Course Provider
Course Highlight
- 1:1 Personalized Mentorship
- 100% Placement Guarantee
- 3 Months Internship
- Online Learning Management System (LMS)
- Specially Designed for Tech and Non-Tech Background Learners
- Online Learning Management System (LMS)
- 3 Months Internship
- 1:1 Personalized Mentorship
- 100% Placement Guarantee
- 20+ Projects and Case Studies
- Multiple Bootcamps & Hackathons
- Unlimited Doubt Sessions
- Fanatical Support
What will you learn in this course?
- Dataisgood has enabled over 400,000 Data professionals through its live classes and self-paced courses.
- 100% Job Guarantee Program
- Fanatical Support with Student Success Management
- Live Online Classes
- Lifetime Access To Self Paced Learnings (400+ Hrs)
Why should you take this course?
The PG program in Data Science, Machine Learning, and Neural Networks offered by dataisgood provides comprehensive coverage of all the subjects that can be used to successfully pass an interview for a data science position.
The program offers various salient features that can be listed as follows:
- Live Classes from practising Data Science Instructors.
- Experimental Learning
- Comprehensive course Curriculum
- Industry Relevant Projects and Case studies
- Dedicated Student Success management(SSM) Team
- Soft skills and Interview Preparation Training
- Process Monitoring
- Strong Alumni Network
- Data Science Project Portfolio
- Exclusive Hackathons
Who should take this course?
- The PG program in Data Science, Machine Learning, and Neural Networks at dataisgood is especially tailored for both experienced professionals and newcomers.
- This course will provide you all the knowledge you need to get started on your data science journey if you are a working professional wishing to transfer into the data science and artificial intelligence industries.
- For newcomers, the program offers a crucial platform that will help you build your skills and provide you a push to begin your career in data science.
Curriculum
SEMESTER 1:
PY201: Python Fundamentals
- Introduction to Python and Data Science
- Data Structures in Python
- Control Structures and Functions
- Introduction to Object-Oriented
- Programming in Python
- File Handling in Python
- Regular ExpressionsAdvanced-Data Structures in Python
- Functional Programming in Python
SEMESTER 2:
PY202: Python for Data Science Visualization
- NumPY for Data Science
- Basics of Series and DataFrame
- Filtering using Pandas
- Extracting and Aggregating Pandas
- Matplotlib and Seaborn
- Plotly and Cuffinks
PY203: Data Cleaning and Preparation
- Introduction to Data Cleaning and Data Types
- Exploring and Visualization the missing values
- Advanced-Data Cleaning Concepts
- Introduction to Feature Engineering
- Feature Extraction and Transformation
- Feature Selection and Dimensionality Reduction
PY204: Machine Learning (scikit)
- Introduction to Machine Learning
- Understanding the Linear regression
- Different types of Regularization Methods
- Logistic // Binary Regression Modelling
- KNN, SVM and Naive Bayes
- Cluster Analysis with K-Means Algorithm
- Advanced Clustering Techniques
SEMESTER 3:
PY205: Tree Based and Boosting Models
- Decision Trees
- Random Forest
- Introduction to Boosting
- Imbalanced Machine Learning Models
- Factor of Imbalanced Machine Learning Models
- Techniques to Handle Imbalanced Data
- Introduction to Recommendation Engines
PY206: Time Series Analysis and Forecasting
- Introduction to Time Series Analysis
- Preprocessing and Visualization of Time Series Data
- Time Series Forecasting using ARIMA
- Exponential Smoothing Models for Time Series
- Forecasting
- Machine Learning Models for Time Series
- Time Series Forecasting using Prophet
PY207: Natural Language Processing
- Introduction to NLP & Text Preprocessing
- Feature Extraction for NLP
- NLP Classification Deep Learning Models
- RNN, LSTM, Resnet, GRU
- Advance NLP Models Transformer and GPT-3
PY208: Deep Learning Fundamentals
- Introduction of Deep learning
- CNN Models
- Transfer Learning Models
- NLP Deep Learning Models
Electives Program
Select any one elective from list mention below:
Elective 1: Business Intelligence Tool (Power BI / Tableau)
- Data loading and transformation with Power Query.
- Data visualization with charts, including various chart types.
- Tabular visualization with Power Pivot and matrices.
- Creating interactive dashboards.
- Using conditional formatting for enhanced data analysis.
Elective 2: Deep Learning with Computer Vision
- Introduction to Deep Learning and Computer Vision.
- Artificial Neural Network and Convolution Neural Network.
- Loss Functions, Pptimizers, and Activation Functions.
- Transfer Learning.
- Object detection and object localization.
Elective 3: Deep Learning with NLP
- Introduction to NLP & Text Preprocessing
- Text Cleaning and Mining.
- RNN, LSTM, Resnet, GRU.
- Advance NLP Models Transformer and GPT-3
