Data Science and Machine Learning Program
Build your career in Data Science and Machine Learning
EMI Starting at
₹ 6,083 / month
Total Program Fee
₹ 36,500
Next Batch Starts: First Week of the Month
Limited no. of seats available
Program Duration: 6 Months
At 10-15 hours/week
Learning Mode
Online
Key Highlights of the Data Science Program
Succeed as a new-age data scientists career with our comprehensive project-based learning program. This program will help you to build solid foundation of data science and machine learning and working on real project at production level, internship-grade projects, hands-on activities etc.
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Online Sessions + Live Lectures
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5 Projects and Assignments
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Personalised Industry Session
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Student Support available all day for your convenience (24*7)
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Software Career Transition Bootcamp for non-tech and new coders
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No Cost EMI Option
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10+ Programming Tools & Languages
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Capstone Project
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Direct access with expert to Clear doubts and asking queries
Who Is This Programme For?
Software Developers, IT Professionals, Engineers, Analysts, Tech Support Professionals, Freshers wanting to kick-start their career in software development
Bachelor’s Degree with 50% or equivalent passing marks. No coding experience required.
Minimum Eligibility
Top Skills You Will Learn
Fundamentals of Data science and Machine Learning, Decision making on data, Data analyst, building and deploying the models.
Job opportunities
Data Scientist, Data Engineer, Data Analyst, Machine Learning Engineer, AI & ML based app developers
Curriculum
The Data Science and Machine Learning Program curriculum has been carefully crafted by Codersarts team to provide you with the skills & knowledge to apply data science techniques to help you make data-driven decisions. Encompass the most business-relevant technologies, such as Machine Learning, Deep Learning, NLP, Recommendation Systems, and more.
Introduction
1 - 3 Weeks
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Environment setup of python
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Setting up machine learning environment (python)
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Jupiter notebook , anaconda , other local environments
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Arithmetic operators in Python: Python
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Strings in Python: Python Basics
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Lists, Tuples and Dictionaries: Python Basics
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Working with Numpy
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Working with pandas
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Working with Matplotlib
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Overview of Sklearn
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Overview of different ML models
Data Exploration and Manipulation
2 - 3 Weeks
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Data collection - Importing Data in Python, Data Exploration, The Dataset and the Data Dictionary
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​​Data preprocessing- Data conversions, Bivarient analysis /variable conversions -non Usable variables
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Outliers
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EDA - Missing value treatment, Dummy value creation and handling, Correlation Analysis, Test train split, Bias Variance trade-off
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Feature engineering - feature extraction, catagorical data encoding, one-hot-encoding
Model selection procedures
1 - 3 Weeks
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Supervised Learning
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Unsupervised Learning
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Reinforcement Learning
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Introduction to overfitting, hyperparameters, cross-validation
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Model Selection Techniques
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Classifications,
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Clustering
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Regression
Model Evaluation
2 - 3 Weeks
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Model Accuracy
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Precision
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Recall
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F1 score
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Jaccard
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AUC
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ROC
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MSE
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MAE
Machine Learning Pipeline
1 - 2 Weeks
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Building project by completing all steps
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Model accuracy improvement
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Basic Hyper tuning
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Model comparison using standard techniques
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Preparing model for deployment
Projects and Hands-on
2 - 4 Weeks
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Classification on iris dataset
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Disease prediction (diabetes dataset)
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Image classification on handwritten datasets
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Recommendation system
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Image classification
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Text classification
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Sentiment analyzer
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Facial image classification
Technologies you will learn
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Numpy
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Pandas
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SciPy
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Matplotlib
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Scikit-learn
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EDA
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Feature engineering
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Machine Learning
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Data Science
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Python Or R
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Hyper tuning
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model for deployment
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Jupiter notebook, Google Colab, AWS Sagemaker
Other Related courses
Coming Soon
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Computer Vision
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Deep Learning
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NLP
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Classification analysis
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Regression analysis
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Clustering analysis
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Classification analysis
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Feature engineering in Python