https://logikbot.quora.com Same thing but it's free. :)

When learning SQL, there are ideas and concepts you must know. If I ask you... what's DML? What's the difference between DML and DDL? You need to know what they are and be able to articulate what each does. If I ask you... What's Transact-SQL? You need to know it's Microsoft's flavor of SQL. If I ask you... what's a data type? You need to know what that is and be able to provide examples. For example... what's the difference between varchar and nvarchar? Interviewers won't have patience for candidates who don't know the basics. They will simply move on to the next candidate. My Transact-SQL course and associated study guide has all of this. You have zero excuses for not having all the basics memorized.

Many machine learning algorithms are sensitive to the range and distribution of attribute values in the input data. Outliers in input data can skew and mislead the training process of machine learning algorithms resulting in longer training times, less accurate models and ultimately poorer results. Even before predictive models are prepared on training data, outliers can result in misleading representations and in turn misleading interpretations of collected data. Outliers can skew the summary distribution of attribute values in descriptive statistics like mean and standard deviation and in plots such as histograms and scatterplots, compressing the body of the data. If you're building machine learning models, you ALWAYS remove outliers. You want your model to find trends in the data, not spend its time chasing down outliers.

Navigate through the posts to machine learning 101. It's a free video from YT. Watch the video and take notes. Did you write down the machine learning hierarchy? Can you define machine learning? Can you define AI? What is a machine learning model? What makes a machine learning model a deep learning model? Ok, you get the idea. There's probably 2-3 pages of notes from this video alone. Technical interviews are brutal. You either know what you're talking about or you don't. Be sure to download the note taking strategy I recommend, (blog post also) and then take notes on all the videos in this series.

You need to put the work in. It's not going to be an easy or a short journey. Nothing in life worth talking about ever is. I can help you with almost every facet of machine learning and data analysis but I can't force you to learn the material. Machine learning isn't hard but the information you need to excel is voluminous. You need to know a ton of shit before you'll be build production models. Break your journey into segments. Stay focused. This isn't a race it's an ultramarathon. Write shit down and study that shit.