Machine Learning Online Course

Introduction to Machine Learning Course

ML for Everyone: Courses for Beginners, Professionals, and Innovato

Become an Expert in

Machine Learning

Over 150+ students have already registered for the next batch!

Machine learning is the study of computer algorithms that can better themselves automatically based on experience and data. It is a subfield of computer science and artificial intelligence (AI). Machine learning is changing the world; it is present in many important industries, including healthcare, education, transportation, food, and entertainment. With the aid of machine learning, almost every part of our daily lives will become easier and more efficient. Housing, cars, shopping, food ordering, and so on will all become easier and more efficient.

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Machine Learning Course Syllabus

Python basic Python concepts & terms Python fundamentals Python data structure & modules

What is machine learning? Applications of machine learning, Supervised vs Unsupervised Learning, Python libraires suitable for ML

Training& Testing, Cross validation, Features & labels, Pickling’s & Scaling, Techniques & Error metrics.

 

Linear regression & Forecasting, Prediction using regression, Logisitic regression & model evaluation, Support vector machines, K-N classification, K –means clustering, Random Forest linear

Content based recommender systems, Collaborative Filtering

Introduction to NLTK, BASICS & LAXICAL PROCESSING, Text Classification, stop words stemming lemmatization, named entity recognition, conduct sentiment analysis using Naïve bayes

Information & Training flow of neural network, Convolutional & Recurrent neural networks, Creating & Deploying networks using TensorFlow & karas

Implementation of all algorithms using sklern

Introduction to open Cv (Image processing) Learning basic image manipulations Introduction to CNN CNN Institution Convolution Operation pooling Flattering building a CNN using python Training the model

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Certificate Included

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Machine Learning Course Fee

Top-quality learning that’s easy on your pocket.
Aayushi Sharma
Aayushi Sharma
AI Developer
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Bharat Skillz offers clear, practical courses with real-life applications. The projects really helped me understand the concepts better. Highly recommended!

Self Paced

You’ll Get Access to:
4999
  • Internship Certificate by Co-Branded Company
  • 1 Minor + 1 Major project
  • LIVE INTERACTIVE SESSIONS
  • Recorded Modules
  • 25+ hours of video content access
  • Certificate on successful completion
  • Training Certificate from Bharat Skillz

Mentor Led

Everything in Self Paced, Plus:
9999
  • Internship Certificate by Co-Branded Company
  • 1 Minor + 1 Major project
  • LIVE INTERACTIVE SESSIONS
  • Recorded Modules
  • 25+ hours of video content access
  • Certificate on successful completion
  • Training Certificate from Bharat Skillz
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Python basic Python concepts & terms Python fundamentals Python data structure & modules

Pandas & visualization

Introduction to probability theory Distribution central limits theorem & hypothesis testing

Introduction to Artificial intelligence Applications of Artifiticial intelligence Hands on work with Python

Introduction to Neuron deep learning (basics & scope) Evolution of deep learning Applications of Deep learning Machine learning Vs. Deep learning Techniques & types of deep learning Introduction to ANN

Neuron Diagram Neuron Models & NETWORK FUNCTIONS of Neurons activation Functions of Gradient descent & Stochastic Ramp function & sigmoid function & Gaussian function

Perceptron & multilayer network Backpropagation Deep neural network installing libraries Creating ANN python Training the model

Introduction to open Cv (Image processing) Learning basic image manipulations Introduction to CNN CNN Institution Convolution Operation pooling Flattering building a CNN using python Training the model

Introduction to open Cv (Image processing) Learning basic image manipulations Introduction to CNN CNN Institution Convolution Operation pooling Flattering building a CNN using python Training the model

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