Train in Data Reviews 

Very well explained!

The instructors are very knowledgeable, the courses are easy to understand and the questions are clarified on time.

Might want few courses like the forecasting with ML to be priced a little lower, haha.

multitimeseries forecasting is exciting. example is good. explain well. notebook file is good. i bought other course in udemy other teacher. but this is good very good.

Materials shared are very simple to understand and also the course agenda is well designed to increase complexity step by step.

what solidad galli teaches is relevant in market. Very good teacher and very good way of explaining concepts. I have fallen in love with ML now.

If you already have some basic background on data science, then these courses get you to the next level if you would like to specialize in time series with machine learning. Love the explanations.

The courses are really great! Very valuable without water

Wish I could award more stars and I really mean every single word ....Train in Data You guys really have no idea how wonderful all of your crafted courses are....I am currently pursuing Masters in Data Science from Deakin University, but the concept clarity and the resources and practical applications you provide, I just bow down my head in respect for that --my uni even doesn't even dares to explore these concepts that you do so fluently

Thanks for your wonderful courses...

The platform trainindata.com is the best platform to learn machine learning. I do not have a degree everything I know about machine learning I learnt from trainindata.com. Having taking almost all the courses I recommend the platform.
if you are beginner start with

feature engineering
feature selection
explainable machine learning
forecasting timeseries data
feature engineering for time series data

and Explore more.

The platform is gold.

Cheers!

Clearly explain the overview of time series forecasting tools. I think it's sufficient for begininers who want to learn how to use time series to solve problems.

I highly recommend this exceptional course to anyone looking to improve their time series forecasting skills. It offers an abundance of valuable materials and insights on creating effective features for forecasting. An excellent resource for enhancing your expertise in the field.

If you are not a beginner and are looking for a course that would teach you more advanced techniques in machine learning, this is for you.
I finished machine learning interpretability course and just purchased other courses as well. The course was super useful, clearly explained theoretical part was followed with practical codes, and lots of useful resources.
Thank you Train in Data for these courses.
Highly recommended.

You make complex topics understandable by providing excellent, highly focused, sequentially designed resources that can be easily accessed through a very convenient and efficient interface all at an affordable price.

Great training course with practical examples!

The quality of the content has exceeded my expectations.

I strongly recommend the Feature Engineering For Time Series Forecasting.

Thank you Sole and Kishan!

The course is well structured, explanations are clear, and the short, engaging videos enhance the learning experience.

The instructor, Soledad, is very experienced, knowledgeable and very good at explaining theory and also the practical implementation in python.

This is a rare combination of talents!

I have just completed 20% of the Course but still I am learning the nuances and a step-step by approach and each approach's assumptions, advantages, dis-advantages and applications. Great respect to the Guru Soledad Galli for a refined, structured and easy-to-comprehend course

Fantastic course. Knowledgeable instructors with detailed deep dives into new concepts. Thorough coverage of practical aspects and hands on coding examples that can be used in production. Great tests and quizzes to validate progress.

Strongly recommended.
The "Feature engineering for time series forecasting" course provided various worked-out examples in the form of code notebooks that went beyond the basic explanations commonly seen elsewhere. These examples helped me in my project work by providing me with a good, functional starting point from which I could progress with my forecasting tasks.