What you'll learn
Machine Learning
Requirements
Basic Python, Numpy and Pandas
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.
Who this course is for:
People interested about data science

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