Complete A.I. & Machine Learning, Data Science Bootcamp

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

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