Master Machine Learning , Deep Learning with Python Course Udemy

Master Machine Learning Deep Learning with Python Course Udemy -

Complete course covering fundamentals of Machine learning , Deep learning with Python code

What you’ll learn

Master Machine Learning , Deep Learning with Python Course

  • Machine Learning


  • Basic Python


Let me begin by telling secrets of mastery of machine learning#Secret 1 – 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 2  – 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.

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.

Master Machine Learning , Deep Learning with Python Course

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.
  • 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
  • Deep Learning – Weights, Bias, Epochs, Gradient Descent,Batch, Stochastic Gradient Descent , Mini Batch

Who this course is for:

  • People interested about data science


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