Machine Learning with Tensorflow
Course Overview
Key Feature
Course outline
Objectives
Prerequisites
Machine Learning with Tensorflow is one of the most important new technologies to emerge into popular consciousness in the last decade, transforming fields from consumer electronics & healthcare to retail. This has led to intense curiosity about Machine Learning field among many students & working professionals in the field. Machine Learning Tensorflow | Machine learning using Python | MATLAB machine learning training | Machine Learning Tensorflow Certification
In todays world making business is very crucial because data comes in high volume, high velocity and variety , making a data driven business decision is typical . Machine Learning models plays a significant role with learning algorithms. Tensorflow is a relatively new open souce machine learning library from Google which simplies creating a complex machine learning models to a great extent, by hiding all the complexities, mathematics and algorithms. This course – Machine Learning with Tensorflow is designed to explore Tensforflow from scratch and building Machine Learning models with real-world data set
- 4 Days / 32 Hrs For Classroom or Online Training
- Soft copy of Study materials
- Course Completion Certificate
- Flexibility to choose classes
- Training by Certified World class trainer
- Industry wise – Real life practical examples
- Teaching assistance to support your learning journey
- Learn the required skills using Technocerts.
Introduction to TensorFlow
- Installing TensorFlow using Docker
- Installing Matplotlib
- Hello World applicatin with TensorFlow
Basic Statistics
- Basic Statistics and Exploratory Analysis
- Descriptive summary statistics with Numpy
- Summarize continous and categorical data
- Outlier analysis
Machine Learning Introduction
- Machine learning essentials
- Data representation and features
- Distance metrics
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Theano, Caffe, Torch, CGT, and TensorFlow
TensorFlow Essentials
- Representing tensors
- Creating operators and executing sessions
- Introduction Jupyter notebook for TensorFlow coding
- TensorFlow variables
- Visualizing data using TensorBoard
ML Algorithm – Linear Regression in TensorFlow
- Regression problems
- Linear regression applications
- Regularization
- Available datasets
- Coding Linear Regression with TensorFlow – Case study
ML Algorithm – Classification in TensorFlow
- Classification problems
- Using linear regression for classification
- Using logistic regression (including multi-dimensional input)
- Multiclass classifiers (such as softmax regression)
- Hands-on practicals session with TensorFlow
ML Algorithm – Clustering in TensorFlow
- Traversing files in TensorFlow
- K-means clustering
- Clustering using a self-organizing map
Simple Neural Networks in TensorFlow
- Introduction to Neural Networks
- Batch training
- Variational, denoising and stacked autoencoders
Reinforcement learning
- Exercise
- Concept of Reinforcement Learning
- Simple model applying Reinforcement Learning in TensorFlow
- Algorithm
Convolution and Recurrent Neural Networks
- Advantages and disadvantages of neural networks
- Convolutional neural networks
- The idea of contextual information
- Recurrent neural networks
- Real world predictive model – example
Case study – Stock Market Analsis with TensorFlow
- Exercise
- Case study – Stock Market Analysis
- Hands on Coding practice in TensorFlow
- Wrap-up
- Introduce Machine Learning
- Machine Learning with Tensorflow
- Tensorboard visualization
- Hands-on coding of Neural Networks with Tensorflow
- Applying ML with Tensorflow on the case study
- Machine Learning essential knowledge is required.
- Basic Python Programming is required.
- Knowledge of Statistics is recommended
