Module- 4 1
Introduction to Machine Learning
To know the types of machine learning algorithms you need to learn about the life cycle of every step involved in the project. The CRISP-DM process is generally applied in data analytics and Artificial intelligence projects. Start learning about CRISP-DM in deep and the stages involved in the project cycle. You will also be learning different types of data like data cleansing, data collection, data preparation, EDA, Data mining, Feature Engineering, and various Error functions. Also, understand what imbalanced data handling techniques and algorithms are!
- Introduction to Machine Learning
- Machine Learning and its types – Supervised Learning, Unsupervised Learning, Reinforcement Learning, Semi-supervised Learning, Active Learning, Transfer Learning, Structured Prediction.
- Data Types namely Continuous, Discrete, Categorical, Count, Qualitative, Quantitative and its identification.
- Data Cleaning / Preparation – Outlier Analysis, Missing Values Imputation Techniques, Transformations, Normalization / Standardization.
- Measures of Central Tendency & Dispersion
- Mean/Average, Median, Mode
- Variance, Standard Deviation, Range
- Various Graphical Techniques to Understand Data
- Bar Plot
- Scatter Plot
- Feature Engineering – Feature Extraction & Feature Selection
- Error Functions – Y is Continuous – Mean Error, Mean Absolute Deviation, Mean Squared Error, Mean Percentage Error, Root Mean Squared Error, Mean Absolute Percentage Error
- Error Functions – Y is Discrete – Cross Table, Confusion Matrix, Binary Cross Entropy & Categorical cross Entropy.