Courses

UDEMY

Source : Udemy
Course Name : Machine Learning A-Z™: Hands-On Python & R In Data Science
Link : Machine Learning A-Z™: Hands-On Python & R In Data Science

Course Details :
Part 1 – Data Preprocessing
Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
Part 4 – Clustering: K-Means, Hierarchical Clustering
Part 5 – Association Rule Learning: Apriori, Eclat
Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
Part 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Detailed course:
Section: 1

Welcome to the course!

1. Applications of Machine Learning
2. Why Machine Learning is the Future
3. Important notes, tips & tricks for this course
4. Installing Python and Anaconda (Mac, Linux & Windows)
5. Update: Recommended Anaconda Version
6. Installing R and R Studio (Mac, Linux & Windows)
7. BONUS: Meet your instructors

Section: 2

Part 1: Data Preprocessing

8. Welcome to Part 1 – Data Preprocessing
9. Get the dataset
10. Importing the Libraries
11. Importing the Dataset
12. For Python learners, summary of Object-oriented programming: classes & objects
13. Missing Data
14. Categorical Data
15. WARNING – Update
16. Splitting the Dataset into the Training set and Test set
17. Feature Scaling
18. And here is our Data Preprocessing Template!
Quiz 1: Data Preprocessing

Section: 3

Part 2: Regression

19. Welcome to Part 2 – Regression

Section: 4

Simple Linear Regression

20. How to get the dataset
21. Dataset + Business Problem Description
22. Simple Linear Regression Intuition – Step 1
23. Simple Linear Regression Intuition – Step 2
24. Simple Linear Regression in Python – Step 1
25. Simple Linear Regression in Python – Step 2
26. Simple Linear Regression in Python – Step 3
27. Simple Linear Regression in Python – Step 4
28. Simple Linear Regression in R – Step 1
29. Simple Linear Regression in R – Step 2
30. Simple Linear Regression in R – Step 3
31. Simple Linear Regression in R – Step 4
Quiz 2: Simple Linear Regression

Section: 5

Multiple Linear Regression

32. How to get the dataset
33. Dataset + Business Problem Description
34. Multiple Linear Regression Intuition – Step 1
35. Multiple Linear Regression Intuition – Step 2
36. Multiple Linear Regression Intuition – Step 3
37. Multiple Linear Regression Intuition – Step 4
38. Prerequisites: What is the P-Value?
39. Multiple Linear Regression Intuition – Step 5
40. Multiple Linear Regression in Python – Step 1
41. Multiple Linear Regression in Python – Step 2
42. Multiple Linear Regression in Python – Step 3
43. Multiple Linear Regression in Python – Backward Elimination – Preparation
44. Multiple Linear Regression in Python – Backward Elimination – HOMEWORK !
45. Multiple Linear Regression in Python – Backward Elimination – Homework Solution
46. Multiple Linear Regression in Python – Automatic Backward Elimination
47. Multiple Linear Regression in R – Step 1
48. Multiple Linear Regression in R – Step 2
49. Multiple Linear Regression in R – Step 3
50. Multiple Linear Regression in R – Backward Elimination – HOMEWORK !
51. Multiple Linear Regression in R – Backward Elimination – Homework Solution
52. Multiple Linear Regression in R – Automatic Backward Elimination
Quiz 3: Multiple Linear Regression

Section: 6

Polynomial Regression

53. Polynomial Regression Intuition
54. How to get the dataset
55. Polynomial Regression in Python – Step 1
56. Polynomial Regression in Python – Step 2
57. Polynomial Regression in Python – Step 3
58. Polynomial Regression in Python – Step 4
59. Python Regression Template
60. Polynomial Regression in R – Step 1
61. Polynomial Regression in R – Step 2
62. Polynomial Regression in R – Step 3
63. Polynomial Regression in R – Step 4
64. R Regression Template

Section: 7

Support Vector Regression (SVR)

65. How to get the dataset
66. SVR Intuition
67. SVR in Python
68. SVR in R

Section: 8

Decision Tree Regression

69. Decision Tree Regression Intuition
70. How to get the dataset
71. Decision Tree Regression in Python
72. Decision Tree Regression in R

Section: 9

Random Forest Regression

73. Random Forest Regression Intuition
74. How to get the dataset
75. Random Forest Regression in Python
76. Random Forest Regression in R

Section: 10

Evaluating Regression Models Performance

77. R-Squared Intuition
78. Adjusted R-Squared Intuition
79. Evaluating Regression Models Performance – Homework’s Final Part
80. Interpreting Linear Regression Coefficients
81. Conclusion of Part 2 – Regression

