Instructions
Muhammad Umer Javed specially designs this course Machine learning using python: A way towards Data science for the non-IT students. This course doesn’t have any prerequisite and covers all the basic programming hardels covers in terms of the short exercises and short case studies
Course overview
- Intro to Machine learning?
- Why Machine Learning is using by python?
- How machine can learn?
- Data Representation
- What is data
- Importance of data collection
- How to collect data
- Data samples
- Data distribution
- Mean and variance
- Time series vs categorical
- Features in Machine learning
- Importance of features
- Features matrix
- The dimension of the data set
- Data representation in 1D, 2D, and 3D
- Skewed vs Non-skewed
- Feature Selection and techniques
- Feature transformation
- Machine learning Algorithms
- Types of Machine Learning
- Supervised learning
- Non supervised learning
- Supervised learning
- Logistic regressions
- Linear regression
- SVM kernel methods
- K-neighbour
- Non Supervised Clustering
- Dimension reduction
- K-mean
- Find k using silhouette
- Types of Machine Learning
- Approach to Solve the Problem
- Cross-Validation Techniques
- Data partition
- K-fold
- Random sampling
- Stratified sampling
- Bias Variance Dilemma
- Model Selection
- Model Evaluation
- Confusion matrix
- Accuracy
- Mean accuracy
- Roc
- Demo