artificial Intelligence (DS, ML & DL)
- Applications of Machine Learning
- Categorization of Machine Learning
- Supervised / Unsupervised / Semi Supervised
- Parametric vs Non-Parametric
15. Supervised Learning (Regression/ Classification)
Course Content
- Linear Regression
- Polynomial Regression
- Lasso Regression
- Ridge Regression
- Stepwise Regression
- Bayesian Regression
16. Classification
Course Content
- Logistic Regression
- KNN
- SVM (Support Vector Machines)
- Decision Tree
- Naive Bayes
- LDA
- Classification for Imbalanced Dataset
17. Ensemble Learning
Course Content
- Random forest
- Adaboost
- Gradient boosting
- Decision Tree
- Xgboost
8. Other Support
Support Content
- Resume building
- Mock interviews
- LinkedIn account creation
- GitHub repository creation
- Migration of projects to GitHub
- Duration5 Months
- Enrolled70
- Lectures50
- Skill LevelEasy
- LanguageEnglish/Hindi
- Quizzes8
- CertificateYes
Pass Percentage85%
Reviews
Arun Panwar
This course was a game-changer for me! The hands-on projects and clear guidance made learning data science exciting and easy to understand. I feel confident tackling real-world problems now.
Akash singh
I loved how practical and engaging this course was! It took me from zero to mastering key data science skills like Python, machine learning, and even deep learning. Highly recommend it