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