Data Science and Machine Learning

Course Description

This course offers a comprehensive introduction to the modern skills needed to become a Data Scientist. You'll build real-world projects for your portfolio and gain access to all code, workbooks, and templates (Jupyter Notebooks) on GitHub, ready to showcase immediately. It addresses the challenge of finding all necessary resources in one place, ensuring you learn the latest trends and essential on-the-job skills.The curriculum is hands-on, guiding you from start to finish on your journey to becoming a professional Machine Learning and Data Science engineer. The course features two tracks: one for those with programming experience and one for complete beginners. You'll learn Python from scratch if needed and then dive into advanced topics like Neural Networks, Deep Learning, and Transfer Learning, applying these skills in real-world projects.

Course Topics:

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 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 DataFrames

Handling CSV Files with Pandas

Deep Learning with TensorFlow 2.0 and Keras

Kaggle Competitions

Presenting Your Findings

Data Cleaning and Preparation

K-Nearest Neighbors

Support Vector Machines

Regression Analysis

Hadoop, Apache Spark, Kafka, and Apache Flink

Setting Up Your Environment with Conda, MiniConda, and Jupyter Notebooks

Using GPUs with Google Colab

By the end of the course, you’ll be equipped to work as a Data Scientist, with a portfolio of projects demonstrating your skills.

Who This Course Is For:

Beginners or juniors who want to learn Machine Learning, Data Science, and Python.

Programmers looking to expand into Data Science and Machine Learning.

Those who want to learn from industry experts with real-world experience.

Anyone seeking a single, comprehensive course on Machine Learning and Data Science.

Learners who want to understand the fundamentals and apply them in real-world scenarios.

Individuals aiming to add value to their business through Machine Learning tools.

This Course includes:

Batch Duration:

2-4 months

Who can join:

Only Pakistan Residents

Pass Final Exam:

Pass Final Exam

Video Medium:

Urdu & English

Apply Now

What you will learn

Become a Data Scientist and get hired.

Master Deep Learning, Transfer Learning, and Neural Networks with TensorFlow 2.0.

Present Data Science projects effectively to management and stakeholders.

Understand real-world applications through case studies and projects.

Implement and improve Machine Learning algorithms.

Build a strong portfolio of work for your resume.

Learn Supervised and Unsupervised Learning techniques.

Explore large datasets with data visualization tools like Matplotlib and Seaborn.

Understand and utilize NumPy in Machine Learning.

Use the popular Scikit-learn library in your projects.

Perform Classification and Regression modeling.

Apply Machine Learning skills in professional settings.

Use modern tools favored by companies like Google, Apple, Amazon, and Meta.

Choose the right Machine Learning model for different problems.

Follow best practices in Data Science workflows.

Program in Python using the latest Python 3.

Pre-process, clean, and analyze large datasets.

Set up a developer environment for Data Science and Machine Learning.

Apply Machine Learning to Time Series data.

Wrangle and explore large datasets using Pandas.

Create a portfolio of Data Science and Machine Learning projects with all code and notebooks provided.

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 industry tools like Hadoop, Spark, and Kafka.

Apply Transfer Learning in your projects.

Do you have questions?

We'll help you to grow your career and growth