#1: Introduction to Machine Learning

1. What is Machine Learning?

2. Why do we need Machine Learning?

3. Which Types of Problems does Machine Learning solve?

4. Types of Data you deal with?

#2: Supervised Learning?

1. What is Supervised Learning?

2. Classification

3. Regression

4. Classification Algorithms

5. Regression Algorithms

6. Model Evaluation Metrics for Classification and Regression

#3: Unsupervised Learning

1. What is Unsupervised Learning?

2. Preprocessing and Scaling Datasets

3. Dimensionality Reduction

4. Feature Extraction

5. Manifold Learning

6. Clustering

7. Clustering Algorithms

#4: Feature Engineering

1. Categorical Features

2. One-hot-encoding

3. Binning and Discretization

4. Interaction and Polynomials

5. Univariate Nonlinear Transformations

6. Linear Models and Trees

7. Feature Selection

#5: Model Evaluation

1. Overfitting and Underfitting

2. Cross-Validation

3. Grid Search

4. Evaluation Metrics

5. Model Selection

6. Hyperparameter Tuning

#6: Working with Text Data

1. Types of Textual Data

2. Analyzing Sentiments

3. Bag of words

4. Stopwords

5. Tf-Idf

6. Tokenization

7. Stemming

8. Lemmatization

9. Topic Modelling

10. Document Clustering

#7: Pipelines

1. Parameter Selection

2. Building Pipelines

3. Using Pipelines in Grid Search