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Tutorials
on
Machine learning
&
Deep-Learning
Machine-Learning tutorials:
6 REGRESSIN MODELS
7 CLASSIFICATION MODELS
2 CLUTERING
1 ASSOCIATION RULE LEARNING
2 REINFORCEMENT LEARNING
1 Natural Language processing
Deep-Learning tutorial
Artificial Neural Networks (ANN)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Self Organizing Maps (SOM)
Boltzmann Machines
AutoEncoders
Tutorials on
Machine learning & Deep-Learning
Description
Kirill Eremenko
This Book is form the courses of two professional Data Scientists from
Hadelin de Ponteves
SuperDataScience and from BlueLife AI.
You can get a quick overview on Machine Learning & Deep Learning from this book.
Also this book will be the best guide for the Courses of Kirill Eremenko and Hadelin de Ponteves. It can
also helpful for Andrew Ng's Machine learning course.
This book will help you learn complex theory, algorithms and coding libraries in a simple way (as a beginner).
It will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and
improve your understanding of this challenging yet lucrative sub-field of Data Science.
Requirements?
Just some high school mathematics level.
What you can get from this Book?
Master Machine Learning on Python
Have a great intuition of many Machine Learning models
Make accurate predictions
Make powerful analysis
Make robust Machine Learning models
Create strong added value to your business
Use Machine Learning for personal purpose
Handle specific topics like Reinforcement Learning, NLP and Deep Learning
Handle advanced techniques like Dimensionality Reduction
Know which Machine Learning model to choose for each type of problem
Build an army of powerful Machine Learning models and know how to combine them to solve any
problem
What is the target audience?
Anyone interested in Machine Learning.
Students who have at least high school knowledge in math and who want to start learning
Machine Learning.
Any intermediate level people who know the basics of machine learning, including the classical
algorithms like linear regression or logistic regression, but who want to learn more about it
and explore all the different fields of Machine Learning.
Any people who are not that comfortable with coding but who are interested in Machine
Learning and want to apply it easily on datasets.
Any students in college who want to start a career in Data Science.
Any data analysts who want to level up in Machine Learning.
Any people who are not satisfied with their job and who want to become a Data Scientist.
Any people who want to create added value to their business by using powerful Machine
Learning tools.
After reading/using this Book you can dive deeper in Machine-Learning or Deep-Learning.
0.1 Machine Learning TOPICS
[0] Data Preprocessing
Data Preprocessing in Python
[1] Regression
i. Simple Linear Regression
ii. Multiple Linear Regression
iii. Polynomial Regression
iv. Support Vector Regression (SVR)
v. Decision Tree Regression
vi. Random Forest Regression
vii. Evaluating Regression Models Performance
[2] Classification
i. Logistic Regression
ii. K-Nearest Neighbors (K-NN)
iii. Support Vector Machine (SVM)
iv. Kernel SVM
v. Naive Bayes
vi. Decision Tree Classification
vii. Random Forest Classification
viii. Evaluating Classification Models Performance
[3] Clustering
i. K-Means Clustering
ii. Hierarchical Clustering
[4] Association Rule Learning
i. Apriori
ii. Eclat
[5] Reinforcement Learning -
i. Upper Confidence Bound (UCB)
ii. Thompson Sampling
[6] Natural Language Processing
0.2 Deep Learning TOPICS
[7] Artificial Neural Networks (ANN)
i. ANN Intuition
ii. Building an ANN
[8] Convolutional Neural Networks (CNN)
i. CNN Intuition
ii. Building a CNN
[9] Dimensionality Reduction
i. Principal Component Analysis (PCA)
ii. Linear Discriminant Analysis (LDA)
iii. Kernel PCA
[10] Model Selection & Boosting
i. Model Selection
ii. XG Boost
iii. Bonus Lectures
[11] Recurrent Neural Networks (RNN)
i. RNN Intuition Building a RNN
ii. Evaluating and Improving the RNN
[12] Self Organizing Maps (SOM)
i. SOMs Intuition Building a SOM
ii. Mega Case Study
[13] Boltzmann Machines
i. Boltzmann Machine Intuition
ii. Building a Boltzmann Machine
[14] AutoEncoders
i. AutoEncoders Intuition
ii. Building an AutoEncoder
Chapter 0 : Part 1
Usage of ML
Get exited, Inspired, Amount of data and how to access them.