Section: 11

Part 3: Classification

82. Welcome to Part 3 – Classification

Section: 12

Logistic Regression

83. Logistic Regression Intuition
84. How to get the dataset
85. Logistic Regression in Python – Step 1
86. Logistic Regression in Python – Step 2
87. Logistic Regression in Python – Step 3
88. Logistic Regression in Python – Step 4
89. Logistic Regression in Python – Step 5
90. Python Classification Template
91. Logistic Regression in R – Step 1
92. Logistic Regression in R – Step 2
93. Logistic Regression in R – Step 3
94. Logistic Regression in R – Step 4
95. Logistic Regression in R – Step 5
96. R Classification Template
Quiz 4: Logistic Regression

Section: 13

K-Nearest Neighbors (K-NN)

97. K-Nearest Neighbor Intuition
98. How to get the dataset
99. K-NN in Python
100. K-NN in R
Quiz 5: K-Nearest Neighbor

Section: 14

Support Vector Machine (SVM)

101. SVM Intuition
102. How to get the dataset
103. SVM in Python
104. SVM in R
SVM.zip

Section: 15

Kernel SVM

105. Kernel SVM Intuition
106. Mapping to a higher dimension
107. The Kernel Trick
108. Types of Kernel Functions
109. How to get the dataset
110. Kernel SVM in Python
111. Kernel SVM in R

Section: 16

Naive Bayes

112. Bayes Theorem
113. Naive Bayes Intuition
114. Naive Bayes Intuition (Challenge Reveal)
115. Naive Bayes Intuition (Extras)
116. How to get the dataset
117. Naive Bayes in Python
118. Naive Bayes in R

Section: 17

Decision Tree Classification

119. Decision Tree Classification Intuition
120. How to get the dataset
121. Decision Tree Classification in Python
122. Decision Tree Classification in R

Section: 18

Random Forest Classification

123. Random Forest Classification Intuition
124. How to get the dataset
125. Random Forest Classification in Python
126. Random Forest Classification in R

Section: 19

Evaluating Classification Models Performance

127. False Positives & False Negatives
128. Confusion Matrix
129. Accuracy Paradox
130. CAP Curve
131. CAP Curve Analysis
132. Conclusion of Part 3 – Classification

Section: 20

Part 4: Clustering

133. Welcome to Part 4 – Clustering

Section: 21

K-Means Clustering

134. K-Means Clustering Intuition
135. K-Means Random Initialization Trap
136. K-Means Selecting The Number Of Clusters
137. How to get the dataset
138. K-Means Clustering in Python
139. K-Means Clustering in R
Quiz 6: K-Means Clustering

Section: 22

Hierarchical Clustering

140. Hierarchical Clustering Intuition
141. Hierarchical Clustering How Dendrograms Work
142. Hierarchical Clustering Using Dendrograms
143. How to get the dataset
144. HC in Python – Step 1
145. HC in Python – Step 2
146. HC in Python – Step 3
147. HC in Python – Step 4
148. HC in Python – Step 5
149. HC in R – Step 1
150. HC in R – Step 2
151. HC in R – Step 3
152. HC in R – Step 4
153. HC in R – Step 5
Quiz 7: Hierarchical Clustering
154. Conclusion of Part 4 – Clustering

Section: 23

Part 5: Association Rule Learning

155. Welcome to Part 5 – Association Rule Learning

Section: 24

Apriori

156. Apriori Intuition
157. How to get the dataset
158. Apriori in R – Step 1
159. Apriori in R – Step 2
160. Apriori in R – Step 3
161. Apriori in Python – Step 1
162. Apriori in Python – Step 2
163. Apriori in Python – Step 3

Section: 25

Eclat

164. Eclat Intuition
165. How to get the dataset
166. Eclat in R
Eclat.zip

Section: 26

Part 6: Reinforcement Learning

167. Welcome to Part 6 – Reinforcement Learning

Section: 27

Upper Confidence Bound (UCB)

168. The Multi-Armed Bandit Problem
169. Upper Confidence Bound (UCB) Intuition
170. How to get the dataset
171. Upper Confidence Bound in Python – Step 1
172. Upper Confidence Bound in Python – Step 2
173. Upper Confidence Bound in Python – Step 3
174. Upper Confidence Bound in Python – Step 4
175. Upper Confidence Bound in R – Step 1
176. Upper Confidence Bound in R – Step 2
177. Upper Confidence Bound in R – Step 3
178. Upper Confidence Bound in R – Step 4

Section: 28

Thompson Sampling

179. Thompson Sampling Intuition
180. Algorithm Comparison: UCB vs Thompson Sampling
181. How to get the dataset
182. Thompson Sampling in Python – Step 1
183. Thompson Sampling in Python – Step 2
184. Thompson Sampling in R – Step 1
185. Thompson Sampling in R – Step 2

