Machine Learning

Today we are probably living in the most defining period of human history. The period has witnessed computing moving from large mainframes to PCs to cloud. The democratization of the tools and techniques used resulted in boost in computing. Today, the data scientists can build data crunching machines with complex algorithms. Machine learning is that technology which has enabled computers to get smarter and more personal.

Machine learning is a field where statistical techniques are used to give computer systems the ability to "learn" with data, without being explicitly programmed. It is the direct application of artificial intelligence (AI) where computer programs are developed that can access data and use it to learn for them. The process of learning starts with data observation and patterns in data are looked and learnt to make better decisions in the future based on the examples provided.

The self-driving cars, practical speech recognition, effective web search and a vastly improved understanding of the human genome are the few implications of machine learning. It is so pervasive today that we use it dozens of times a day without knowing it.

Our course provides the learning about the most effective machine learning techniques, gain practice in implementing them and getting them to work. We’ll also give the practical know-how needed to quickly apply these techniques to new problems. Finally, you’ll also learn about some of the Silicon Valley's best practices in innovation pertaining to machine learning and AI.

Our course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. The topics we have covered are:(i) supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks) (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning) (iii) Best practices in machine learning (bias/variance theory, innovation process in machine learning and AI).

The course draws from numerous case studies and applications. The  algorithms are used to help learn in building smart robots (with perception and control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Machine learning algorithms

  • Supervised machine learning algorithms applies the past learning to new data using labeled examples to predict future events. It consist of a target / outcome variable (dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, a function is generated to map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Examples: Regression, KNN, Logistic Regression, Decision Tree, Random Forest etc.
  • Unsupervised machine learning algorithms are used when the training information is neither classified nor labeled. Here there is no target or outcome variable to predict / estimate.  It is used for population clusters in different groups and also for segmenting customers in different groups for specific intervention. Examples: Apriori algorithm, K-means.
  • Semi-supervised machine learning algorithms uses both labeled and unlabeled data for training. Typically a small amount of labeled data and a large amount of unlabeled data is used. The learning accuracy of the system is more using this method. It is chosen when the acquired labeled data requires skilled and relevant resources in order to learn from it. Otherwise, additional resources are not required.
  • Reinforcement machine learning algorithms is a method where the system interacts with its environment by producing actions and discovering errors or rewards. The machine is exposed to an environment where it trains itself continually using trial and error. On the basis of past experience and learning the machine tries to capture the best possible knowledge to make accurate business decisions. Example: Markov Decision Process

The machine learning can be understood by designing and completing small projects. Python machine learning is a popular and powerful way of doing so. The complex machine learning algorithms can be easily run using python codes. Python is a complete language and platform that can be used for both research and development and developing production systems. Also there are a lot of modules and libraries to choose from which actually provides multiple ways to do each task. The best way to get started with Python for machine learning is to complete a project. Machine learning using python gives a bird’s eye view of the small projects where books and courses are very frustrating.

The machine learning is applied to the datasets end-to-end and the key steps i.e., loading data, summarizing data, evaluating algorithms and making some predictions are covered. This gives us a template that can be used on dataset after dataset. This help filling in the gaps such as further data preparation and improving result tasks once we gain confidence.

Machine learning enables analysis of massive quantities of data. Python can be leveraged to perform machine learning and the task becomes easier. However, in order to use python with machine learning the basic understanding of python is required. There are many modules to help the programmers with machine learning using python. The computer is trained using a given data set to predict the properties of a given new data. This process of training the computer for accurate predictions involves the use of specialized algorithms. The training data is fed to python algorithm which uses it to give predictions on a new test data. Combining machine learning with artificial intelligence and cognitive technologies we can make it even more effective in processing large volumes of information.


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