Machine Learning, AI & Data Science

Learn & Earn with Program

Course Description

This comprehensive and project-based course will introduce you to all of the modern skills of a Data Scientist, and along the way, we will build many real-world projects to add to your portfolio. You will get access to all the code, workbooks, and templates (Jupyter Notebooks) on Github so that you can put them on your portfolio immediately! This course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on-the-job skills that employers want.

The curriculum will be very hands-on as we walk you from start to finish through becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know to program, you can dive right in and skip the section where we teach you Python from scratch. If you are entirely new, we take you from the very beginning and teach you Python and how to use it in the real world for our projects. Don't worry; once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning, and Transfer Learning so you can get real-life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)!

The topics covered in this course are:

  • Data Exploration and Visualizations

  • Neural Networks and Deep Learning

  • Model Evaluation and Analysis

  • Python 3

  • Tensorflow 2.0

  • Numpy

  • Scikit-Learn

  • Data Science and Machine Learning Projects and Workflows

  • Data Visualization in Python with MatPlotLib and Seaborn

  • Transfer Learning

  • Image recognition and classification

  • Train/Test and cross-validation

  • Supervised Learning: Classification, Regression and Time Series

  • Decision Trees and Random Forests

  • Ensemble Learning

  • Hyperparameter Tuning

  • Using Pandas Data Frames to solve complex tasks

  • Use Pandas to handle CSV Files

  • Deep Learning / Neural Networks with TensorFlow 2.0 and Keras

  • Using Kaggle and entering Machine Learning competitions

  • How to present your findings and impress your boss

  • How to clean and prepare your data for analysis

  • K Nearest Neighbours

  • Support Vector Machines

  • Regression analysis (Linear Regression/Polynomial Regression)

  • How Hadoop, Apache Spark, Kafka, and Apache Flink are used

  • Setting up your environment with Conda, MiniConda, and Jupyter Notebooks

  • Using GPUs with Google Colab

By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We will use everything we learn in the course to build real-world professional projects. By the end, you will have a stack of projects you have built that you can show others.

Here’s the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don’t know where to go from there or how to build your projects. Or they show you a lot of code and complex math on the screen, but they don't explain things well enough to go off on your own and solve real-life machine-learning problems.

Whether you are new to programming, want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course will challenge you to go from an absolute beginner with no Data Science experience to someone that can go off, and build their Data Science and Machine learning workflows.

Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career.

You hear statements like Artificial Neural Network or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean!

Who this course is for:

  • Anyone with zero experience (or beginner/junior) who wants to learn Machine Learning, Data Science, and Python

  • You are a programmer that wants to extend your skills into Data Science and Machine Learning to make yourself more valuable.

  • Anyone who wants to learn these topics from industry experts that don’t only teach but have worked in the field

  • You’re looking for one single course to teach you about Machine learning and Data Science and get you caught up to speed with the industry.

  • You want to learn the fundamentals and be able to truly understand the topics instead of just watching somebody code on your screen for hours without really “getting it.”

  • You want to learn to use Deep Learning and Neural Networks with your projects.

  • You want to add value to your own business or the company you work for by using powerful Machine Learning tools.

  • Batch Duration: 6 Months
  • Videos Duration: 42+ hours
  • Fee: Free of Cost
  • Who can Join: Everyone
  • Enrolled Trainees: 1001
  • Video Medium: Urdu & English

What you will learn

    After completing this course, a trainee will be able to:

    • Become a Data Scientist and get hired
    • Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
    • Present Data Science projects to management and stakeholders
    • Real life case studies and projects to understand how things are done in the real world
    • Implement Machine Learning algorithms
    • How to improve your Machine Learning Models
    • Build a portfolio of work to have on your resume
    • Supervised and Unsupervised Learning
    • Explore large datasets using data visualization tools like Matplotlib and Seaborn
    • Learn NumPy and how it is used in Machine Learning
    • Learn to use the popular library Scikit-learn in your projects
    • Learn to perform Classification and Regression modelling
    • Master Machine Learning and use it on the job
    • Use modern tools that big tech companies like Google, Apple, Amazon and Meta use
    • Learn which Machine Learning model to choose for each type of problem
    • Learn best practices when it comes to Data Science Workflow
    • Learn how to program in Python using the latest Python 3
    • Learn to pre process data, clean data, and analyze large data.
    • Developer Environment setup for Data Science and Machine Learning
    • Machine Learning on Time Series data
    • Explore large datasets and wrangle data using Pandas
    • A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided
    • Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry
    • Learn how to apply Transfer Learning

ehunar - Hunarmand Kamyab Jawan Program