Machine Learning Tutorial Part - 8 | Complete Machine Learning Zero to Hero | Computer Science

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

Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!
What you’ll learn

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
Requirements
No prior experience is needed (not even Math and Statistics). We start from the very basics.
A computer (Linux/Windows/Mac) with internet connection.
Two paths for those that know programming and those that don’t.
All tools used in this course are free for you to use.