Machine Learning

1. Introduction to Machine Learning

2. Python

• Introduction to Python
• Basic data structures in Python
• Slicing & dicing of data structures using Python
• Loops, if-elif-else conditions
• User-defined functions & lambda functions
• Map, applymap & apply
• NumPy arrays
• Pandas Series & DataFrame
• Visualization techniques for data analysis

3. Statistics

• Descriptive Statistics
• Inferential Statistics

4. Statistical Modeling

Linear Regression

• Assumptions
• Evaluation of Linear Models
• Data preparation for Linear Regression
• Model fitting and prediction

Logistic Regression

• Assumptions
• Confusion Matrix
• Evaluation Metrics:
  
  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • AUC-ROC Curve

5. Overfitting vs Underfitting

• Handling Overfitting
• Regularization

6. Machine Learning Models

• Tree-Based Algorithms
• Decision Tree
• Random Forest
• Gradient Boosting Classifier
• XGBoost
• LightGBM
• KNN
• SVM

7. Project

• Minimum 3 hours

8. Unsupervised Machine Learning Algorithms

Natural Language Processing (NLP)

1. Introduction to NLP
2. SpaCy & NLTK Packages
3. Tokenization, Stemming & Lemmatization using SpaCy & NLTK
4. Phrase Matching using SpaCy
5. Text Classification
6. Sentiment Analysis
7. Topic Modeling

Notes

• Interview questions will be discussed during the sessions.
• Recorded videos will be provided daily.
• Two free doubt-clearing classes are included.