Fundamentals of Machine Learning

Details of the course ↓

Unit No.

CONTENT

1

Unit 1 ⇗:

  • Review of Statistical Concepts: Mean, Median, Mode, Outliers, Range, Average Deviation, Absolute Deviation, Squared Deviation, Standard Deviation, Probability theory.
  • Review of Linear Algebra:Vectors and Matrices, Matrix operations, Properties, Inverse and Transpose.
  • Introduction to Machine Learning: What is Machine Learning, Introduction to ML's three approaches: Supervised, Unsupervised and Reinforcement Learning?

2

Unit 2 ⇗:

  • Introduction to Python: • Data types and variables, Operators and operator precedence • Data type conversions, Command line argument, Data input, Comments, Import modules, Control statements.
  • Functions and modules in python Python built in functions, Python Modules, File Handling.

3

Unsupervised Learning Algorithms ⇗:

  • UnSupervised Learning Algorithms Clustering: K-means, Silhoutte Scores, Hierarchical Clustering, Fuzzy c- means, DBScan Dimensionality Reduction: Low Variance Filter, High Correlation Filter, Backward Feature Elimination, Forward Feature Selection, Principle Component Analysis, Projection Methods.

4

Data Analysis and ML with Python ⇗:

  • Data Analysis and Machine Learning with Python NumPy, SciPy Matplotlib, Pandas Scikit-Learn. NumPy Basics. A Multidimensional Array Object (ndarrays ) Creating ndarrays Data Types for ndarrays Basic Indexing and Slicing, Getting Started with pandas Series, Data Frame and Index Objects Re indexing Indexing, Selection and Filtering Sorting and Ranking, Loading from CSV and other structured text formats, Normalizing data, Dealing with missing data, Data manipulation (alignment, aggregation, and summarization), Group-based operations: split-apply-combine Statistical analysis, Date and time series analysis with Pandas, Visualizing data

5

Validation Techniques & Supervised Learning Algorithms ⇗:

  • Validation Techniques: Hold out, K-Fold Cross Validation, Leave one out, Bootstrapping. Supervised Learning Algorithms: Linear Regression, Logistic Regression, Decision Trees, Support Vector Machine, K-Nearest Neighbours, CN2 Algorithm, Naive Bayes, Artificial Neural Networks.