Section: 29

Part 7: Natural Language Processing

186. Welcome to Part 7 – Natural Language Processing
187. Natural Language Processing Intuition
188. How to get the dataset
189. Natural Language Processing in Python – Step 1
190. Natural Language Processing in Python – Step 2
191. Natural Language Processing in Python – Step 3
192. Natural Language Processing in Python – Step 4
193. Natural Language Processing in Python – Step 5
194. Natural Language Processing in Python – Step 6
195. Natural Language Processing in Python – Step 7
196. Natural Language Processing in Python – Step 8
197. Natural Language Processing in Python – Step 9
198. Natural Language Processing in Python – Step 10
199. Homework Challenge
200. Natural Language Processing in R – Step 1
201. Natural Language Processing in R – Step 2
202. Natural Language Processing in R – Step 3
203. Natural Language Processing in R – Step 4
204. Natural Language Processing in R – Step 5
205. Natural Language Processing in R – Step 6
206. Natural Language Processing in R – Step 7
207. Natural Language Processing in R – Step 8
208. Natural Language Processing in R – Step 9
209. Natural Language Processing in R – Step 10
210. Homework Challenge

Section: 30

Part 8: Deep Learning

211. Welcome to Part 8 – Deep Learning
212. What is Deep Learning?

Section: 31

Artificial Neural Networks

213. Plan of attack
214. The Neuron
215. The Activation Function
216. How do Neural Networks work?
217. How do Neural Networks learn?
218. Gradient Descent
219. Stochastic Gradient Descent
220. Backpropagation
221. How to get the dataset
222. Business Problem Description
223. ANN in Python – Step 1 – Installing Theano, Tensorflow and Keras
224. ANN in Python – Step 2
225. ANN in Python – Step 3
226. ANN in Python – Step 4
227. ANN in Python – Step 5
228. ANN in Python – Step 6
229. ANN in Python – Step 7
230. ANN in Python – Step 8
231. ANN in Python – Step 9
232. ANN in Python – Step 10
233. ANN in R – Step 1
234. ANN in R – Step 2
235. ANN in R – Step 3
236. ANN in R – Step 4 (Last step)

Section: 32

Convolutional Neural Networks

237. Plan of attack
238. What are convolutional neural networks?
239. Step 1 – Convolution Operation
240. Step 1(b) – ReLU Layer
241. Step 2 – Pooling
242. Step 3 – Flattening
243. Step 4 – Full Connection
244. Summary
245. Softmax & Cross-Entropy
246. How to get the dataset
247. CNN in Python – Step 1
248. CNN in Python – Step 2
249. CNN in Python – Step 3
250. CNN in Python – Step 4
251. CNN in Python – Step 5
252. CNN in Python – Step 6
253. CNN in Python – Step 7
254. CNN in Python – Step 8
255. CNN in Python – Step 9
256. CNN in Python – Step 10
257. CNN in R

Section: 33

Part 9: Dimensionality Reduction

258. Welcome to Part 9 – Dimensionality Reduction

Section: 34

Principal Component Analysis (PCA)

259. Principal Component Analysis (PCA) Intuition
260. How to get the dataset
261. PCA in Python – Step 1
262. PCA in Python – Step 2
263. PCA in Python – Step 3
264. PCA in R – Step 1
265. PCA in R – Step 2
266. PCA in R – Step 3

Section: 35

Linear Discriminant Analysis (LDA)

267. Linear Discriminant Analysis (LDA) Intuition
268. How to get the dataset
269. LDA in Python
270. LDA in R

Section: 36

Kernel PCA

271. How to get the dataset
272. Kernel PCA in Python
273. Kernel PCA in R

Section: 37

Part 10: Model Selection & Boosting

274. Welcome to Part 10 – Model Selection & Boosting

Section: 38

Model Selection

275. How to get the dataset
276. k-Fold Cross Validation in Python
277. k-Fold Cross Validation in R
278. Grid Search in Python – Step 1
279. Grid Search in Python – Step 2
280. Grid Search in R

Section: 39

XGBoost

281. How to get the dataset
282. XGBoost in Python – Step 1
283. XGBoost in Python – Step 21
284. XGBoost in R

 

Source : Udemy
Course Name : Python for Data Science and machine learning bootcamp
Link : Python for Data Science and machine learning bootcamp

Course Details :

What will I learn?

Use Python for Data Science and Machine Learning
Use Spark for Big Data Analysis
Implement Machine Learning Algorithms
Learn to use NumPy for Numerical Data
Learn to use Pandas for Data Analysis
Learn to use Matplotlib for Python Plotting
Learn to use Seaborn for statistical plots
Use Plotly for interactive dynamic visualizations
Use SciKit-Learn for Machine Learning Tasks
K-Means Clustering
Logistic Regression
Linear Regression
Random Forest and Decision Trees
Natural Language Processing and Spam Filters
Neural Networks
Support Vector Machines

Curriculum For This Course

Course Introduction

Environment Set-Up
Jupyter Overview

Python Crash Course
Python for Data Analysis – NumPy
Python for Data Analysis – Pandas
Python for Data Analysis – Pandas Exercises
Python for Data Visualization – Matplotlib
Python for Data Visualization – Seaborn
Python for Data Visualization – Pandas Built-in Data Visualization
Python for Data Visualization – Plotly and Cufflinks
Python for Data Visualization – Geographical Plotting

