Data Science &Machine Learning with Python

Course details

Through this Machine Learning course, you will learn how to process, clean, visualize and analyse data by using Python, one of the most popular machine learning tools.

• After a broad overview of the discipline's most common techniques and applications, you'll gain more insight into the assessment and training of different machine learning models.
• The rest of the course is dedicated to a first reconnaissance with three of the most basic machine learning tasks: classification, regression and clustering.

Introduction to Data Science

• What is data science and why is it so important?
• Applications of data science
• Various data science tools
• Data Science project methodology
• Tool of choice-Python: what & why?
• Case study

Introduction to Python

• Installation of Python framework and packages: Anaconda & pip
• Writing/Running python programs using Spyder Command Prompt
• Working with Jupyter notebooks
• Creating Python variables
• Numeric , string and logical operations
• Data containers : Lists , Dictionaries, Tuples & sets
• Practice assignment

Iterative Operations & Functions in Python

• Writing for loops in Python
• While loops and conditional blocks
• List/Dictionary comprehensions with loops
• Writing your own functions in Python
• Writing your own classes and functions
• Practice assignment

Data summary & visualization in Python

• Need for data summary & visualization
•  Summarizing numeric data in pandas
• Summarizing categorical data
• Group wise summary of mixed data
• Basics of visualization with ggplot & Sea born
• Inferential visualization with Sea born
• Visual summary of different data combinations
• Practice assignment

Data Handling in Python using NumPy & Pandas

•  Introduction to NumPy arrays, functions & properties
• Introduction to Pandas & data frames
• Importing and exporting external data in Python
• Feature engineering using Python

Machine Learning in Python Machine Learning Basics

• Converting business problems to data problems
• Understanding supervised and unsupervised learning with examples
• Understanding biases associated with any machine learning algorithm
• Ways of reducing bias and increasing generalization capabilities
• Drivers of machine learning algorithms
• Cost functions
• Brief introduction to gradient descent
• Importance of model validation
• Methods of model validation
• Cross validation & average error

Generalized Linear Models in Python

• Linear Regression
• Regularization of Generalized Linear Models
• Ridge and Lasso Regression
• Logistic Regression
• Methods of threshold determination and performance measures for classification score models
• Case Study

Tree Models using Python

•  Introduction to decision trees
• Tuning tree size with cross validation
• Introduction to bagging algorithm
• Random Forests
• Grid search and randomized grid search Extra Trees (Extremely Randomized Trees)
• Partial dependence plots
• Case Study & Assignment

Support Vector Machines (SVM) & kNN in Python

• Introduction to idea of observation based learning
• Distances and similarities
•  k Nearest Neighbors (kNN) for classification
• Brief mathematical background on SVM/li>
• Regression with kNN & SVM
• Case Study

Unsupervised learning in Python

• Need for dimensionality reduction
• Principal Component Analysis (PCA)
• Difference between PCAs and Latent Factors
• Factor Analysis
• Hierarchical, K-means & DBSCAN Clustering
• Case study

Artificial Neural Networks in Python

• Introduction to Neural Networks
• Single layer neural network
• Multiple layer Neural network
• Back propagation Algorithm
• Neural Networks Implementation in Python
• Case study

Updated on 20 July, 2020