Are you looking to take an online course in practical machine learning or machine learning with TensorFlow? If you want to get extensive information about machine learning with scikit-learn and TensorFlow. Then this course is an excellent resource that is designed for you only. When you enroll in this course, you learn various techniques to do efficient machine learning tasks. If you know about pandas and NumPy or an absolute beginner, this course also offers many advantages. This online practical machine learning course in TensorFlow 2.0 and Scikit contains up-to-date content. That prepares you for the upcoming and latest trend you need to face. You will know the theory and practical knowledge. By the end of the course, you will have comprehensive information with many real-life examples to explain to the user. 

What will you get when you take our practical ML with Tensorflow 2.0 and Scikit course?

  • Enhancing present ML skills with fundamentals of practical machine learning with Scikit and Tensorflow.
  • Get practical and comprehensive information about each concept and also learn supervised and unsupervised ML with scikit.
  • Understand neural networks with Tensorflow 2.0 and, aside from this, gain expertise in reinforcement learning. 
  • This course also helps you to know about deep learning and image classification with time series.

Who can take this machine learning with the scikit and TensorFlow 2.0 course?

If you are a developer and familiar with pandas and NumPy and want to learn ML terminologies. Also, any interested candidate who wants to get a highly demanding skill with a government-approved certificate.

Course Content

Total: 55 lectures
  • Overview of the Anaconda Distribution
  • Installing the Anaconda Distribution for Scikit-Learn
  • Installing TensorFlow 2.0 from the Anaconda Distribution
  • Install Scikit-Learn and Tensorflow 2.0 Manually Through pip
  • What Is Machine Learning?
  • First Scikit-Learn Model
  • Overfitting and Regularization
  • Probability and Statistics Review
  • Probability Distribution and Metrics
  • Supervised Learning and KNN
  • Logistic Regression
  • Naïve Bayes
  • Support Vector Machines
  • Decision Trees
  • Ensemble Methods
  • K-means and Hierarchical Clustering
  • Connectivity and Density Clustering
  • Gaussian Mixture Models
  • Variational Bayesian Gaussian Mixture Models
  • Decomposing Signals into Components
  • Signal Decomposition with Factor and Independent Component Analysis
  • Novelty Detection
  • Outlier Detection
  • Locally Linear Embedded Manifolds
  • Multi-Dimensional Scaling and t-SNE Manifolds
  • Density Estimation
  • Restricted Boltzmann Machine
  • TensorFlow 2.0 Overview
  • TensorFlow 2.0's Gradient Tape
  • Working with Neural Networks and Keras
  • Keras Customization
  • Custom Networks in Keras
  • Core Neural Network Concepts
  • Regression and Transfer Learning
  • TensorFlow Estimators and TensorBoard
  • Introduction to ConvNets
  • ConvNets In Keras
  • Image Classification with Data Augmentation
  • Convolutional Autoencoders
  • Denoising and Variational Autoencoders
  • Custom Generative Adversarial Networks
  • Semantic Segmentation
  • Neural Style Transfer
  • Using Word Embeddings
  • Text Pipeline with Tokenization for Classification
  • Sequential Data with Recurrent Neural Networks
  • Best Practices with Recurrent Neural Networks
  • Time Series Forecasting
  • Forecasting with CNNs and RNNs
  • NLP Language Models
  • Generating Text from an LSTM
  • Sequence to Sequence Models
  • MT Seq2Seq with Attention
  • NLP Transformers
  • Training Transformers and NLP In Practice


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