Data Capstone Project

Introduction to Machine Learning
Linear Regression
Cross Validation and Bias-Variance Trade-Off
Logistic Regression
K Nearest Neighbors
Decision Trees and Random Forests
Support Vector Machines
K Means Clustering
Principal Component Analysis
Recommender Systems
Natural Language Processing
Big Data and Spark with Python
Neural Nets and Deep Learning

APPENDIX: OLD TENSORFLOW VIDEOS (Version 0.8)
BONUS: DISCOUNT COUPONS FOR OTHER COURSES

 

Source : Udemy
Course Name : Machine learning with Scikit-learn
Link : Machine learning with Scikit-learn

Course Details :

What Will I Learn?

Load data into scikit-learn; Run many machine learning algorithms both for unsupervised and supervised data
Assess model accuracy and performance
Being able to decide what’s the best model for every scenario

Requirements

Some Python and statistics knowledge is required: Being able to code loops, functions, classes in Python is necessary.
Understanding what are random variables, what is a Gaussian distribution, and the underlying concepts behind linear regression are necessary as well.

Curriculum For This Course

27 Lectures

Introduction to Scikit-learn
Supervised methods
Unsupervised methods

Detailed Curriculum For This Course

  • Introduction to Scikit-learn
    1. Introduction
    2. Installing scikit-learn
    3. Data manipulation: from Pandas to scikit-learn
    4. Creating synthetic data
  • Supervised methods
    1. Naive Bayes : Bernoulli – Multinomial
    2. Detecting spam in real SMS Kaggle data
    3. Linear Support Vector Machines (SVM): SVM and LinearSVC
    4. Linear Support Vector Machines (SVM): NuSVM
    5. Quiz on SVM
    6. Logistic regression
    7. Predicting if income >50k using real US Census Data
    8. Isotonic regression
    9. Linear regression – Lasso – Ridge
    10. Quiz on Lasso – Ridge
    11. Decision trees
    12. Introduction to ensemble methods
    13. Averaging ensemble methods – Part 1: Bagging
    14. Averaging ensemble methods – Part 2: Random forests
    15. Digit Classification via Random Forests
    16. Boosting ensemble methods
    17. Grid Search Cross Validation
    18. Predicting real house prices in the US using ExtraTreesRegressor
  • Unsupervised methods
    1. Density Estimation
    2. Principal Components
    3. Principal Components
    4. K-Means
    5. Preview
    6. DBScan
    7. Clustering
    8. Clustering and PCA on real countries data from Kaggle
    9. Outlier detection
    10. Novelty detection

 

Source : Udemy
Course Name : A Gentle Introduction to Machine Learning Using SciKit-Learn
Link : A Gentle Introduction to Machine Learning Using SciKit-Learn

Course Details :

What Will I Learn?

At the end of the course you’ll understand how to create an end to end model using Python’s SciKit_Learn.
You’ll understand the nomenclature and process when creating a solution in SciKit_Learn.
You will also have a Jupyter Notebook that’s annotated with all the important points in the course.
You will also receive a completed Jupyter Notebook filled with models and references.

Curriculum For This Course

  • Introduction
    1. What is our Goal?
    2. Predictive Modeling
    3. Why use scikit-learn?
    4. Installing Python and SciKit Learn
    5. Terminology
    6. Jupyter Notebook Anatomy
    7. Course Downloads
    8. Summary
    9. Quiz
  • Building Our Model
    1. An End to End Model Walk through – Part 1
    2. An End to End Model Walk through – Part 2
    3. All Canned Data is Clean
    4. Building the Model – Part 1
    5. Building the Model – Part 2
    6. Building the Model – Part 3
    7. Building the Model – Part 4
    8. Summary
    9. Quiz

 

Source : Udemy
Course Name : Introduction to Natural Language Processing (NLP)
Link : Introduction to Natural Language Processing (NLP)

Course Details :

What Will I Learn?

Work with text data using the Natural Language Toolkit.
Load and manipulate custom text data.
Analyze text to discover, sentiment, important key words, and statistics.

Curriculum For This Course

  • Course Introduction
    • Course Intro and Outline
  • Setup
    • Windows Setup
    • OS X Setup
  • Python Refresher
    • Lists
    • Dictionaries
    • Loops and Conditionals
    • Functions
  • NLTK and the Basics
    • Overview – The Natural Language Toolkit
    • Counting Text
    • Example – Words Per Sentence Trends
    • Frequency Distribution
    • Conditional Frequency Distribution
    • Example – Informative Words
    • Bigrams
    • Overview – Regular Expressions
    • Regular Expression Practice
  • Tokenization , Tagging, Chunking
    • Overview – Tokenization
    • Tokenization
    • Normalizing
    • Part of Speech Tagging
    • Example – Multiple Parts of Speech
    • Example – Choices
    • Chunking
    • Named Entity Recognition
  • Custom Sources
    • Overview – Character Encoding
    • Text File
    • HTML
    • URL
    • CSV File
    • Exporting
    • NLTK Resources
    • Example – Remove Stopwords
  • Projects
    • Sentiment Analysis Intro
    • Basic Sentiment Analysis
    • Gender Prediction Intro
    • Gender Prediction
    • TF-IDF Intro
    • TF-IDF
  • Appendix
    • Additional NLP Resources
    • Learning Python
    • Future Course Content

