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  • Interested in the field of Machine Learning? Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way. A Road map connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them. Below are few Applications of Machine Learning in Practical Real World Machine learning can help with the diagnosis of diseases. Many physicians use chat bot with speech recognition capabilities to discern patterns in symptoms. Real-world examples for medical diagnosis: Assisting in formulating a diagnosis or recommending a treatment option. Google Maps uses machine learning in combination with various data sources including aggregate location data, historical traffic patterns, local government data, and real-time feedback from users, to predict traffic. Python leads the pack, with 57% of data scientists and machine learning developers using it and 33% Prioritising it for development. So, In this course also you will able learn Basics of Python to Advance State of the Art Techniques of  Deep Learning Models. There are 4 different sections in this course for complete understanding of all the concepts in Artificial Intelligence such as Python, Machine Learning, Deep Learning, Time Series Analysis.This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way: PYTHON - Data Structures, List, Tuples, Dictionary, Libraries, Functions, Operators etc Data Cleaning and Preprocessing MACHINE LEARNING - Regression: Simple Linear Regression, , SVR, Decision Tree , Random Forest, Clustering: K-Means, Hierarchical Clustering Algorithms Classification: Logistic Regression, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification Natural Language Processing: Bag-of-words model and algorithms for NLP DEEP LEARNING - Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long short term Memory, Vgg16 , Transfer learning, Web Based Flask Application. Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. Who this course is for: Anyone interested in Machine Learning. Students who have at least high school knowledge in maths and who want to start learning Machine Learning. Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning. Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets. Any students in college who want to start a career in Data Science. Any data analysts who want to level up in Machine Learning. Any people who are not satisfied with their job and who want to become a Data Scientist. Any people who want to create added value to their business by using powerful Machine Learning tools. I hope you will Enjoy this course. I will see you in the course.

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

A Road map connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.

Below are few Applications of Machine Learning in Practical Real World

  1. Machine learning can help with the diagnosis of diseases. Many physicians use chat bot with speech recognition capabilities to discern patterns in symptoms. Real-world examples for medical diagnosis: Assisting in formulating a diagnosis or recommending a treatment option.

  2. Google Maps uses machine learning in combination with various data sources including aggregate location data, historical traffic patterns, local government data, and real-time feedback from users, to predict traffic.

Python leads the pack, with 57% of data scientists and machine learning developers using it and 33% Prioritising it for development. So, In this course also you will able learn Basics of Python to Advance State of the Art Techniques of  Deep Learning Models.

There are 4 different sections in this course for complete understanding of all the concepts in Artificial Intelligence such as Python, Machine Learning, Deep Learning, Time Series Analysis.
This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:

 

PYTHON -

Data Structures, List, Tuples, Dictionary, Libraries, Functions, Operators etc

Data Cleaning and Preprocessing

 

MACHINE LEARNING -

Regression: Simple Linear Regression, , SVR, Decision Tree , Random Forest,

Clustering: K-Means, Hierarchical Clustering Algorithms

Classification: Logistic Regression, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Natural Language Processing: Bag-of-words model and algorithms for NLP

 

DEEP LEARNING -

Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long short term Memory, Vgg16 , Transfer learning, Web Based Flask Application.

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

 

Who this course is for:

Anyone interested in Machine Learning.

Students who have at least high school knowledge in maths and who want to start learning Machine Learning.

Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.

Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.

Any students in college who want to start a career in Data Science.

Any data analysts who want to level up in Machine Learning.

Any people who are not satisfied with their job and who want to become a Data Scientist.

Any people who want to create added value to their business by using powerful Machine Learning tools.

 

I hope you will Enjoy this course. I will see you in the course.

