About the course
This course provides a concise introduction to the fundamental concepts in machine learning and popular machine learning algorithms. The course is accompanied by handson problemsolving exercises in Python.
This course covers the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, knearest neighbor, an introduction to Bayesian learning and the naïve Bayes algorithm, support vector machines and kernels, and neural networks with an introduction to Deep Learning. It also covers the basic clustering algorithms. Feature reduction methods are also discussed. This course also introduces the basics of computational learning theory. Also, this course covers various issues related to the application of machine learning algorithms. This course also covers hypothesis space, overfitting, bias and variance, tradeoffs between representational power and learnability, evaluation strategies, and crossvalidation.
Learning Outcomes
After completing this course, you will be able to:
 Understand the fundamental concepts in machine learning and popular machine learning algorithms.
 Understand the fundamental issues and challenges of machine learning such as data, model selection, model complexity, etc.
 Understand the strengths and weaknesses of many popular machine learning approaches.
 Understand the underlying mathematical relationships within and across Machine Learning algorithms and the paradigms of supervised and unsupervised learning.
 Design and implement various machine learning algorithms in a range of realworld applications.
 Boost your hireability through innovative and independent learning.
Target Audience
The course can be taken by:
Students: All students who are pursuing professional graduate/postgraduate courses related to computer science and engineering or data science.
Teachers/Faculties: All computer science and engineering teachers/faculties.
Professionals: All working professionals from the computer science / IT / Data Science domain.
Why learn Machine Learning?
Machine Learning lays the foundation for Artificial Intelligence. Artificial Intelligence (AI) is indeed moving tremendously. Selfdriving cars are AI applications, also, Siri on your iPhone as well as Youtube’s video recommendations are AI applications. Machine Learning is the rave of the moment. Tons of companies are going all out to hire competent engineers, as ML is gradually becoming the brain behind business intelligence. Just as humans learn from experience, ML systems learn from data. So, learning ML would make you more knowledgeable in data science, and thus more attractive in the labor market. Also, there’s a potentially positive demand for ML engineers. So, it’s worth learning to have a go at the Machine learning course if you want to be a highly demanded ML professional.
Course Features
 24X7 Access: You can view lectures at your own convenience.
 Online lectures: 22 hours of online lectures with highquality videos.
 Updated Quality content: Content is latest and gets updated regularly to meet the current industry demands.
Test & Evaluation
1. During the program, the participants will have to take all the assignments given to them for better learning.
2. At the end of the program, a final assessment will be conducted.
Certification
1. All successful participants will be provided with a certificate of completion.
2. Students who do not complete the course / leave it midway will not be awarded any certificate.
Topics to be covered
 Introduction
 What is the history of Machine Learning?
 What is the difference between Machine Learning solution and programmatic solution?
 What is a formal definition of Machine Learning?
This is only a demo course. The full course can be bought here
 What are some domains and examples of Machine Learning?
 How can we create a (machine) learner?
 Different types of Machine Learning
 What are the broad types of Machine Learning?
 What is Unsupervised / Supervised / Semisupervised and Reinforcement learning?
 What is supervised learning? (In detail)
 What are some examples of Classification and Regression problems?
 What are Features, Some of the Sample training examples of feature and Can we draw some Schematic Diagrams (for Supervised learning)?
 What is Classification Learning? and what are some of its tasks and performance metric?
 How do we get data for the learning problems? How are representations of functions used in Machine Learning? What is the hypothesis space?
 Hypothesis Space and Inductive Bias
 What is inductive learning?
 What are the features and feature vectors?
 What is the start of the classification problem? What are feature space and hypothesis space for Classification problems?
 5 types of representations of a function
 Hypothesis space
 Terminology (example, training data, instance space, concept, target function)
 What is the size of the hypothesis space (for n boolean features) and what is hypothesis language?
 What is inductive learning hypothesis?
 What are inductive learning and consistent hypothesis? Why is inductive learning an illposed problem?
 What are various types of bias? (Occam's Razor, MDL, MM) and what are the important issues in Machine Learning? What is generalization? (Bias and Variance)
 Evaluation and CrossValidation
 What is an experimental evaluation of learning algorithms?
 How do we Evaluate predictions? and What is an absolute error? (Evaluate predictions)
 What is the sum of squares error and number of misclassification? (Evaluate predictions)
 What is the confusion matrix?
 What is accuracy, precision, and recall? (evaluate predictions)
 What is sample error and true error?
 What are the sources of errors?
 What are the difficulties in evaluating the hypothesis with limited data and possible solutions?
 How can we evaluate with limited training data?
 What is K fold cross validation tradeoff in Machine Learning?
 Tutorial I
 Introduction to Tutorial I
 Types of learning: supervised vs unsupervised learning
 Example of supervised vs unsupervised learning
 Types of features: categorical vs continuous features
 Types of supervised learning: regression vs classification
 Bias vs Variance
 Generalization performance of a learning algorithm
 Linear Regression
 What is regression? (Linear functions and other functions) and What are various Types of regression models?
