💻Skills

Some skills and expertise areas:

1.  DFT Calculations: VASP, SIESTA, Quantum Espresso
2.  Molecular Dynamic calculations:  VASP, SIESTA (AIMD), LAMMPS (classical MD)

         Arc_tut

1. Pre-processing software: VESTA, ASE, GDIS, WxDragon, Avogadro, Jmol, etc
2. Post-processing software: Xcrysden, VESTA, Origin Lab, Gnuplot, Xmgrace, etc
3. Programming Languages: FORTRAN, Python, MATLAB, Mathematica
4. Operating System: Linux (Ubuntu, Kali), Window
5. Documentation:  Latex, Microsoft Office Packages (Office365, Excel, Visio, Powerpoint,  Jupyter Notebook, Google Collab, etc.)

 kkkkkkkkkkkkkkkk Practicalsjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj

                   1. Linear Algebra for Machine Learning            

Chapter 1

1.  Introduction to Numpy arrays   
2.  Interactive tool: play with two lines
3.  Linear  Systems as Matrices  
4. Recovery Qz

Chapter 2

1.  Introduction to the Numpy's Linear Algebra (linalg) Sub-library
2. The Gaussian Elimination Programming
3. Interactive tool: play with planes(3D)
4. Recovery Qz

Chapter 3

1. Vector Operations: Scalar Multiplication, Sum and Dot Product of Vectors
2. Interactive Tool: Linear Transformations
3. Recovery Qz
4. Matrix Multiplications
5. Linear Transformation
6. Linear Transformation and Neural Networks

Chapter 4

1. Interpreting Eigenvalues and eigenvectors
2.  PCA in Action
3. Interactive tool: Linear Span

                 2. Calculus for Machine Learning                   

Chapter 1

1. Concepts of Derivative: Interactive tool
2. Common Derivative: Interactive tool
3. Differentiation using Python
4.  Cost Minimization

Chapter 2

1. Max, Min and Saddle point: play with surface plot
2. Gradient Descent: One Variable
3. Gradient Descent: Two Variable
4. Linear Regression Using Numpy vs Scikit-learn vs Gradient Descent

Chapter 3

1. Regression with Perceptron
2. Classification with Perceptron
3. Newton's Method
4. Neural Network with Two layers

    3. Probability and Statistics for Machine Learning    

Chapter 1

1. Four Birthday Problems
2. Interactive Tool: Repeated Experiments
3. Monty Hall Problem
4. Interactive Tool: Relationship between PMF/PDF and CDF
5. Introduction to Pandas
6. Panda for Data Analysis -Rideshare Project

       4. Supervised ML- Regression+ Classification           

Chapter 1

1. Python and the Jupyternote book
2. Model Representation
3. Cost Function 
4. Gradient Descent 

Chapter 2

1. Python, NumPy, and Vectorization
2. Multiple variables Linear Regression
3. Feature Scaling and Learning rate
4. Feature Engineering with Polynomical Regression
5. Sklearn and the Gradient Descent
6. Linear Regression using Scikit-Learn
7. Linear Regression Problem*

Chapter 3

1. Classification
2. Sigmoid Function
3. Decision Boundary
4. Logistic Loss 
5. Cost Function
6. Gradient Descent
7. Scikit Learn
8. Overfitting
9. Regularization
10. Logistic Regression vvi*

                 5. Advanced  Machine Learning                   

Chapter 1

1. Neurons and Layers
2. Simple Neural Network, using TensorFlow
3. Simple Neural Network using Numpy 
4. Neural Networks for Handwritten Digit Recognition, Binary*

Chapter 2

1. ReLU Activation 
2. Softmax Activation Function
3. Multi-class Classification
4. Neural Networks for Handwritten Digit Recognition, Multiclass

Chapter 3

1. Model Evaluation and Selection
2. Diagnosing Bias and Variance

Chapter 4

1. Decision Trees
2. Tree Ensemble 

                 *. Machine Learning Physicists               

Full 24 notebook 

                 6. Unsupervised Learning ML                   

1. KMeans Clustering
2. Anomaly detection
3. Collaborative Filtering Recommender System
4. Deep Learning for Content-based Filtering
5.  Principle Component Analysis