 

EDX

Source : EdX
Course Name : Machine Learning Fundamentals
Link : Machine Learning Fundamentals

Course Details :

In this course, part of the Data Science MicroMasters program, you will learn a variety of supervised and unsupervised learning algorithms, and the theory behind those algorithms.
Using real-world case studies, you will learn how to classify images, identify salient topics in a corpus of documents, partition people according to personality profiles, and automatically capture the semantic structure of words and use it to categorize documents.
Armed with the knowledge from this course, you will be able to analyze many different types of data and to build descriptive and predictive models.All programming examples and assignments will be in Python, using Jupyter notebooks.

What you’ll learn

Classification, regression, and conditional probability estimation
Generative and discriminative models
Linear models and extensions to nonlinearity using kernel methods
Ensemble methods: boosting, bagging, random forests
Representation learning: clustering, dimensionality reduction, autoencoders, deep nets

 

Source : EdX
Course Name : Machine Learning
Link : Machine Learning

Course Details :

What you’ll learn

Supervised learning techniques for regression and classification
Unsupervised learning techniques for data modeling and analysis
Probabilistic versus non-probabilistic viewpoints
Optimization and inference algorithms for model learning

Course Syllabus

Week 1: maximum likelihood estimation, linear regression, least squares
Week 2: ridge regression, bias-variance, Bayes rule, maximum a posteriori inference
Week 3: Bayesian linear regression, sparsity, subset selection for linear regression
Week 4: nearest neighbor classification, Bayes classifiers, linear classifiers, perceptron
Week 5: logistic regression, Laplace approximation, kernel methods, Gaussian processes
Week 6: maximum margin, support vector machines, trees, random forests, boosting
Week 7: clustering, k-means, EM algorithm, missing data
Week 8: mixtures of Gaussians, matrix factorization
Week 9: non-negative matrix factorization, latent factor models, PCA and variations
Week 10: Markov models, hidden Markov models
Week 11: continuous state-space models, association analysis
Week 12: model selection, next steps

 

Source : University of Texas at Austin Computer Science
Course Name : Machine Learning
Link : Machine Learning

Course Details :

Chapter Outline:

Introduction
Definition of learning systems. Goals and applications of machine learning. Aspects of developing a learning system: training data, concept representation, function approximation.
Concept Learning and the General-to-Specific Ordering
Concept Learning and the General-to-Specific Ordering. The concept learning task. Concept learning as search through a hypothesis space. General-to-specific ordering of hypotheses. Finding maximally specific hypotheses. Version spaces and the candidate elimination algorithm. Learning conjunctive concepts. The importance of inductive bias.
Decision Tree Learning
Decision Tree Learning. Representing concepts as decision trees. Recursive induction of decision trees. Picking the best splitting attribute: entropy and information gain. Searching for simple trees and computational complexity. Occam’s razor. Overfitting, noisy data, and pruning.
Artificial Neural Networks
Artificial Neural Networks. Neurons and biological motivation. Linear threshold units. Perceptrons: representational limitation and gradient descent training. Multilayer networks and backpropagation. Hidden layers and constructing intermediate, distributed representations. Overfitting, learning network structure, recurrent networks.
Evaluating Hypotheses
Bayesian Learning
Computational Learning Theory
Computational Learning Theory. Models of learnability: learning in the limit; probably approximately correct (PAC) learning. Sample complexity: quantifying the number of examples needed to PAC learn. Computational complexity of training. Sample complexity for finite hypothesis spaces. PAC results for learning conjunctions, kDNF, and kCNF. Sample complexity for infinite hypothesis spaces, Vapnik-Chervonenkis dimension.
Instance-Based Learning
Genetic Algorithms
Learning Sets of Rules
Analytical Learning
Combining Inductive and Analytical Learning
Reinforcement Learning
Ensemble Learning
Using committees of multiple hypotheses. Bagging, boosting, and DECORATE. Active learning with ensembles.(read this paper)
Experimental Evaluation of Learning Algorithms
Evaluating Hypotheses. Measuring the accuracy of learned hypotheses. Comparing learning algorithms: cross-validation, learning curves, and statistical hypothesis testing.
Rule Learning: Propositional and First-Order
Learning Sets of Rules. Translating decision trees into rules. Heuristic rule induction using separate and conquer and information gain. First-order Horn-clause induction (Inductive Logic Programming) and Foil. Learning recursive rules. Inverse resolution, Golem, and Progol.
Support Vector Machines
Maximum margin linear separators. Quadratic programming solution to finding maximum margin separators. Kernels for learning non-linear functions.
Bayesian Learning
Bayesian Learning and new on-line chapter. Probability theory and Bayes rule. Naive Bayes learning algorithm. Parameter smoothing. Generative vs. discriminative training. Logistic regression. Bayes nets and Markov nets for representing dependencies.
Instance-Based Learning
Instance-Based Learning. Constructing explicit generalizations versus comparing to past specific examples. k-Nearest-neighbor algorithm. Case-based learning.
Text Classification
Bag of words representation. Vector space model and cosine similarity. Relevance feedback and Rocchio algorithm. Versions of nearest neighbor and Naive Bayes for text.
Clustering and Unsupervised Learning
Learning from unclassified data. Clustering. Hierarchical Agglomerative Clustering. k-means partitional clustering. Expectation maximization (EM) for soft clustering. Semi-supervised learning with EM using labeled and unlabeled data.
Language Learning
Classification problems in language: word-sense disambiguation, sequence labeling. Hidden Markov models (HMM’s). Veterbi algorithm for determining most-probable state sequences. Forward-backward EM algorithm for training the parameters of HMM’s. Use of HMM’s for speech recognition, part-of-speech tagging, and information extraction. Conditional random fields (CRF’s). Probabilistic context-free grammars (PCFG). Parsing and learning with PCFGs. Lexicalized PCFGs.

