Course Overview

Become a machine learning engineer with Howe's applied learning program. At Howe, we offer a detailed course on machine learning concepts, such as supervised and unsupervised learning, algorithms, support vector machine, time series modelling, and regression through real-time industry use cases. With the dedicated mentoring session, you can easily learn all the nuances of machine learning.

Opt for the course now and become a certified machine learning engineer.

Program Feature

  • Corporate Trainers for Training
  • Affordable Course Fees
  • Flexible Schedule
  • Free Demo on Request
  • Certification on Completion
  • Create Your Own Content

Python Programming and Anaconda Navigator Download & Installation

Course Content

  • Anaconda Navigator Download
  • Anaconda Navigator Installation
  • Create environment and download libraries
  • Introduction to Jupiter notebook
  • Python Object & Data Structure
    • Numbers
    • String
    • List
    • Dictionary
    • Tuples
    • Sets and Booleans
  • Python Statement
  • Methods and Function
  • OOPs
  • Python Libraries
    • Numpy
    • Pandas
    • Matplotlib
    • Seaborn
  • Machine Learning application
  • Machine learning Process
  • How to become a machine learning engineer
  • Pattern Recognition
  • What is PGM
  • MRF
  • Introduction to statistic
  • Statistical analysis process
  • Kurtosis
  • Co-relation matrix
  • Statistics practical
  • Data preparation process
  • Type of Data
  • Feature Scaling
  • KNN Data preprocessing
  • KNN modeling
  • Visualize KNN model. Rise of artificial neuron
  • Introduction to ANN
  • Perceptron
  • Activation Functions
  • Feed forward Neural networks
  • Cost function in neural network
  • Back-propagation neural network
  • Introduction to CNN
  • CNN arch and Convolutional layer
  • Pooling layer and fully connected layer
  • RNN introduction
  • Recurrent neurons
  • Various configuration of RNNs
  • Training recurrent neural network
  • TensorFlow Introduction
  • Computational Graph
  • ANN practical
  • CNN Practical
  • RNN Practical
  • dt classifier
  • dt regressor
  • RF practical
  • Decision tree regression
  • Decision Tree Classification
  • Random Forest
  • SVM introduction
  • SVM Mathematics
  • Non-linear SVM
  • Clustering Introduction
  • K-means theory
  • k-means Mathematical
  • k-means practical part 1
  • k-means practical part 2
  • Basics of NLP
  • NLP application
  • Feature extraction
  • Gaussian NB
  • NLP practical’s
  • Rf intro
  • Case study overview
  • Bellman eq
  • MDP
  • Q-learning
  • Dynamic programming
  • Q-learning practical
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