Foundation of Machine Learning and Deep learning

The talk introduces the basic of machine learning with a focus on supervised learning (classification and regression). It further presents the basic of deep learning, research direction, and opportunities for machine learning and deep learning in developing countries. It lastly presents different open source python libraries and frameworks for machine learning and deep learning.

Transcript

1. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Foundation of Machine Learning and Deep learning Anthony FAUSTINE sambaiga@gmail.com 05th OCTOBER 2017 Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 1 / 55
2. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Learning goal • Understand the basics of Machine learning and deep learning. • Explore opportunities and research direction in machine learning (ML) and AI. • Understand different python libararies for doing ML and AI research and development. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 2 / 55
3. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Presenter Bio • PhD Scholar at Nelson Mandela African Institution of Science and Technology, • Research : Applied machine learning and signal processing for computational sustainability. • Probabilistic-deep learning algorithms (Hybrid HMM-DNN) for energy dis-aggregation problem. • Unsupervised deep learning. • Bayesian reasoning. • Co-founder Pythontz, Awesome-tech (First ML and AI startup in Tz). • Assistant Lecturer (UDOM), Research and Consultant (Machine Intelligence and Data Science). • Contact : sambaiga.github.io, sambaiga@gmail.com, @sambaiga Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 3 / 55
4. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Pythontz Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 4 / 55
5. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Pythontz About Pythontz • A postive peer learning community for interested Python users in Tanzania. Vision • To create a vibrant and diverse python community in Tanzania. Mission • To foster the application of python programming across industries, learning centers, schools and community in Tanzania. • Focus : Web-development, data science, Machine learning and Artificial Intelligence. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 5 / 55
6. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Awesome-Tech About Awesometech • We are all about continuous innovation, making ideas happen and crafting amazing products. Vision • We focus on the development and deployment of data driven application and systems using Machine Learning and Artificial Intelligence techniques. Mission • To unleash the power of machine learning and artificial intelligence in solving real-community problems. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 6 / 55
7. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Outline 1 Introduction to Machine Learning 2 Typical ML task : Linear Regression 3 Typical ML task : Classification 4 Fundamentals of Deep Learning 5 Research direction and opportunities 6 Python libraries for ML and AI Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 7 / 55
8. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Introduction Data Science ? The future belongs to the companies and people that turn data into products. By Mike Loukides June 2, 2010 Data science : deals with analyzing and manipulating data to derive insights and build data products. • Applies machine learning to create data products Data product : any tool created with the help of data to make a more informed decision. • The end goal of DS ⇒ data product. • Data Science is the real-world application of machine learning, with the goal of creating data products. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 8 / 55
9. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Introduction Data Science ? The future belongs to the companies and people that turn data into products. By Mike Loukides June 2, 2010 Data science : deals with analyzing and manipulating data to derive insights and build data products. • Applies machine learning to create data products Data product : any tool created with the help of data to make a more informed decision. • The end goal of DS ⇒ data product. • Data Science is the real-world application of machine learning, with the goal of creating data products. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 8 / 55
10. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Machine learning Machine learning (ML) : a set of algorithms that automatically detect patterns in data and use the uncovered pattern to make inferences or predictions. ML is a subfield of AI ⇒ aims to enable computers to learn on their own. ML algorithms : 1 Identify patterns in observed data. 2 Build models that explain the world. 3 Predict or do inference. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 9 / 55
11. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Machine learning Aplication • It is an exciting and fast-moving field of computer science with many recent applications. • Computer vision : Object Classification in Photograph, image captioning. • Speech recognition, Automatic Machine Translation, • Communication systems • Robots learning complex behaviors : • Recommendations services ML use cases Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 10 / 55
12. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Why Machine learning ? • Hard problems in high dimensions, like many modern CV or NLP problems require complex models ⇒ difcult to program the correct behavior by hand. • Machines can discover hidden, non-obvious patterns. • A system might need to adapt to a changing environment. • A learning algorithm might be able to perform better than its human programmers. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 11 / 55
13. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Machine learning types Machine learning is usually divide into three major types : 1 Supervised Learning • Learn a model from a given set of input-output pairs, in order to predict the output of new inputs. • Further grouped into Regression and classification problems. 2 Unsupervised Learning • Discover patterns and learn the structure of unlabelled data. • Example Distribution modeling and Clustering. 3 Reiforcement Learning • Learn what actions to take in a given situation, based on rewards and penalties. More details on RL • Example consider teaching a dog a new trick : you cannot tell it what to do, but you can reward/punish it. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 12 / 55
14. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Outline 1 Introduction to Machine Learning 2 Typical ML task : Linear Regression 3 Typical ML task : Classification 4 Fundamentals of Deep Learning 5 Research direction and opportunities 6 Python libraries for ML and AI Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 13 / 55
15. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Linear Regression In regression : predict a scalar-valued target, such as the price of stock. • The target is predicted as a linear function of the inputs. Example applications : 1 weather forecasting. 2 house pricing prediction. 3 student performance prediction. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 14 / 55
16. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Linear Regression : Formulate a learning problem To formulate ML problem mathematically, you need to define two things : 1 Model (Hypothesis) : set of allowable functions that compute predictions from the inputs • In linear regression, the model consists of linear functions given by : ˆ y = f (y, x) = j wj xj + b where w is the weights, and b is the bias. 2 Loss function : defines how well the model fit the data • How far off the prediction ˆ y is from the target y given as : L(ˆ y, y) = 1 2 (ˆ y − y)2 Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 15 / 55
17. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Formulate a learning problem : Cost Function Cost Function Jθ : Is the loss, averaged over all the training examples given by. Jθ = 1 N N i=1 L(ˆ y(i), y(i)) = 1 2N N i=1 (ˆ y(i) − y(i))2 = 1 2N N i=1   j wj x(i) j + b − y(i)   In vectorized form : Jθ = 1 2N ˆ y − y 2= 1 2N (ˆ y − y)T (ˆ y − y) where ˆ y = wTx Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 16 / 55
18. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Formulate a learning problem : Optimization Problem Combining the model and loss function, you get an optimization problem : Objective : minimize a cost function Jθ with respect to the model parameters θ(i.e. the w and b) • A popular minimization technique is gradient descent θt+1 = θt − α ∂Jθ ∂θ where α is the learning rate. Common issues to look out for : • Convergence to a local, non-global minimum. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 17 / 55
19. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Linear Regression Use gradient descent to solve the minimum cost function Jθ θt+1 = θt − α ∂Jθ ∂θ For parameter w and b : wt+1 = wt − α ∂Jθ ∂w bt+1 = bt − α ∂Jθ ∂b where : ∂Jθ ∂w = 1 N xT(ˆ y − y) ∂Jθ ∂b = 1 N (ˆ y − y) Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 18 / 55
20. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Outline 1 Introduction to Machine Learning 2 Typical ML task : Linear Regression 3 Typical ML task : Classification 4 Fundamentals of Deep Learning 5 Research direction and opportunities 6 Python libraries for ML and AI Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 19 / 55
21. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Classification Goal is to learn a mapping from inputs x to target y such that y ∈ {1 . . . k} where k is the number of classes. • If k = 2, this is called binary classification (in which case we often assume y ∈ {0, 1} • If k > 2, this is called multiclass classification. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 20 / 55
22. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Classification : Logistic regression Goal is to predict the binary target class y ∈ {0, 1}. Model is given by : ˆ y = σ(z) = 1 1 + e−z where z = wTx + b This function squashes the predictions to be between 0 and 1 such that : p(y = 1 | x, θ) = σ(z) and p(y = 0 | x, θ) = 1 − σ(z) Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 21 / 55
23. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Classification : Logistic regression Loss function : it is called crossentropy and defined as : LCE (ˆ y, y) = − log ˆ y if y = 1 − log(1 − ˆ y) if y = 0 • The crossentropy can be written in other form as : LCE (ˆ y, y) = −y log ˆ y − (1 − y) log(1 − ˆ y) • The cost function Jθ with respect to the model parameters θ is thus : Jθ = 1 N N i=1 LCE (ˆ y, y) = 1 N N i=1 −y(i) log ˆ y(i) − (1 − y(i)) log(1 − ˆ y(i)) Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 22 / 55
24. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Classification : Logistic regression Use gradient descent to solve the minimum cost function Jθ θt+1 = θt − α ∂Jθ ∂θ For parameter w and b : wt+1 = wt − α ∂Jθ ∂w bt+1 = bt − α ∂Jθ ∂b where : ∂Jθ ∂w = 1 N xT(ˆ y − y) ∂Jθ ∂b = 1 N (ˆ y − y) Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 23 / 55
25. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Multi-class Classification What about classification tasks with more than two categories ? • Targets form a discrete set {1, ..., K}. • It’s often more convenient to represent them as indicator vectors, or a one-of-K encoding : Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 24 / 55
26. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Multi-class Classification What about classification tasks with more than two categories ? • Targets form a discrete set {1, ..., K}. • It’s often more convenient to represent them as indicator vectors, or a one-of-K encoding : Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 24 / 55
27. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Multi-class Classification Model : softmax function ˆ yk = softmax(z1 . . . zk) = ezk k ezk where zk = j wkj xj + b Loss Function : cross-entropy for multiple-output case LCE (ˆ y, y) = − K k=1 yk log ˆ yk = −yT log ˆ y Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 25 / 55
28. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Multi-class Classification Cost funcion Jθ = 1 N N i=1 LCE (ˆ y, y) = −1 N N i=1 K k=1 yk log ˆ yk The gradient descent algorithm will be : wt+1 = wt − α ∂Jθ ∂w where ∂Jθ ∂w = 1 N xT(ˆ y − y) bt+1 = bt − α ∂Jθ ∂b where ∂Jθ ∂b = 1 N (ˆ y − y) Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 26 / 55
29. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Outline 1 Introduction to Machine Learning 2 Typical ML task : Linear Regression 3 Typical ML task : Classification 4 Fundamentals of Deep Learning 5 Research direction and opportunities 6 Python libraries for ML and AI Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 27 / 55
30. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals What is Deep Learning Deep Learning a subclass of machine learning algorithms that : • use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. • flexible models with any input/output type and size These algorithms may be supervised or unsupervised. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 28 / 55
31. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Deep Learning Success Speech Recognition Figure – https ://svail.github.io/mandarin/ Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 29 / 55
32. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Deep Learning Success Image caption Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 30 / 55
33. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Deep Learning Success Image painting Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 31 / 55
34. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Deep Learning Success Image classification Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 32 / 55
35. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Deep Learning Success Game Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 33 / 55
36. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Deep Learning Success Fashion Figure – Deep style Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 34 / 55
37. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Why Deep Learning and why now ? Why deep learning : Hand-Engineered Features vs. Learned features Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 35 / 55
38. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Why Deep Learning and why now ? Why now : • Large data-sets • GPU Hardware Advances + Price Decreases • Improved Techniques (Better algorithms & understanding) • Open source tools and models (Theano,Tensorflow, pytorch etc) Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 36 / 55
39. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals The Perceptron A perceptron classifier is a simple model of a neuron • Invented in 1954 by Frank Rosenblatt • Inspired by neurobiology The output : y = f (x) = g(z(x)) • pre-activation : z(x) = wx + b • activation function : g(.) • x, y input, output. • w, b weight and bias parameter θ Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 37 / 55
40. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals The Perceptron : Activation Function Importance of Activation Functions • Activation functions add non-linearity to our network’s function. • Most real-world problems + data are non-linear. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 38 / 55
41. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Multilayer Perceptrons (MLP) We can connect lots perceptron units together into a directed acyclic graph. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 39 / 55
42. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Multilayer Perceptrons (MLP) : Single hidden layer • Hidden layer pre-activation : z(x) = w(1)x + b(1) (z(x)i = j w(1) i,j xj + b(1) i ) • Hidden layer activation h(1)(x) = g(z(x)) • output layer activation f (x) = O w(2)h(1)(x) + b(2) where O(.) is the output activation usually softmax for classification problem. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 40 / 55
43. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Multilayer Perceptrons (MLP) : L hidden layers • layer pre-activation for k > 0 : z(k)(x) = w(k)h(k−1)x + b(k) • Hidden layer activation from 1 . . . L h(k)(x) = g(z(k)(x)) • output layer activation f (x) = h(L+1)(x) = O z(x)(x) Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 41 / 55
44. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Training Multilayer Perceptrons (MLP) Objective : Find parameters θ : w and b that minimize the cost function : arg max θ 1 N i L(f (x(i) : θ), y(i)) To train a neural net, we need • Loss function : L(f (x(i) : θ), y(i)) • A procedure to compute gradients : ∂Jθ ∂θ Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 42 / 55
45. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Training Multilayer Perceptrons (MLP) : Stochastic Gradient Descent (SGD Initialize θ randomly. For N epochs perform : • Randomly select a small batch of samples • Compute gradients : ∂Jθ ∂θ • Update parameters θ with update rule : θ(t+1) := θ(t) − α ∂Jθ ∂θ Stop when reaching criterion Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 43 / 55
46. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Training Multilayer Perceptrons (MLP) : Computing Gradients Backpropagation : an efficient way to compute partial derivatives of MLP. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 44 / 55
47. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Deep learning Architecture : Convolutional Neural Network • Enhances the capabilities of MLP by inserting convolution layers. • Composed of many “filters”, which convolve, or slide across the data, and produce an activation at every slide position • Suitable for spatial data, object recognition and image analysis. • The common usage of CNN : self driving cars, drones, computer vision, text analytics Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 45 / 55
48. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Deep learning Architecture : Recurrent Neural Networks (RNN) • Have recurrent memory loops which take the input from the previous and/or same layers or states. • Have unique capability to model along the time dimension and arbitrary sequence of events and inputs. • Suitable for sequenced data analysis such as time-series, sentiment analysis, NLP, language translation, speech recognition, image captioning, and script recognition. • Common type : LSTM and GRUs. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 46 / 55
49. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Deep learning Architecture : Deep Generative models Idea :learn to understand data through generation → replicate the data distribution that you give it. Two types : Variational Autoencoders (VAE), and Generative Adversarial Networks(GAN). • Can be used to generate Musics, Speach, Langauge, Image, Handwriting, Language • Suitable for unsupervised learning as they need lesser labelled data to train. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 47 / 55
50. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Outline 1 Introduction to Machine Learning 2 Typical ML task : Linear Regression 3 Typical ML task : Classification 4 Fundamentals of Deep Learning 5 Research direction and opportunities 6 Python libraries for ML and AI Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 48 / 55
51. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Research direction • Unsupervised deep learning. • Add more reasoning (uncertatinity) abilities in deep learning models (deep-probabilistic models) • Many applications which are under-explored especially in developing countries. • Deep reiforcement learning. • Computational efficiency. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 49 / 55
52. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Outline 1 Introduction to Machine Learning 2 Typical ML task : Linear Regression 3 Typical ML task : Classification 4 Fundamentals of Deep Learning 5 Research direction and opportunities 6 Python libraries for ML and AI Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 50 / 55
53. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Jupyter Jupyter : Open-source web application for interactive and exploratory computing. • Allows to create and share documents that contain live code, equations, visualizations and explanatory text. • It is a platform for Data Science at scale. • Covers all the life-cycle of scientific ideas :ideas to publications. • Demo Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 51 / 55
54. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Scikit-Learn for ML Scikit-Learn (sklearn) is Python’s premier general-purpose machine learning library. Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 52 / 55
55. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Python ML and AI libraries Tensorflow Theano Pytorch Keras Edward PyMC3 NLTK Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 53 / 55
56. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals Data Science Platform Kaggle : helps you learn, work, and play. Data set : • Academic Torrents • UCI Machine learning repository Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 54 / 55
57. Introduction to Machine Learning Typical ML task : Linear Regression Typical ML task : Classification Fundamentals THANK YOU Anthony FAUSTINE sambaiga@gmail.com pythontz 05th OCTOBER 2017 55 / 55