Machine Learning and Deep Learning tutorial - Learning to create Deep Learning applications
Machine Learning and Deep Learning is one of the fastest growing fields these days. Companies are looking for professionals having experience in these technologies. If you want to make career in this highly paid field then you should learn and practice it very well. We are giving many tutorials and examples in this section. Our tutorials will help you in learning and mastering it well.
We will provide you many articles, code snippet and projects in the field of Machine Learning. These materials will help you in learning and mastering the core concepts in-depth.
So, What should you know before starting machine learning?
Machine learning is a board field and it requires experience in many fields including Python, Java, Big Data and Mathematics. So, you should have solid programming experience in Java, Scala, Python or any other programming language. Python programming language is preferred programming language for developing machine learning and artificial intelligence model. So, if you are planning to run programming languages then learn Python in detail. You can check our tutorials at Python tutorials section.
You should also learn Mathematics before starting machine learning and deep learning programming.
1. Basics of Machine Learning programming
In order to start machine learning programming you should have prior experience in many programming, database and visualization technologies.
Python for Data Analysis:
First of all developer should learn the basics of Python programming language and make yourself well versed in Python programming language. Topics to master in Python are:
- Programming syntax of Python
- Writing examples to learn all basics of Python
- You must get get acquainted with Data Structures, Object Oriented Programming and Data Manipulation
- Learn the Data Visualization libraries in Python
Introduction to SQL:
In Data Analytics and Data Science programming you have to work with the large amount of data which should be retrieved from various stores includes SQL stores. So, developer must learn SQL queries for querying data from underlying databases. You can learn SQL at:
Math for Data Analysis:
Mathematics is heart of Machine Learning and Deep Learning projects. Various mathematical algorithms are used to solve define solutions for a business problem. Then programming languages are used to develop and train machine learning models. So, knowledge of Mathematics is must of become a good data scientist. You should learn at least following topics in Mathematics:
- Linear Algebra - This includes Scalar, Vector, Matrices and Tensors. You should fresh your knowledge and understand its application in Data Analysis
- Probability and Statistics
- Calculus
- Formulation of machine learning algorithm
Mathematics knowledge is important in Data Science, if you have in-depth knowledge of Mathematics then you can design better model and also tweak it in case model is not performing as expected. Understanding mathematical algorithm is required to tweak training parameters and make model better.
2. Statistics Essentials
Statistics plays an important role in developing machine learning and deep learning models. Here are the topics you should master in Statistics.
Inferential Statistics:
- Probability Distribution Functions
- Random Variables
- Sampling Methods
- Central Limit Theorem and others as per your project requirements
Hypothesis Testing:
In the Hypothesis testing you should learn how to formulate and test hypotheses to solve business problems.
Exploratory Data Analysis:
Learn the skills to summarize data sets and derive initial insights. You can also learn the tools used for data visualization.
3. Machine Learning
You must learn following topics in Machine Learning:
- Linear Regression: Develop in-depth
programming skills in developing linear regression models.
- Supervised Learning: Important algorithms
learn are Naive Bayes and Logistic Regression
- Unsupervised Learning: In unsupervised machine learning important algorithms to learn are K-Means and Hierarchical clustering.
- Support Vector Machines: You learn to classify data points
using support vectors.
- Decision Trees: Various decision tree algorithms are used in machine learning so you should learn the fundamentals and its implementation using any of the programming language of your choice.
4. Natural Language Processing
The Natural Language Processing (NLP) is one of the most uses of Machine Learning these days. Here machine learning and deep learning models are developed to process vast collection of unstructured data. NLP is used to process twitter, facebook and many other text format data to understand users reviews. Here following main things can be learned:
- Basics of text processing: Learn basics of Natural
language processing with Python or any other programming language. Understand
various software packages, libraries and tools used for the
process of text data.
- Lexical processing: Understanding the
lexical processing is very important towards learning NLP and
here you have to learn to extract features from
unstructured text. You have to develop machine learning models to extract the
information from text data.
- Syntax and Semantics: The semantic meaning
and sentiment analysis is another area where NLP is widely used.
To become a good Data Scientist you must learn these
technologies.
- Other areas text analytics: There are other areas where text analysis can be used and you must explore application of Natural Language Processing in other industries.
Right skills in Natural Language Processing are must for data scientist as the amount of unstructured text is increasing at very high speed. So, new methodology needs to be adopted fast for developing text data processing programs in coming days.
5. Neural Networks & Deep Learning
The Neural Networks and Deep Learning are the topics of very high importance in today's machine learning scenario. These technologies are used worldwide for creating intelligent applications for industry, education, agriculture, drug research, medical science and defense. There is a big demand of skilled professionals in these technologies. Here in this section we will teach you Neural Networks and Deep Learning with many examples.
Information flow in a neural network: First of all you must the components and structure of artificial neural networks. You should learn how you can design your own neural network and then write programs.
Training a neural network: Once you designed and coded your neural network the next step is to train it. You will learn how to prepare data and train your neural network with latest cutting-edge techniques.
Convolutional Neural Networks: You should learn how to use the CNN's to solve complex image/video/audio classification problems for your business.
Recurrent Neural Networks: The RNN is also very popular and used by data scientists and researches for various projects. It is also used for text analytics.
LearnTensorflow and keras: You should also learn to build and deploy industry grade applications with the latest TensorFlow and Keras deep learning libraries.
6. Graphical Models
Directed and Undirected Models: You should learn the power of Probabilistic Graphical models as this topic is also very useful and widely used.
Inference: Learn how graphical models for drawing inferences using datasets.
Learning: You must also learn to estimate parameters and structure of graphical models 0
7. Reinforcement Learning
The reinforcement learning is another big field of machine learning which is used in many scenarios.
Introduction to RL: Learn the basics of reinforcement learning and its applications in development of industry applications.
Markov Decision Processes: Learn about the Model processes. 1
Q-learning: The Q-learning is part reinforcement learning algorithm which is used to learn a policy. It teaches an agent to take an action based on the circumstances. This is also used in industry very extensively.