Course Content
Advanced Python: (Week 1 & 2)
Python Fundamentals
Pandas
Numpy
Matplotlib
Sklearn
Model Metrics & Evaluation: (Week 3)
Mean Absolute Error, Mean Squared Error, Root Mean
Squared Error
Confusion Matrix, Precision, Recall and F1 Score
Bias Variance Trade off
Statistics and Probability: (Week 4 & 5)
Mean, Median, Mode
Variability and Spread
Probability Distributions
Z-Score
Confidence Interval, Hypothesis Testing
T-test, Avona, Chi-Square Test, Correlation and Covariance
Machine Learning Algorithms
Supervised Algorithms: (Week 6,7,8)
Linear Regression
Logistic Regression
Decision Tree
Random Forest
Gradient Boosting Trees
XGboost
Supervised Algorithms Project: (Week 9)
Unsupervised Algorithms: (Week 10)
K-means
Hierarchical cluster
Association rule
PCA
Time series Algorithms: (Week 11)
ARIMA
Seasonal ARIMA
Recommendation Algorithms: (Week 12)
Collaborative Filtering
SVD
SQL: (Week 13,14,15)
SQL Commands:
Selecting & Retrieving Data
Filtering, Sorting and Calculating Data
Aggregate and Group by
Subqueries and Joins
Modifying Data
Deep Learning Algorithms: (Week 13,14,15)
Artificial Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Transformers
Note: Students have the option to select either SQL or Deep Learning as an elective topic.
Capstone Project & Industry Applications: (Week 16)
Capstone Project Discussion
Real world Industry Applications
Career Guidance: (Week 17)
Resume Evaluation
1:1 Doubt Clearance
Self-Learning Plan
Mock Interviews
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Contacts
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