0.1 Applications of Machine Learning
[1] Facial Recognition: Eg: facebook.
[2] Games without joystick. Recognize action, movement for game control.
Random forest is udsed.
[3] In VR movement. ML algorithm detecting your movement.
[4] Text to speech, voice recognition, voice typing
[5] Robot companion.
Reinforcement learning used.
[6] Recommender system, or a recommendation system: Amazon, Netflix, audible. Facebook ads, Youtube video
suggestion.
[7] Medicines
[8] In space-satellite for recognize certain aria of earth. Image processing.
[9] Explore other planet, Space-Robot.
0.2 ML is the Future
Data is everywhere:
Data exhaust. 1 letter = 1byte
1 page = 1 kilo byte, 1000 letter
1 book = 1000 page, 1 mega byte
1 DNA, Gnome = 1000 books, 1 giga byte
1 persons life (80 year) as video(hd) = 1 tb
1 Amazon Rainforest 700, 000 000 000 trees, as paper and fill those page = 1
petabyte
1 Exabyte = 1000 petabyte as whole world.
Exponential growth of data:
We need ML to explore that high amount of data. Only ML-Algorithms can
explore these data.
Chapter 0 : Part 2
ML : What? Why? How?
Understanding the concepts.
0.3 What Is Machine Learning?
Machine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from
experience.
Machine Learning: Machine learning algorithms use computational methods to “learn” information directly from data without
relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of
samples available for learning increases. Deep learning is a specialized form of machine learning.
0.4 Why Machine Learning Matters
With the rise in big data, machine learning has become a key technique for solving problems in areas, such as:
i). Computational finance, for credit scoring and algorithmic trading
ii). Image processing and computer vision, for face recognition, motion detection, and object detection
iii). Computational biology, for tumor detection, drug discovery, and DNA sequencing
iv). Energy production, for price and load forecasting
v). Automotive, aerospace, and manufacturing, for predictive maintenance
vi). Natural language processing, for voice recognition applications
Machine learning algorithms find natural patterns in data that generate insight and help you make better decisions
and predictions. They are used every day to make critical decisions in medical diagnosis, stock trading, energy load forecasting,
and more.
For example, media sites rely on machine learning to sift through millions of options to give you song or movie
recommendations. Retailers use it to gain insight into their customers’ purchasing behavior.
When Should You Use ML: Consider using machine learning when you have a complex task or problem involving a large amount of
data and lots of variables, but no existing formula or equation. For example, machine learning is a good option if you need to
handle situations like these:
Hand-written rules and equations are too complex—as in face recognition and speech recognition.
The rules of a task are constantly changing—as in fraud detection from transaction records.
The nature of the data keeps changing, and the program needs to adapt—as in automated trading, energy demand forecasting,
and predicting shopping trends.
0.5 How Machine Learning Works
Walk through the three types of machine learning (clustering, classification, and regression). Machine learning uses two
types of techniques:
[1] Supervised learning: which trains a model on known input and output data so that it can predict future outputs,
[2] Unsupervised learning: which finds hidden patterns or intrinsic structures in input data.
Machine learning
SUPERVISED LEARNING UNSUPERVISED LEARNING
Develop predictive model based on both input and output data Group and interpret data based only on input data
CLASSIFICATION REGRESSION CLUSTERING
0.6 Supervised Learning
Supervised Learning: Supervised machine learning builds a model that makes predictions based on evidence in the presence of
uncertainty.
