Python for Machine Learning and  Data Science Training 

Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

30+ course

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• a powerful N-dimensional array object
  • sophisticated (broadcasting) functions
  • tools for integrating C/C++ and Fortran code
  • useful linear algebra, Fourier transform, and random number capabilities

• 7 – 10 Lectures

• Weekend option available

• Price : 4000 Rs.

Scipy Topics

• high-level scientific computing

• File input/output: scipy.io

• Special functions: scipy.special

• Linear algebra operations: scipy.linalg

• Interpolation: scipy.interpolate

• Optimization and fit: scipy.optimize

• Statistics and random numbers: scipy.stats

• Numerical integration: scipy.integrate

•  Fast Fourier transforms: scipy.fftpack

• Signal processing: scipy.signal

• Image manipulation: scipy.ndimage

Matplotlib – Data Visualization (R, ggplot) Topics

• Understanding plt.subplots()

• Basic plots

• Advance plotting

• Visualizing arrays with matplotlib

• Plotting with the pandas + matplotlib combination

sklearn Topics

• Supervised learning algorithms

• Unsupervised learning algorihms

• Model selection and evaluation

• Dataset transformations

• Dataset loading utilities

• Strategies to scale computationally: bigger data

Pandas

• 10 – 15 Lecture

• Weekend option available

• Price : 6000 Rs.

Pandas Topics

• Arithmetic Operations

• Grouping by a Categorical

• Resample

• Upsampling

• Downsampling

• Rolling and Expanding

• Plotting

Machine Learning for Beginners

• 20 – 25 Lectures

• 2 hours per day

• Weekend option available

• Price : 25000 Rs.

Topics we cover

• Environment setup of python

• Setting up machine learning environment (python)

• Jupiter notebook , anaconda , other local environments

• Arithmetic operators in Python: Python

• Strings in Python: Python Basics

• Lists, Tuples and Dictionaries: Python Basics

• Working with Numpy

• Working with pandas

• Working with Matplotlib

• Overview of Sklearn

• Overview of different ML models

Data collection

• Importing Data in Python     

• Data Exploration

• The Dataset and the Data Dictionary

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Data preprocessing

• Data conversions

• Bivarient analysis /variable conversions  -non Usable variables 

• Outliers

• -EDA
  -Missing value treatment
  -Dummy value creation
  -handling
  -Correlation Analysis
  -Test train split
  -Bias Variance trade-off

• Feature engineering
  -feature extraction
  -catagorical data encoding
  -onehotencoding

Model selection procedures

• Regression

• Decision tree(intro with implementation)

• Bayesian(intro with implementation

• SVM(intro with implementation)

Bonus projects

• Classification on iris dataset

• Disease prediction (diabetes dataset)

• Image classification on hand written datasets