 

Coursera

Source : Coursera
Course Name : Machine Learning
Link : Machine Learning

Course Details :

WEEK 1 :

Introduction:
Supervised Learning
Unsupervised Learning
Linear Regression with One Variable
Model Representation
Cost Function
Gradient Descent
Gradient Descent For Linear Regression
Linear Algebra Review
Matrices and Vectors
Addition and Scalar Multiplication
Matrix Vector Multiplication
Inverse and Transpose

WEEK 2 :

Linear Regression with Multiple Variables
Setting Up Your Programming Assignment Environment
Octave/Matlab Tutorial
Vectorization
Plotting Data

WEEK 3 :

Logistic Regression
Classification
Hypothesis Representation
Decision Boundary
Simplified Cost Function and Gradient Descent
Multiclass Classification: One-vs-all
Regularization
The Problem of Overfitting
Regularized Linear Regression

WEEK 4 : Neural Networks: Representation

Non-Linear Hypothesis
Neurons and the Brain
Multiclass Classification
Multi-class Classification and Neural Networks

WEEK 5 : Neural Networks: Learning

Backpropagation Algorithm
Implementation Note: Unrolling Parameters
Gradient Checking
Random Initialization

WEEK 6 :

Advice for Applying Machine Learning
Evaluating a Hypothesis
Model Selection and Train/Validation/Test Sets
Diagnosing Bias vs. Variance
Regularization and Bias/Variance
Learning Curves
Deciding What to Do Next Revisited
Machine Learning System Design
Error Analysis
Error Metrics for Skewed Classes
Trading Off Precision and Recall
Data For Machine Learning

WEEK 7 : Support Vector Machines

Optimization Objective
Large Margin Intuition
Mathematics Behind Large Margin Classification
Kernels
Using An SVM

WEEK 8 :

Unsupervised Learning
K-Means Algorithm
Optimization Objective
Choosing the Number of Clusters
Dimensionality Reduction
Principal Component Analysis Problem Formulation
Choosing the Number of Principal Components
Advice for Applying PCA

WEEK 9 :

Anomaly Detection
Content Based Recommendations
Collaborative Filtering
Vectorization: Low Rank Matrix Factorization
Implementational Detail: Mean Normalization
Recommender Systems
Collaborative Filtering Algorithm
Vectorization: Low Rank Matrix Factorization
Implementational Detail: Mean Normalization

WEEK 10 : Large Scale Machine Learning

Stochastic Gradient Descent
Mini-Batch Gradient Descent
Stochastic Gradient Descent Convergence
Map Reduce and Data Parallelism

WEEK 11 : Application Example: Photo OCR

Getting Lots of Data and Artificial Data
Ceiling Analysis: What Part of the Pipeline to Work on Next

 

Source : Coursera
Course Name : Neural Networks and Deep Learning
Link : Neural Networks and Deep Learning

Course Details :

WEEK 1 : Introduction to deep learning

What is a neural network?
Supervised Learning with Neural Networks

WEEK 2 : Neural Networks Basics

Logistic Regression
Binary Classification
Logistic Regression Cost Function
Gradient Descent
Computation graph
Logistic Regression Gradient Descent
Vectorization
Broadcasting in Python
Python Basics with numpy

WEEK 3 : Shallow neural networks

Neural Network Representation
Computing a Neural Network’s Output
Vectorized Implementations
Non-linear activation functions
Backpropagation intuition
Planar data classification with a hidden layer

