Machine Learning with Python

1.Statistical Learning

Learning objectives:

• In this module, you will learn the basics of statistics including:
• Basics of statistics like mean (expected value), median and mode
• Distribution of data in terms of variance, standard deviation, and interquartile
• range; and explore data and measures and simple m graphics analyses
• Basics of probability via daily life examples
• Marginal probability and its importance with respect to Machine Learning
• Bayes’ theorem and conditional probability including alternate and null
• hypotheses

Topics

• Statistical Analysis Concepts
• Descriptive Statistics
• Introduction to Probability
• Bayes’ Theorem
• Probability Distributions
• Hypothesis Testing and Scores

2.Python for Machine Learning

Learning objectives

• In the Python for Machine Learning module, you will learn how to work with data using
• Python:
• How to define variables, sets, and conditional statements
• The purpose of functions and how to operate on files to read and write data in Python
• Understand how to use Pandas – a must have package for anyone attempting data analysis with Python
• Data Visualization using Python libraries like matplotlib, seaborn and gplot

Topics

• Python Overview
• Pandas for pre-Processing and Exploratory Data Analysis
• NumPy for Statistical Analysis
• Matplotlib and Seaborn for Data Visualization
• Scikit Learn

3.Introduction to Machine Learning

Learning objectives

• Get introduced to Machine Learning via real-life examples and the multiple ways in which it affects our society. You will learn:
• Various algorithms and models like Classification, Regression, and Clustering.
• Supervised vs Unsupervised Learning
• How Statistical Modelling relates to Machine Learning

Topics

• Machine Learning Modelling Flow
• How to treat Data in ML
• Types of Machine Learning
• Performance Measures
• Bias-Variance Trade-Off
• Overfitting and Underfitting

4.Optimisation

Learning objectives

• Gain an understanding of various optimisation techniques such as:
• Batch Gradient Descent
• Stochastic Gradient Descent
• ADAM
• RMSProp

Topics

• Maxima and Minima
• Cost Function
• Learning Rate
• Optimization Techniques

5.Supervised Learning

Learning objectives

In this module you will learn about Linear and Logistic Regression with Stochastic Gradient Descent via real-life case studies

• Hyper-parameters tuning like learning rate, epochs, momentum, and class-
• balance
• The concepts of Linear and Logistic Regression with real-life case studies
• How KNN can be used for a classification problem with a real-life case study
• on KNN Classification
• About Naive Bayesian Classifiers through another case study
• How Support Vector Machines can be used for a classification problem

Topics

• Linear Regression Case Study
• Logistic Regression Case Study
• KNN Classification Case Study
• Naive Bayesian classifiers Case Study
• SVM – Support Vector Machines Case Study

6.Unsupervised Learning

Learning objectives

Learn about unsupervised learning techniques:

• K-means Clustering
• Hierarchical Clustering

Topics

• Clustering approaches
• K Means clustering
• Hierarchical clustering

7.Ensemble Techniques

Learning objectives

Learn the ensemble techniques which enable you to build machine learning models including:

• Decision Trees for regression and classification problems through a real-life case study
• Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID
• Basic ensemble techniques like averaging, weighted averaging and max voting
• You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting
• Random Forest, with a real-life case study, and how it helps avoid overfitting compared to decision trees
• The Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis
• The comprehensive techniques used to find the optimum number of components/factors using scree plot, one-eigenvalue criterion
• PCA/Factor Analysis via a case study

Topics

• Decision Trees with a Case Study
• Introduction to Ensemble Learning
• Different Ensemble Learning Techniques
• Bagging
• Boosting
• Random Forests
• Case Study
• PCA (Principal Component Analysis)
• PCA
• Its Applications

8.Recommendation Systems

Learning objectives

• Learn to build recommendation systems. You will learn about:
• Association Rules
• Apriori Algorithm to find out strong associations using key metrics like Support,
• Confidence and Lift
• UBCF and IBCF including how they are used in Recommender Engines

Topics

• Introduction to Recommendation Systems
• Types of Recommendation Techniques
• Collaborative Filtering
• Content-based Filtering
• Hybrid RS
• Performance measurement
• Case Study