Module 1
Working with NumPy pandas and matplotlip

How to read the documentation and understand packages

Module 2
Data Wrangling: Data Collection

Collecting data from multiple sources such as CSV, JSON and APIs

  How to use Kaggle as a data scientist and work oncompetitions

Module 3
Data Wrangling: Data
Exploration and Cleaning

Data exploration, cleaning and visualisation. Feature Engineering

Data Wrangling on Kaggle competition

• Titanic: Machine Learning from Disaster
•  House Prices: Advanced Regression Techniques

Module 4
Machine learning. Scikitlearn, Hyperparameter Tuning

Intro to Machine Learning, Decision Trees, Random  Forests, SVM, Ensembles: Bagging vs Boosting. Clustering for Unsupervised Learning K-Means on Two-Dimensional Data, K-Means on n-Dimensional Data

Applying machine learning model on the above projects and submitting the solution on Kaggle

Module 5
Deep Learning

what is a neural network Implementing with ANN using Keras

   working on MNIST dataset and submit the solution on Kaggle https://www.kaggle.com/c/digit-re cognizer

Module 6
Model Evaluation 

Learn how to evaluate and compare different model and what are different accuracy matrices