A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model
to generate reasonable predictions for the response to new data.
Use supervised learning if you have known data for the output you are trying to predict.
Techniques for SUPERVISED LEARNING: Supervised learning uses CLASSIFICATION and REGRESSION techniques to develop
machine learning models.
0.6.1 Classification
Classification: Classification techniques predict discrete responses—for example, whether an email is genuine or spam,
or whether a tumor is cancerous or benign.
Classification models classify input data into categories. Typical applications include medical imaging, speech
recognition, and credit scoring.
Use classification if your data can be tagged, categorized, or separated into specific groups or classes. For example,
applications for hand-writing recognition use classification to recognize letters and numbers.
In image processing and computer vision, Unsupervised Pattern Recognition techniques are used for object detection
and image segmentation.
Common CLASSIFICATION Algorithms: Common algorithms for performing classification include
[1] Support Vector Machine (svm),
[2] Boosted and Bagged DECISION TREES,
[3] K-Nearest Neighbor,
[4] Naïve bayes,
[5] Discriminant analysis,
[6] Logistic Regression, and
[7] Neural Networks.
0.6.2 Regression
Regression: Regression techniques predict continuous responses—for example, changes in temperature or fluctuations in
power demand. Typical applications include electricity load forecasting and algorithmic trading.
Use regression techniques if you are working with a data range or if the nature of your response is a real number, such as temperature
or the time until failure for a piece of equipment.
Common REGRESSION Algorithms: Common regression algorithms include
[1] Linear model,
[2] Nonlinear model,
[3] Regularization,
[4] Stepwise Regression,
[5] Boosted and Bagged Decision Trees,
[6] Neural Networks, and
[7] Adaptive Neuro-Fuzzy Learning.
Example of supervised learning:
Using Supervised Learning to Predict Heart Attacks: Suppose clinicians want to predict whether someone will have a heart attack
within a year. They have data on previous patients, including age, weight, height, and blood pressure. They know whether
the previous patients had heart attacks within a year. So the problem is combining the existing data into a model that can predict
whether a new person will have a heart attack within a year.
0.7 Unsupervised Learning
Unsupervised learning finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets
consisting of input data without labeled responses.
Clustering
Clustering is the most common unsupervised learning technique. It is used for exploratory data analysis to find hidden patterns or
groupings in data. Applications for cluster analysis include Gene Sequence analysis, Market Research, and Object Recognition.
Example:
For example, if a cell phone company wants optimize the locations where they build cell phone towers, they can use machine
learning to estimate the number of clusters of people relying on their towers. A phone can only talk to one tower at a time, so the
team uses Clustering Algorithms to design the best placement of cell towers to optimize Signal Reception for groups, or
clusters, of their customers.
Common CLUSTERING algorithms:
[1] K-Means and K-Medoids,
[2] Hierarchical Clustering,
[3] Gaussian Mixture models,
[4] Hidden Markov Models,
[5] Self-Organizing Maps,
[6] Fuzzy C-Means Clustering, and
[7] Subtractive Clustering.
0.8 Which Machine Learning Algorithm to Use?
Choosing the right algorithm can seem overwhelming—there are dozens of supervised and unsupervised machine learning algorithms,
and each takes a different approach to learning.
There is no best method or one size fits all. Finding the right algorithm is partly just trial and error—even highly experienced
data scientists can’t tell whether an algorithm will work without trying it out.
Algorithm selection also depends on the size and type of data you’re working with, the insights you want to get from the data,
and how those insights will be used.
Here are some guidelines on choosing
between Supervised and Unsupervised
machine learning:
Choose Supervised Learning if you
need to train a model to make a
Prediction—for example, the future
value of a continuous variable, such
as temperature or a stock price, or a
classification—for example,
identify makes of cars from webcam
video footage.
Choose unsupervised learning if
you need to explore your data and
want to train a model to find a good
internal representation, such as
splitting data up into clusters.
