Description
Price: 29.00 USD | Size: 68.9 GB | Duration : 200+ Hours | 133 Video Lessons
BRAND: Expert TRAINING | ENGLISH | INSTANT DOWNLOAD | 
4.9
Data Science Mentorship Program
Requirements
- A laptop and an internet connection to attend live classes.
- Basic Mathematical knowledge(not much).
- A zeal to learn complex topics.
About the course
Data Science Mentorship Program is a 7-month long mentorship program designed to make you an industry-ready data scientist. The core philosophy behind the design of the program is its hands-on approach. Each module under the program consists of a plethora of assignments and projects. During the course, you would be exposed to industry-relevant curriculum, tools and libraries. We would also be doing a bunch of real-life case studies to make sure you learn the concepts practically. Along with the course, we would also be focusing on interview preparation. To go with this, we will also be providing doubt support to the paid members of the course, by conducting doubt clearance classes. Apart from academics, we would also focus on portfolio building and career counselling. We strive hard to impart quality education at an affordable price and we believe we can help you in your journey to becoming a kickass data scientist. Hope to meet you in the program. Happy Learning!
What You Will Learn
Python and SQL
Data Science
Data Visualization
Feature Engineering
Model Deployment and monitoring
Machine Learning Algorithms
Working on Real-life projects
Exploratory Data Analysis
Course Curriculum
Python for Data Science
- Fundamentals of Python
- Numpy
- Pandas
Data Visualization
- Basic Statistics
- Matplotlib
- Seaborn
SQL for Data Science
- Database Basics
- SQL Basics
- Advanced SQL
Data Analysis Process
- Data Acquisition
- Data Cleaning
- Data Wrangling & EDA
Basic Machine Learning
- Machine Learning Theory
- ML Metrics
- Small End to End project
Mathematics of Data Science
- Advanced Statistics
- Linear Algebra
- Probability & Calculus
Machine Learning Algorithms- 1
- Linear & Logistics Regression
- SVM & KNN
- Naive bayes & Bagging
- Decision Tree & Adaboost
- Random Forest
Machine Learning Algorithms- 2
- Gradient Boosting & XgBoost
- PCA & K-Means
- Hierarchical Clustering
- DBSCAN
- T-sne
Practical Machine Learning – 1
- Bias Variance Trade-off
- Regularization
- Cross Validation
- Working with Missing Data
- Feature Scaling & Encoding
Practical Machine Learning – 2
- Feature Transformation
- Pipelines
- Date and Time
- Feature Construction
- Feature Selection
Practical Machine Learning – 3
- Model Tuning
- Imbalanced Datasets
- Multicollinearity
- Data Leakage
- Working with large dataset
Model Productionalization & Deployment
End-to-End Case Studies
+ Many More
Who is this course for
01.
Industry professionals targeting to enter Data Science
02.
Students trying to make a career in Data Science
03.
High School students who are passionate about Learning Data Science
04.
Individuals with career gaps looking for a pivot in their career
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