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The Road to Becoming an
ML Engineer

A Career Journey Designed to take you from Backend to Machine Learning engineer.

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Machine Learning is changing the world. From content personalization and spam filters, to self-driving cars and Virtual personal assistants or even cold fusion, ML workflows are now a first class citizen in the software industry.

We are offering experienced backend engineers an opportunity to get into the exciting world of Machine Learning. Will it be you?

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Week 1

Introduction to ML workloads, data crunching with python, building data pipelines, learning common practices and tools to generate and manage datasets.

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Week 2

Model training and tuning, designing and implementing ML pipelines, model storing and serving techniques for production systems.

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6-8 months, once a week

Advanced topics such as feature engineering, data lineage, Hyper parameters tuning, model monitoring and drift, and more.

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The program offers you a unique opportunity to develop your Machine Learning skills and knowledge with lots of hands-on experience.
From day one you will be joining Tikal.
After your training, you’ll be working together with TIkal’s experts to tackle and solve complex real-world problems.

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Essential Skills to join the program

  • At least 5 years of experience working as a Backend/Data engineer

  • Industry experience with data technologies - SQL and others

  • Coding skills (Python - a plus)

  • BSc in Computer science/ Software engineering or related fields - a plus

  • Familiarity with ML concepts - a plus

Ready to join the program and continue your tech journey?

If you think you have what it takes, apply now, and maybe you’ll find yourself working alongside some of the world’s best teams!

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Watch Q&A From the Panel discussion how to become ML Engineer

What you’ll learn 

Prepherals Fundamentals

  • AWS

  • Git

  • Linux

Statistical modeling

  • Probability & decision making

  • Distributions and Parameter estimation

  • Hypotheses testing

  • Predictive Statistics

Big data handling

  • Parallel computing

  • PySpark on python

  • Parallelising and Concurrency in Python

  • Handeling Big Data

  • Bias and Drop Analysis

Statistics Fundamentals

  • Probability basic concepts

  • Important distributions

  • LLN and CLT

  • Random variables

  • Bayesian statistics

  • Statistics Basic concepts

Machine learning

  • ML Intro

  • Regression

  • Classifiers

  • Model Selection

  • Decision Trees

  • Unsupervised learning

Neural networks

  • Back propagation

  • CNNs and RNNS

  • Common architectures

  • Training an NN model

  • Use cases

  • Custom views

Advanced Data scince tools in Python

  • Jupyter

  • Scientific Python

  • Matplotlib & Seaborn

SQL Basics

  • Filtering, Sorting, and

  • Calculating Data with SQL

  • Aggregate functions

  • Subqueries and Joins

  • Modifying and Analyzing

  • Data with SQL

Coding BP

  • Best practices in big data

  • Debugging

  • Time management in data work

  • Testing