 What is the linear regression?
 Looking at an example of a training set for regression
 What is multiple linear regression?
 What assumption are we making for errors?
 The least squares regression line
 How do we learn the parameters (for single regression and for multiple linear regression)
 What is the delta or LMS method and how do we use gradient descent?
 What is LMS update or delta rule, batch descent and stochastic gradient descent?
 Introduction to Decision Trees
 What is a decision tree?
 How to draw a sample decision trees for discrete data?
 How to draw a sample decision trees for continuous data?
 Generate a decision tree from training examples
 Decision tree for playing tennis
 Introduction to ID3 (searching for a good tree )
 Learning Decision Tree
 How do we select attributes for the decision tree? (information gain, entropy)
 Example of creating a decision tree (using the ID3 algorithm)
 What is the GINI Index?
 How do we split continuous attributes and what are the practical issues in classification
 Practical issues in classification
 Overfitting
 What is overfitting?
 An example of underfitting and overfitting
 Overfitting due to noise or insufficient examples
 How to avoid overfitting?
 What is MDL?
 What are the conditions for prepruning?
 How do we use reduced error pruning for post pruning?
 What are the triple tradeoffs in model selection and generalization?
 What is regularization?
 Python exercise on decision tree and linear regression
 Python exercise on linear regression
 Python exercise on logistic regression
 Python exercise on decision tree regression
 Tutorial II
 How to solve a sample problem in linear regression?
 How to solve problems related to decision trees?
 How to find the entropy of a set and use in decision trees?
 What is the information gain?
 KNearest Neighbour
 What are instancebased learning and KNearest Neighbour algorithm?
 What are the standard distance function (Euclidean distance) and the 3 issues related to it?
 What are some examples of KNearest Neighbour and what is the impact of k?
 How can we use weighted distance functions?
 Why do we need to remove extra features?
 What are the various approaches to giving weights?
 Feature Selection
 Why do we need feature reduction?
 What is the curse of dimensionality?
 How can we do feature reduction? (selection and extraction)
 How can we evaluate feature subset? (wrapper / supervised and filter / unsupervised)
 How can we use the feature selection algorithm? (forward and backward selection algorithm)
 What are univariate feature selection methods?
 What are multivariate feature selection methods?
 Feature Extraction
 What is feature extraction and what kind of features do we want?
 What are the principal components (PCs) and how do we choose features?
 How do we choose the direction of the principal components (PCs) and how do we use PCA?
 How do we choose a feature (axis) for classification and how is Linear Discriminant Analysis useful?
 Collaborative Filtering
 What is a recommender system?
 How can we formally define recommendation problem?
 What are the two types of recommendation systems? (content, collaborative filtering)
 What are the two types of collaborative filtering? (used based nearest nbr, itembased nearest nbr)
 What are the two phases of algorithms for collaborative filtering? (nbr formation, recommendation)
 What are the issues with userbased KNN CF?
 What is itembased collaborative filtering?
 Python Exercise on KNN and PCA
 What will we cover?
 How do we use KNeighbors classifier in Python?
 How do we use randomized PCA in Python?
 How can we do face recognition using PCA and KNN?
 Tutorial III
 What is the curse of dimensionality?
 What is feature selection?
 What is feature reduction and PCA? (principal component analysis)
 How do you calculate the eigenvalues and eigenvector of a matrix?
 What is KNN (K Nearest Neighbour) classification?
 Bayesian Learning
 How is probability used for modeling concepts?
 What is the Bayes theorem?
 Can we look at an example of Bayes theorem?
 How can the Bayes theorem be applied to find the hypothesis in Machine Learning? (MAP hypothesis)
 What is the Bayes optimal classifier?
 Gibbs sampling
 Naive Bayes
 Naive Bayes algorithm
 Naive Bayes algorithm for discrete x
 What is smoothing and why is it required?
 Can we look at an example of a Naive Bayes algorithm for discrete x?
 How do we use smoothing when estimating parameters?
 What is the assumption that we made in Naive Bayes and what happens if it is invalid?
 What is gaussian Naive Bayes? (for continuous X, but discrete Y)
 What are Bayesian networks?
 Bayesian Network
 Why do we need a Bayes network?
 Can we look at an example of Bayes network?
 What does a Bayesian network represent?
 What can we do with a Bayesian network (Inference)?
 Where can we apply Bayesian network?
 How do we define a Bayesian network?
 What is the graphical representation of the Naive Bayes model?
 What is the hidden Markov model?
 How is learning helped by Bayesian belief networks?
 Python Exercise on Naive Bayes
 How to use the Naive Bayes classifier?
 What is the Naive Bayes classifier?
 How is the Naive Bayes classifier relevant in the context of email spam classification?
