Data Science Training in Mohali with Itronix Solutions School of Analytics. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
Itronix Solutions is one of the best training institute in Mohali and Chandigarh for Machine Learning. The course offered by Itronix Solutions covers exactly how to acquire practical hands-on Skills in the easiest, fastest and cheapest way possible. Students will be trained under highly qualified experts and industry practitioners.
BIG DATA AND MACHINE LEARNING TRAINING with ITRONIX SOLUTIONS
➤In collaboration with IBM, a global leader in technology-driven solutions
➤160 hour course, covering Deep and Machine Learning, Hadoop, Spark and Python
➤Free Access to IBM’s Cloud Platforms featuring Cognitive Classes and IBM Watson
➤Delivered in Classroom or Online Formats
Data Science and Analysis
Learn about representing data through various data structures, extracting meaningful information from data and developing a visual understanding of problems using data
Develop a geometric intuition of various ideas in linear algebra and transition to its computational applications
Probability and Statistics
Understand “chance” and it’s association with statistics. Transition to computational science to solve problems
Develop a meaningful understanding of rate of change and understand the importance of this in machine learning by looking at some common applications
Linear optimization discussing hill climbing, genetic algorithms and simulated annealing from a computational viewpoint
Discover foundational principles of machine learning using linear models and Support Vector machines for classification and regression tasks
Moving forward, I make the assumption that you are not an expert in:
- Machine learning
- Any of Python’s machine learning, scientific computing, or data analysis libraries
It would probably be helpful to have some basic understanding of one or both of the first 2 topics, but even that won’t be necessary; some extra time spent on the earlier steps should help compensate.
Step 1: Basic Python Skills
If we intend to leverage Python in order to perform machine learning, having some base understanding of Python is crucial. Fortunately, due to its widespread popularity as a general-purpose programming language, as well as its adoption in both scientific computing and machine learning, coming across beginner’s tutorials is not very difficult. Your level of experience in both Python and programming, in general, are crucial to choosing a starting point.
Step 2: Foundational Machine Learning Skills
Like almost anything in life, required depth of theoretical understanding is relative to practical application. Gaining an intimate understanding of machine learning algorithms is beyond the scope of this article, and generally requires substantial amounts of time investment in a more academic setting, or via intense self-study at the very least.
The good news is that you don’t need to possess a PhD-level understanding of the theoretical aspects of machine learning in order to practice, in the same manner that not all programmers require a theoretical computer science education in order to be effective coders.
Step 3: Scientific Python Packages Overview
Alright. We have a handle on Python programming and understand a bit about machine learning. Beyond Python there are a number of open source libraries generally used to facilitate practical machine learning. In general, these are the main so-called scientific Python libraries we put to use when performing elementary machine learning tasks (there is clearly subjectivity in this):
- numpy – mainly useful for its N-dimensional array objects
- pandas – Python data analysis library, including structures such as dataframes
- matplotlib – 2D plotting library producing publication quality figures
- scikit-learn – the machine learning algorithms used for data analysis and data mining tasks
Step 4: Getting Started with Machine Learning in Python
- Machine learning fundamentals.
Step 5: Machine Learning Topics with Python
With a foundation having been laid in scikit-learn, we can move on to some more in-depth explorations of the various common, and useful, algorithms. We start with k-means clustering, one of the most well-known machine learning algorithms. It is a simple and often effective method for solving unsupervised learning problems:
Step 6: Advanced Machine Learning Topics with Python
We’ve gotten our feet wet with scikit-learn, and now we turn our attention to some more advanced topics. First up are support vector machines, a not-necessarily-linear classifier relying on complex transformations of data into higher dimensional space.
- Support Vector Machines, by Jake VanderPlas
Next, random forests, an ensemble classifier, are examined via a Kaggle Titanic Competition walk-through:
- Kaggle Titanic Competition (with Random Forests), by Donne Martin
Dimensionality reduction is a method for reducing the number of variables being considered in a problem. Principal Component Analysis is a particular form of unsupervised dimensionality reduction:
- Dimensionality Reduction, by Jake VanderPlas
Before moving on to the final step, we can take a moment to consider that we have come a long way in a relatively short period of time.
Using Python and its machine learning libraries, we have covered some of the most common and well-known machine learning algorithms (k-nearest neighbors, k-means clustering, support vector machines), investigated a powerful ensemble technique (random forests), and examined some additional machine learning support tasks (dimensionality reduction, model validation techniques). Along with some foundational machine learning skills, we have started filling a useful toolkit for ourselves.
Step 7: Deep Learning in Python
The learning is deep.
Deep learning is everywhere! Deep learning builds on neural network research going back several decades, but recent advances dating to the past several years have dramatically increased the perceived power of, and general interest in, deep neural networks. If you are unfamiliar with deep learning, KDnuggets has many articles detailing the numerous recent innovations, accomplishments, and accolades of the technology.
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