WEEK 4 : Deep Neural Networks

Forward Propagation in a Deep Network
Forward and Backward Propagation
Parameters vs Hyperparameters

 

Source : Coursera
Course Name : Convolutional Neural Networks
Link : Convolutional Neural Networks

Course Details :

WEEK 1 : Foundations of Convolutional Neural Networks

Edge Detection
Padding
Strided Convolutions
Convolutions Over Volume
One Layer of a Convolutional Network
Pooling Layers

WEEK 2 : Deep convolutional models: case studies

Classic Networks
ResNets
Inception Network Motivation
Using Open-Source Implementation
Transfer Learning
Data Augmentation
State of Computer Vision
Keras Tutorial – The Happy House (not graded)
Residual Networks

WEEK 3 : Object detection

Landmark Detection
Object Detection
Convolutional Implementation of Sliding Windows
Bounding Box Predictions
Intersection Over Union
Non-max Suppression
Anchor Boxes
YOLO Algorithm

WEEK 4 : Special applications: Face recognition & Neural style transfer

Siamese Network
Triplet Loss
Face Verification and Binary Classification
Cost Function
Style Cost Function
1D and 3D Generalization

 

NPTEL

Source : NPTEL course offered July 2020
Course Name : Deep Learning
Link : Deep Learning

Course Details :

Week 1 :

(Partial) History of Deep Learning, Deep Learning Success Stories, McCulloch Pitts Neuron, Thresholding Logic, Perceptrons, Perceptron Learning Algorithm

Week 2 :

Multilayer Perceptrons (MLPs), Representation Power of MLPs, Sigmoid Neurons, Gradient Descent, Feedforward Neural Networks, Representation Power of Feedforward Neural Networks

Week 3 :

FeedForward Neural Networks, Backpropagation

Week 4 :

Gradient Descent (GD), Momentum Based GD, Nesterov Accelerated GD, Stochastic GD, AdaGrad, RMSProp, Adam, Eigenvalues and eigenvectors, Eigenvalue Decomposition, Basis

Week 5 :

Principal Component Analysis and its interpretations, Singular Value Decomposition

Week 6 :

Autoencoders and relation to PCA, Regularization in autoencoders, Denoising autoencoders, Sparse autoencoders, Contractive autoencoders

Week 7 :

Regularization: Bias Variance Tradeoff, L2 regularization, Early stopping, Dataset augmentation, Parameter sharing and tying, Injecting noise at input, Ensemble methods, Dropout

Week 8 :

Greedy Layerwise Pre-training, Better activation functions, Better weight initialization methods, Batch Normalization

Week 9 :
Learning Vectorial Representations Of Words

Week 10 :

Convolutional Neural Networks, LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet, Visualizing
Convolutional Neural Networks, Guided Backpropagation, Deep Dream, Deep Art, Fooling Convolutional
Neural Networks

Week 11 :

Recurrent Neural Networks, Backpropagation through time (BPTT), Vanishing and Exploding Gradients, Truncated BPTT, GRU, LSTMs

Week 12 :

Encoder Decoder Models, Attention Mechanism, Attention over images

SUGGESTED READING MATERIALS:

Deep Learning, An MIT Press book, Ian Goodfellow and Yoshua Bengio and Aaron Courville http://www.deeplearningbook.org

 

Source : NPTEL course offered July 2020
Course Name : Artificial Intelligence Search Methods for problem Solving
Link : Artificial Intelligence Search Methods for problem Solving

Course Details :

Week Topics
1 Introduction: Overview and Historical Perspective, Turing Test, Physical Symbol Systems and the scope of Symbolic AI, Agents.
2 State Space Search: Depth First Search, Breadth First Search, DFID
3 Heuristic Search: Best First Search, Hill Climbing, Beam Search
4 Traveling Salesman Problem, Tabu Search, Simulated Annealing
5 Population Based Search: Genetic Algorithms, Ant Colony Optimization
6 Branch & Bound, Algorithm A*, Admissibility of A*
7 Monotone Condition, IDA*, RBFS, Pruning OPEN and CLOSED in A*
8 Problem Decomposition, Algorithm AO*, Game Playing
9 Game Playing: Algorithms Minimax, AlphaBeta, SSS*
10 Rule Based Expert Systems, Inference Engine, Rete Algorithm
11 Planning: Forward/Backward Search, Goal Stack Planning, Sussman’s Anomaly
12 Plan Space Planning, Algorithm Graphplan

The following topics are not part of evaluation for this course, and are included for the interested student. These topics will be covered in detail in two followup courses “AI: Knowledge Representation and Reasoning” and “AI: Constraint Satisfaction Problems”.

