| 13-3-2023 |
1.5 hours |
Artificial Intelligence, Introduction to Machine Learning, Its implications with real-world examples, Concept of self-learning |
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1.5 hours |
Types of learning {Supervised learning (Classification, Regression), Unsupervised learning, Reinforcement learning) |
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| 20-3-2023 |
1.5 hours |
Introduction to Datasets, Statistical Analysis of data (Descriptive & Predictive), Data Collection (Numerical data, Categorical data) |
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1.5 hours |
Data Preprocessing and its techniques (Data cleaning, Data integration, Data Argumentation, Data reduction, Data transformation) |
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| 27-3-2023 |
1.5 hours |
Introduction to python and libraries |
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1.5 hours |
Implementation of data preprocessing using python |
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| 3-4-2023 |
1.5 hours |
Regression problem Linear Regression (linear equation, slop of the line, relationship between attributes, intercept, ordinary least square, residual error) |
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1.5 hours |
Implementation of simple linear regression Evaluate the relation between attributes using plotting. |
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| 10-4-2023 |
1.5 hours |
Multiple linear regression (Dummy variable, multicollinearity, dummy variable trap, building a model using backward elimination) |
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1.5 hours |
Implementation of multiple linear regression using stat library in python. |
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| 17-4-2023 |
1.5 hours |
Polynomial Regression and implementation (Degree of polynomial) |
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1.5 hours |
Logistic Regression intuition and real-world example |
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| 24-4-2023 |
1.5 hours |
K-nearest neighbor intuition and solved example using real-world dataset. |
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1.5 hours |
Implementation of K-NN using python |
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| 1-5-2023 |
1 Hour |
Mid Term |
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| 8-5-2023 |
1.5 hours |
Decision Tree (Entropy, information gain) |
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1.5 hours |
Mathematical implementation using dataset on python |
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| 15-5-2023 |
1.5 hours |
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1.5 hours |
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| 22-5-2023 |
1.5 hours |
Supervised Algorithm-Naïve Bayes (Conditional probability, Bayes theorem) Derive theorem mathematically |
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1.5 hours |
Support vector machine (linearly separable data, non-linearly separable data) Linear SVM implementation using example |
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| 29-5-2023 |
1.5 hours |
Kernel function intuition (RBF kernel function, Sigmoid function) Implementation of SVM |
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1.5 hours |
Unsupervised learning, Clustering (k-mean clustering intuition- k mean++, the elbow method implementation using example) |
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| 5-6-2023 |
1.5 hours |
Implementation of K-mean EM Algorithm/DBSCAN intuition |
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1.5 hours |
Ensemble learning (Bagging, Boosting) Bagging(Bootstrap aggregation, row sampling with replacement), Random forest |
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| 12-6-2023 |
1.5 hours |
Bias-Variance trade off, Boosting (Adaboost) |
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1.5 hours |
Stacking Optimization algorithm (Gradient Descent, Stochastic Gradient Descent) |
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| 19-6-2023 |
1.5 hours |
Evaluation Matrices, Reinforcement learning (Tuning model complexity) |
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1.5 hours |
Natural language processing (Bag of words, stemming, lemmatization) |
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| 26-6-2023 |
1.5 hours |
Implementation of NLP using dataset using python |
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1.5 hours |
Neural network (how brain works, perceptron, the neuron) |
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| 3-7-2023 |
2 Hour |
Final Term |
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