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  KMeans

Deep Learning  Weights, Bias, Epochs, Gradient Descent,Batch, Stochastic Gradient Descent , Mini Batch
Appendix course on Numpy and Pandas have also been added.
A good knowledge of Python, Numpy and Pandas is required.
I would strongly recommend that unless you don’t have mastery of Python Language, Numpy and Pandas please don’t proceed with the course.
 People interested about data science
Suggest:
☞ Ensemble Machine Learning in Python: Random Forest, AdaBoost
☞ Machine Learning and AI: Support Vector Machines in Python
☞ Learn Python Through Exercises
☞ Deep Learning Prerequisites: Logistic Regression in Python
☞ Machine Learning Intro for Python Developers
☞ The Python Bible™  Everything You Need to Program in Python