A1 Constraint Satisfaction Problems, Algorithm AC-1, Knowledge Based Systems
A2 Propositional Logic, Resolution Refutation Method
A3 Reasoning in First Order Logic, Backward Chaining, Resolution Method

Text Book (Chapters 1-8): Deepak Khemani, A First Course in Artificial Intelligence, McGraw Hill (India), 2013

 

Source : NPTEL course offered July 2020
Course Name : Introduction to Machine Learning
Link : Introduction to Machine Learning

Course Details :

Week 1:

Introduction: Basic definitions, types of learning, hypothesis space and inductive bias, evaluation, cross-validation

Week 2:

Linear regression, Decision trees, overfitting

Week 3:

Instance based learning, Feature reduction, Collaborative filtering based recommendation

Week 4:

Probability and Bayes learning

Week 5:

Logistic Regression, Support Vector Machine, Kernel function and Kernel SVM

Week 6:

Neural network: Perceptron, multilayer network, backpropagation, introduction to deep neural network

Week 7:

Computational learning theory, PAC learning model, Sample complexity, VC Dimension, Ensemble learning

Week 8:

Clustering: k-means, adaptive hierarchical clustering, Gaussian mixture model

SUGGESTED READING

Machine Learning. Tom Mitchell. First Edition, McGraw- Hill, 1997.
Introduction to Machine Learning Edition 2, by Ethem Alpaydin

 

Source : NPTEL course offered July 2020
Course Name : Scalable Data Science
Link : Scalable Data Science

Course Details :

Week 1 : Background

Introduction (30 mins) Probability: Concentration inequalities, (30 mins) Linear algebra: PCA, SVD (30 mins) Optimization: Basics, Convex, GD. (30 mins) Machine Learning: Supervised, generalization, feature learning, clustering. (30 mins)

Week 2 : Memory-efficient data structures

Hash functions, universal / perfect hash families (30 min)
Bloom filters (30 mins)
Sketches for distinct count (1 hr)
Misra-Gries sketch. (30 min)

Week 3 : Memory-efficient data structures (contd.)

Count Sketch, Count-Min Sketch (1 hr)
Approximate near neighbors search: Introduction, kd-trees etc (30 mins)
LSH families, MinHash for Jaccard, SimHash for L2 (1 hr)

Week 4 : Approximate near neighbors search

Extensions e.g. multi-probe, b-bit hashing, Data dependent variants (1.5 hr)
Randomized Numerical Linear Algebra Random projection (1 hr)

Week 5 :

Randomized Numerical Linear Algebra CUR Decomposition (1 hr)
Sparse RP, Subspace RP, Kitchen Sink (1.5 hr)

Week 6 :

Map-reduce and related paradigms Map reduce – Programming examples – (page rank, k-means, matrix multiplication) (1 hr)
Big data: computation goes to data. + Hadoop ecosystem (1.5 hrs)

Week 7 :

Map-reduce and related paradigms (Contd.) Scala + Spark (1 hr)
Distributed Machine Learning and Optimization: Introduction (30 mins)
SGD + Proof (1 hr)

Week 8 : Distributed Machine Learning and Optimization

ADMM + applications (1 hr)
Clustering (1 hr)
Conclusion (30 mins)

SUGGESTED READING MATERIALS:

J. Leskovec, A. Rajaraman and JD Ullman. Mining of Massive Datasets. Cambridge University Press, 2nd Ed.
Muthukrishnan, S. (2005). Data streams: Algorithms and applications. Foundations and Trends® in Theoretical Computer Science, 1(2), 117-236.
Woodruff, David P. “”Sketching as a tool for numerical linear algebra.”” Foundations and Trends® in Theoretical Computer Science 10.1–2 (2014): 1-157.
Mahoney, Michael W. “”Randomized algorithms for matrices and data.”” Foundations and Trends® in Machine Learning 3.2 (2011): 123-224.

 

Source : NPTEL course offered July 202
Course Name : The Joy of Computing using Python
Link : The Joy of Computing using Python

Course Details :

Motivation for Computing
Welcome to Programming!!
Variables and Expressions : Design your own calculator
Loops and Conditionals : Hopscotch once again
Lists, Tuples and Conditionals : Lets go on a trip
Abstraction Everywhere : Apps in your phone
Counting Candies : Crowd to the rescue
Birthday Paradox : Find your twin
Google Translate : Speak in any Language
Currency Converter : Count your foreign trip expenses
Monte Hall : 3 doors and a twist
Sorting : Arrange the books
Searching : Find in seconds
Substitution Cipher : What’s the secret !!
Sentiment Analysis : Analyse your Facebook data
20 questions game : I can read your mind
Permutations : Jumbled Words
Spot the similarities : Dobble game
Count the words : Hundreds, Thousands or Millions.
Rock, Paper and Scissor : Cheating not allowed !!
Lie detector : No lies, only TRUTH
Calculation of the Area : Don’t measure.
Six degrees of separation : Meet your favourites
Image Processing : Fun with images
Tic tac toe : Let’s play
Snakes and Ladders : Down the memory lane.
Recursion : Tower of Hanoi
Page Rank : How Google Works !!