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Quality Score

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Overall Score : 84 / 100

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Course Description

Let me begin by telling secrets of mastery of machine learning. # Secret 1 - The overall secret is machine learning is to know what not to learn. Given the amount of information in machine learning it is important to focus on important concepts and not get distracted. #Secret 2 - The requirement of maths and statistics is very shallow. In general people think that to master machine learning one needs to know lot of maths and statistics. That is not true. When it comes to applying machine learning, the knowledge of maths and statistics is limited. The way to think about this to compare with knowledge of database indexes. You need to master the best practices of using database indexes. You don't need to know how databases indexes algorithms work. The same holds for machine learning concepts.

#Secret 3 - The key skill to master machine learning is fine tuning. Any experienced ML expert will tell you that the maximum time that goes in taking machine learning problems to production is optimisation. Hence ,is important to understand terms like overfitting ,underfitting sensitivity, specificity, precision, ROC, AUC. The course spends lot of time on these key fundamental concepts. Also the likes of Google and Amazon are producing tools like AutoML where the requirement of coding is close to zero. But what is still required are the fundamental concepts. The world of tomorrow of data science is less of coding but more key concepts.

A journey of thousand miles begins with first step. You always wanted to learn machine learning but many factors stopped you - fear of Maths , Statistics , the complexity of subject. Today is the day to break away from those fears. Enrol in the machine learning course and see for yourself that mastering machine learning can be simplified. Following are topics the course covers. The course uses Google Python notebooks. You see the code results immediately.
  • Fundamentals of machine learning - Cost Functions, Labelled and Unlabelled data, Feature weights, Training and Testing Cross Validation.
  • Feature Engineering - Normalization, Standardization
  • Linear Regression
  • Classification - Concepts about True Positive, True Negative, Sensitivity, Specificity, Precision, ROC, AUC, Confusion Matrix
  • KNN - Algorithm
  • OverFitting and UnderFitting
  • Regularization
  • Decision Trees - Entropy, Information Gain
  • Bagging and Boosting
  • Unsupervised Learning - K-Means
  • Deep Learning - Weights, Bias, Epochs, Gradient Descent,Batch, Stochastic Gradient Descent , Mini Batch
Appendix course on Numpy and Pandas have also been added. Following are essential points before taking the course
  • A good knowledge of Python, Numpy and Pandas is required. Please don't proceed with the course unless you master it.
  • You need to be patient. Please be prepared to spend two to four months to digest these concepts if you are completely new to machine learning.

Who this course is for:
  • People interested about data science

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Instructor Details

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Vishal Singh has 20+ years of IT experience. He has seen software from the FoxPro and Clipper days. He has hands on experience in designing ML systems. Has worked extensively on machine learning NLP problems in bio science for gene extraction from text. The course design is based on his personal experiences of mastering machine learning

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Reviews

4.2

14 total reviews

5 star 4 star 3 star 2 star 1 star
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By Wenderson Alexandre de Sousa Silva

Há muita redundância nas informações passadas e alguns erros nas legendas.

By Rohan sahu

Its good

By Amit Patel

Simple byte size ML concept

By Amol Anant Alim

its a very nice explanation of all concepts related to ML

By Rashmi Gupta

So far ok. The basics are covered well. What would have helped is a video on what to focus and what no to in more detail.

By Jay B S

Yes, Extremely USEFUL to understand such a subject which others make most complex unnecessarily. No words to Thank Mr. Vishal Singh for this great effort as making complex things simple understand takes great effort, Nobel intention and maturity of subject.

By UDAYA BHASKARAN

Great course

By Aamer Syed

The instructor had presented ML/DL in a very interesting way.

The content being bite sized is easy to grasp

Thank you!

By Alok Agarwal

The course is a very good starter course for a beginner. The concepts are well explained in simple maths.

By Arnab Jana

nice experience, great lectures; it would have been great if svm theory was also included.

By Pradipto Rudra

The modules are very interesting, in order and organized. The content is crisp as well. However, the later slides being the advanced stages, required a little more time and space to allow understanding and sinking in - the delivery was very hurried during these.

By Abhishek Chatterjee

Nearly every video containing the python code is explained not clearly.Please take the issue into matter.