  • Interested in the field of Machine Learning? Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way. A Road map connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them. Below are few Applications of Machine Learning in Practical Real World Machine learning can help with the diagnosis of diseases. Many physicians use chat bot with speech recognition capabilities to discern patterns in symptoms. Real-world examples for medical diagnosis: Assisting in formulating a diagnosis or recommending a treatment option. Google Maps uses machine learning in combination with various data sources including aggregate location data, historical traffic patterns, local government data, and real-time feedback from users, to predict traffic. Python leads the pack, with 57% of data scientists and machine learning developers using it and 33% Prioritising it for development. So, In this course also you will able learn Basics of Python to Advance State of the Art Techniques of  Deep Learning Models. There are 4 different sections in this course for complete understanding of all the concepts in Artificial Intelligence such as Python, Machine Learning, Deep Learning, Time Series Analysis.This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way: PYTHON - Data Structures, List, Tuples, Dictionary, Libraries, Functions, Operators etc Data Cleaning and Preprocessing MACHINE LEARNING - Regression: Simple Linear Regression, , SVR, Decision Tree , Random Forest, Clustering: K-Means, Hierarchical Clustering Algorithms Classification: Logistic Regression, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification Natural Language Processing: Bag-of-words model and algorithms for NLP DEEP LEARNING - Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long short term Memory, Vgg16 , Transfer learning, Web Based Flask Application. Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. Who this course is for: Anyone interested in Machine Learning. Students who have at least high school knowledge in maths and who want to start learning Machine Learning. Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning. Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets. Any students in college who want to start a career in Data Science. Any data analysts who want to level up in Machine Learning. Any people who are not satisfied with their job and who want to become a Data Scientist. Any people who want to create added value to their business by using powerful Machine Learning tools. I hope you will Enjoy this course. I will see you in the course.
  • Interested in the field of Machine Learning? Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way. A Road map connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them. Below are few Applications of Machine Learning in Practical Real World Machine learning can help with the diagnosis of diseases. Many physicians use chat bot with speech recognition capabilities to discern patterns in symptoms. Real-world examples for medical diagnosis: Assisting in formulating a diagnosis or recommending a treatment option. Google Maps uses machine learning in combination with various data sources including aggregate location data, historical traffic patterns, local government data, and real-time feedback from users, to predict traffic. Python leads the pack, with 57% of data scientists and machine learning developers using it and 33% Prioritising it for development. So, In this course also you will able learn Basics of Python to Advance State of the Art Techniques of  Deep Learning Models. There are 4 different sections in this course for complete understanding of all the concepts in Artificial Intelligence such as Python, Machine Learning, Deep Learning, Time Series Analysis.This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way: PYTHON - Data Structures, List, Tuples, Dictionary, Libraries, Functions, Operators etc Data Cleaning and Preprocessing MACHINE LEARNING - Regression: Simple Linear Regression, , SVR, Decision Tree , Random Forest, Clustering: K-Means, Hierarchical Clustering Algorithms Classification: Logistic Regression, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification Natural Language Processing: Bag-of-words model and algorithms for NLP DEEP LEARNING - Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long short term Memory, Vgg16 , Transfer learning, Web Based Flask Application. Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. Who this course is for: Anyone interested in Machine Learning. Students who have at least high school knowledge in maths and who want to start learning Machine Learning. Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning. Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets. Any students in college who want to start a career in Data Science. Any data analysts who want to level up in Machine Learning. Any people who are not satisfied with their job and who want to become a Data Scientist. Any people who want to create added value to their business by using powerful Machine Learning tools. I hope you will Enjoy this course. I will see you in the course.
View More...
  • Section 1 : Python - Data Structures (Lists, Tuple, Dictionary) and String Manipulations 3 Lectures 00:00:08

    • Lecture 1 :
    • Python - Data Structures (Lists, Tuple, Dictionary) and String Manipulations Preview
    • Lecture 2 :
    • Python - Implementation Of Lambda, Recursion, Functions.
    • Lecture 3 :
    • Python - Understand Of Libraries,Exploratory Data Analysis,Descriptive Analysis
  • Section 2 : MACHINE LEARNING - Introduction to Basics of ML for Beginners 8 Lectures 00:00:22

    • Lecture 1 :
    • MACHINE LEARNING - Classical Machine Learning Algorithm Logistic Regression
    • Lecture 2 :
    • MACHINE LEARNING - Word Embedding Techniques BoW, TF -IDF, W2V etc
    • Lecture 3 :
    • MACHINE LEARNING - Text Cleaning and Preprocessing with Amazon Reviews Data
    • Lecture 4 :
    • MACHINE LEARNING - Classical Machine Learning Algorithm Linear Regression
    • Lecture 5 :
    • MACHINE LEARNING - Decision Tree Classifier and Regression with Example
    • Lecture 6 :
    • MACHINE LEARNING - Geometric Intuition of Ensembles Models and Flask Project
    • Lecture 7 :
    • MACHINE LEARNING - Data Analysis on Loan Approval Status
    • Lecture 8 :
    • MACHINE LEARNING - Unsupervised Learning Algorithms K means Cluster Techniques
  • Section 3 : DEEP LEARNING - Introduction to Basics of DL for Beginners 6 Lectures 00:00:14

    • Lecture 1 :
    • DEEP LEARNING - Introduction to Neural Networks and Basics of MLP, BACKPROP
    • Lecture 2 :
    • DEEP LEARNING - In Depth Understanding of RNN and LSTM with Examples
    • Lecture 3 :
    • DEEP LEARNING - Intuition Behind the Computer Vision and CNN Algorithm
    • Lecture 4 :
    • DEEP LEARNING - Convolutional Neural Networks with Pizza and CIFAR Projects
    • Lecture 5 :
    • DEEP LEARNING - Practical Examples on Transfer Learning for Vgg16 Model
    • Lecture 6 :
    • DEEP LEARNING - Web Based Flask Framework for Wild Animal Recognition with CNN
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