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.