Training & Internship

Best Machine Learning Training for beginners

Summary

This article deals with the domain of Machine Learning, its objectives, and technicalities, how to start learning from scratch the technology of Machine Learning, how easy or difficult is it to embark on this recent development, why is the concept of Machine Learning important and what are the best Machine Learning Training for Beginners and Engineering students.

Machine Learning is an umbrella term that can be applied to many different concepts and techniques. According to Indeed, Machine Learning has shown a growth of 344% in the year 2019 with an average base salary of $416,085 per year. You need to familiarize yourself with the different forms of model analysis, variables, and algorithms in order to understand Machine Learning.

In simpler terms, Machine Learning is basically a “Computer mimicking a Human” or the development of a “Humanoid”. It is enabling a computer to carry out tasks without feeding it with line-by-line instructions to do so. The computer is itself capable of analyzing patterns within the data and then generalizing these patterns to new data. It understands from the user actions and implements it accordingly in its applications.

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How do I start learning ML?

Frankly speaking, there is no set way to become a full-fledged and immensely talented Machine Learning Engineer. However, choosing the best Machine Learning Trainings is the primary key to unlock the world to this amazing technology. With proper guidance and expert training, gaining knowledge in this area will be a cakewalk!

However, the below-mentioned skills are a prerequisite in developing a successful career in Machine Learning.

  1. Knowledge of Python Programming is a must. Python is the most preferred language for Machine Learning followed by R. In fact, there are numerous Python libraries such as Keras, TensorFlow, Scikit-learn, etc. which are precisely useful in this domain. Therefore, having relevant coding skills prior to the start of Machine Learning Training is imperative.
  2. You need to have a decent knowledge of Multivariable Calculus, Linear Algebra, and Statistics. If your focus is R&D in Machine Learning, then you have to implement many algorithms from scratch, so mastery of Calculus is important. Otherwise, 80% of your time as an ML Expert will go into data analysis. Bayesian Thinking is an important part of Machine Learning which deals with certain topics like Conditional Probability, Priors, and Posteriors, etc.
  3. Familiarize yourself with the various term associated with Machine Learning Technology and its types. Apart from this, you need to practice Machine Learning algorithms a lot! The most time-consuming part is actually data collection, integration, cleaning and pre-processing. Ensure that you improve upon yourself in this domain. Also, learn different models and practice on real data sets.
  4. Join Machine Learning Training for Beginners and Engineers. The courses (both free and paid versions!) are available online as well as in various Data Learning Centres.
  5. You must be updated with the various Machine Learning algorithms like K-nearest Neighbour, Naïve Bayes, Decision trees, Linear Regression, Logistic regression, etc. In order to develop models, knowledge of coding is required. Once of start to imbibe the basic concepts, marching towards advanced terminologies won’t be a hassle!
  6. Take part in competitions like Titanic: Machine Learning Disaster challenge, Digit Recognizer Project, etc. after you have fully understood the basis of ML Technology. This will help you to implement your theoretical knowledge to gain hands-on experience and will make you more proficient.

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Is Machine Learning hard to learn?

Look, getting started with anything new is hard, although the extent varies accordingly. If you do not begin with it properly, it will definitely start to bug you. The key factor in making the learning process enjoyable is to start learning layer by layer. First, master the basics and then march forward to advance concepts.

First, making a big picture of the problem in your head is important. Then ask yourself questions like how are you going to solve it and whether the problem is being solved satisfactorily. Then you delve deeper into the individual components, getting more intuition of each. After this, you should decipher the associated terminologies.

For example: In linear regression, the function class is linear and the evaluation method is square loss. In linear SVM, the function class is linear and the evaluation method is hinge loss, etc. First, understand these algorithms at the high-level. Then only delve into the technical details. 

Learning how to use machine learning isn’t much harder than learning any other set of libraries for a programmer. The key is to focus on using it, not designing the algorithm. Consider it this way: if you want to sort data, you don’t invent a sort algorithm, you pick an appropriate algorithm and use it right.

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Why is Machine Learning important?

Why is Machine Learning important?

Apparently, a lot of companies are investing heavily in AI and Machine Learning. These investments are broadly driven by the three fundamental forces:

  1. The number of devices that are connected to the internet and emitting data on a regular basis. These devices like your mobiles, laptops, smartwatches are basically capturing data and sending them on Cloud. There would be 50 billion such devices by the year 2020. Machine Learning Technology will help these devices yield better decisions.
  2. The cost of storing data has gone down significantly. What used to cost us a million dollars back in the 1980s, now just costs us a few cents when it comes to data storage.
  3. The cost of computations has also dropped. With the advent of Cloud Technology, there is no limit to how much computations you run, at a very low price.

These are the three forces which are driving Machine Learning as a Technology and no matter in which field you are, these technologies would disrupt each and every domain in the upcoming 10-15 years. In fact, recent technological developments like Amazon Go- the world’s most advanced shopping technology, no lines, no checkout, just grab and go! And Seeing AI developed by Microsoft is based on Deep Learning and AMIDST Processing technologies and has already made lives easier. So, if you have to make a decision about your career, this is one of the hottest domains at present.

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Best Machine Learning training for beginners

Below enlisted are some of the trending machine learning training for beginners:

1. Fraud Detection using Machine Learning: In this machine learning training course you will learn how to use ML to extract and analyse past banking data to figure out fraudulent card transactions. You will learn various algorithms and develop a Fraud Detection project using Python. You will gain knowledge about concepts such as Mean squared error function, Linear Regression, and its types, etc. in the process.

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2. Machine Learning using Python: This machine learning course will definitely be your best start to embark on this new field. You will get to learn about building a project that predicts the price of a house by taking data about other houses in the locality. By developing this project, you will learn about ML algorithms, Importing and Processing a Dataset, Anaconda, Jupyter, pandas, etc.

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3. Movie Recommendation using ML: As part of this course you would be able to develop an ML Model to recommend movie titles based on the user’s viewing history.

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4. Handwritten Digits Recognition using ML: In this course, you will use Deep Learning and Neural network to predict the handwritten digits using MNIST dataset. This ML model has various applications in recognizing the number plates of vehicles, processing bank cheque amounts, etc.

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These were some of the best machine learning training for beginners. If you are very much interested in machine learning and want to develop skills in this technology, you can check out our programs on machine learning.

When you listen to some thought-leaders in the computer industry, like Jeff Bezos and Sundar Pichai, you would hear them say that we are still in the Phase-1 of AI and Machine Learning. This technology is being continuously employed in various sectors like pattern and image recognition, Predictive Analysis, etc. Hence, the domain and scope of Machine Learning keep on increasing day-by-day.

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Best Machine Learning Training for beginners
Skyfi Labs Last Updated: 2020-02-19





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