 Tutorial IV
 How do we estimate the probabilities using the frequency distribution of probability?
 How do we use Bayes rule?
 What is MAP inference?
 What is the Naive Bayes assumption?
 What is Bayesian networks (the structures), inference and marginalization?
 Logistic Regression
 What are Logistic Regression (for Classification problems) and sigmoid function?
 What are some of the Interesting Properties of Sigmoid function?
 How can we use stochastic gradient descent with logistic regression?
 Introduction Support Vector Machine
 Support vector machine
 Functional margin
 The functional margin of a set of point
 Solving the optimization problem
 SVM The Dual Formulation
 Lagrangian duality in brief
 The KKT conditions
 Implication of Lagrangian
 The dual problem
 SVM Maximum Margin with Noise
 Linear SVM formulation
 Limitation of previous SVM formulation
 What objective to be minimized?
 Lagrangian
 Dual formulation
 Nonlinear SVM and Kernel Function
 Nonlinear SVM, feature space, and kernel function
 Kernel trick
 Commonly used the kernel function
 Performance
 SVM Solution to the Dual Problem
 SMO algorithm (sequential optimization)
 Coordinate ascent
 SMO (for the dual problem)
 Python Exercise on SVM
 Support vector classification
 Visualize the decision boundaries
 Load data
 Introduction to NN
 Neural network and neuron
 Perceptron  basic unit in NN
 Gradient descent
 Stochastic gradient descent
 Multilayer networks  by stochastic many NN
 Multilayer Neural Network
 Limitation of perceptrons
 Multilayer NN
 Power/ Expressiveness of multilayer networks
 Twolayer backpropagation neural network
 Learning for BP nets
 Derivation
 Neural Network and Backpropagation Algorithm
 Single layer perceptron and boolean functions (OR, XOR)
 Representation capability of NNs
 Learning in multilayer N using backpropagation
 Derivation
 Backpropagation algorithm
 Training practices: batch vs stochastic and learning in the epoch
 Overfitting in anns and local minima
 Deep Neural Network
 Deep learning
 Hierarchical representation & unsupervised pretraining
 Architecture & Training
 Pooling
 CNN properties
 Python Exercise on Neural Network
 How can we create an artificial neural network using TensorFlow and TFLearn to recognize handwritten digits?
 How do we load dependencies (to recognize handwritten digits)?
 How do we load the data (to recognize handwritten digits)?
 How do we make the model (to recognize handwritten digits)?
 How do we train the model (to recognize handwritten digits)?
 What is our takeaway from this exercise (to recognize handwritten digits)?
 Tutorial VI
 What is the perception?
 What is the perceptron learning rule?
 How do we represent a boolean function using a perceptron?
 What is the forward and backward pass algorithm or backpropagation algorithm?
 Stochastic gradient descent and batch gradient descent
 A quick overview of some deep learning algorithms
 Introduction to Computational Learning Theory
 The goal of learning theory & Core aspect of Machine Learning
 PAC
 Prototypical concept learning task
 Sample Complexity Finite Hypothesis Space
 What is Sample Complexity?
 Can we look at an example of the consistent case?
 What is FindS algorithm and what can it do?
 VC Dimension
 What kind of theorems do we have when hypothesis state is infinite?
 What is shattering?
 What is the definition of VC dimension?
 What is the upper bound and lower band on sample complexity with VC?
 Introduction to Ensembles
 What is ensemble learning?
 How can we use weak learners?
 How can we combine learners in Bayesian classifiers?
 Why are ensembles successful and what are the main challenges with them?
 Bagging and Boosting
 What is Bagging?
 What is Boosting and what is AdaBoost?
 Why does ensembling work?
 Introduction to Clustering
 What is unsupervised learning and clustering?
 What are some applications of clustering, and what are various aspects of clustering?
 Major clustering approaches
 How can we measure the quality of clustering?
 Kmeans Clustering
 What is the Kmeans algorithm?
 How can we describe the Kmeans Algorithm, and can we look at an illustration of it?
 What are the similarity and distance measures?
 What is the proof of convergence of Kmeans, time complexity, advantages, and disadvantages?
 What is modelbased clustering?
 How can we apply Kmeans on an RGB image?
 What is EM algorithm?
 Agglomerative Hierarchical Clustering

 What is hierarchical clustering, bottom up and top down clustering?
 What is a Dendrogram?
 What is the algorithm for Agglomerative Hierarchical Clustering?
 What is the complete link method?
 What is average link clustering?

 Python Exercise on Kmeans clustering
 Can we look at python code for K means algorithm?
 Can we look at python code for Gaussian mixture model?
 Hierarchical agglomerative clustering
 Tutorial VIII
 What is Kmeans clustering?
 Solving a sample problem n Kmeans clustering
 What is agglomerative hierarchical clustering?
 What is Gaussian mixture model?
 Machine Learning Final Quiz