This 400 hours course has been designed for people with basic knowledge in machine learning and decent programming skills. We recommend to be familiar with linear algebra (matrix multiplication) and calculus (multivariable derivatives).
You will gain excellent theoretical knowledge of deep-learning concepts, and bring high-level theory to life using cutting-edge frameworks such as TensorFlow, Keras and PyTorch.
You will study convolutional networks, recurrent networks, generative adversarial networks and deep reinforcement learning. You'll have the opportunity to prove your skills by building several projects and will gain exclusive insights from working professionals in the field.
Syllabus:
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01 - Neural Networks Basics: maths crash course, machine learning review, perceptron implementation.
02 - Deep Learning Fundamentals: multi-layer perceptron, backpropagation, activation functions, dropout, gradient descent, neural networks training.
03 - Big Data Engineering: Hadoop, MapReduce, Spark & PySpark, Elastic Search, Docker,  Teradata, Jenkin, Paperspace, AWS and Google Cloud.
04 - Convolutional Neural Networks (CNN): image classification, weight initialization, auto encoder, transfer learning.
05 - Recurrent Neural Networks (RNN): text generation, RNN and LSTM implementation, hyperparameters, embeddings, sentiment prediction.
06 - Generative Adversarial Networks (GAN): deep convolutional GAN, semi-supervised GAN.
07 - Reinforcement Learning (RL): Monte-Carlo methods, Q-Learning, Deep Q-Network, policy gradients, Gym.

This 400 hours course is the perfect track for beginners with no coding background. Through an accelerated intensive learning experience, you will gain a solid knowledge of data science with Python.
Without prior experience, you will soon be able to work with multiple data sources, create compelling visualizations, perform valuable exploratory data analysis, and implement machine learning models.  The course covers the most important tools used in the industry to help you build solid foundations, with an emphasis on learning by doing and our project driven approach will help you build a portfolio as you learn. Our instructors have deep practical and theoretical knowledge on the material covered.
We bring you the technical skills you need to join a growing tech startup or launch your own data-science based project. Last but not least, you will enjoy the  strong family-like atmosphere in the space
Syllabus:
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01 - Python Foundations : data types and structures, control flow, functions and modules, OOP, Git and Github.
02 - Data collection : interactions with files,  websites scraping, communication with APIs, database queries, regular expressions, big data with PySpark.
03 - Data analysis and visualization: Numpy, Pandas, Matplotlib, Seaborn, Tableau, exploratory data analysis (EDA), introduction to statistics.
04 - Machine Learning with Scikit-Learn: concepts and principles, classification, regression, train-test, Naive Bayes, cross-validation, feature selection, hyper parameters, PCA, unsupervised clustering, unsupervised dimensionality reduction, recommandation, deployment, regularization, anomaly detection, customer churn, random forest, decision trees, fraud detection, gradient boosting, times series.
05 - Natural Langage Processing (NLP) with NLTK/spaCy/Gensim/AutoML: preprocessing, tokenization, n-gram, lemmatization, latent semantic analysis, topic modeling, entity sentiment analysis.
06 - Deep Learning Introduction with Tensorflow: perceptron, multi-layer, convolutional neural networks, Docker and AWS deployment.

VIVADATA