Download Python Machine Learning || Build Real World Projects

What you would learn in Python Machine Learning || Build Real World Projects course?

Welcome to the Python Machine Learning course. I believe that it will answer all your concerns about Machine Learning.

The course I am taking is based on three aspects :

1. Learn and understand the difficulties of this field using The Easy Way.

2. Learn from theoretical concepts through Python concrete illustrations.

3. In the process of building a relationship of learning between you and me, You will receive learning, Assistance, and Guidance from me.

As you may have guessed, machines, Learning (ML), and Artificial Intelligence (AI) are what the world is discussing these times;

If you'd like to :

• Join in this field, and I would advise you to:

• Learn how to create as well as test Machine Learning Models;

• Learn to deal with real-world problems

• Learn more about your knowledge of Python Programming Language besides what you'll be learning in the roadmap for the course.

Then:

• I invite you to dive into the ocean of learning and developing Apps or Models that be thought of !!

From the basic ideas of ML natural language Processing, Deep Learning, and Neural Nets moving to Real World Problems Solving and project creation:

• With more than 15 talks, it covers the theory of concepts developed with the most straightforward concepts to help you effortlessly master the concepts.

• With more than ten examples of practical Python code examples.

• More than 12 projects that are Real World Issues solved with Step by Step Construction Instructions.

The course is designed to help you through a step-by-stage learning process :

• Beginning with various presentations to provide an introduction to the fields of AI-ML & DL and the Intersection between them.

• Moving on to ideas, methods, and techniques are part of ML, like Cross Validation, Resampling, Dimensionality Reduction, and Natural Language Processing.

• Alongside, we will discuss Regression, Regularization, and Over/Under Fitting in depth with concrete Python example

• Then we'll move on to part 2 of the Projects Building, where we tackle the Real World Problem in ML.

• Numerous projects address various issues in a wide range of fields, from Health Care, Fashion, and Computer Vision to Speech and Emotion Recognition and Sentiment Analysis using various information in the form of Textual, Numerical, Audio, and images.

Many ML Algorithms are employed and utilized during the process of learning or the process of project development :

• SVM, NB, Random Forest, KNN, K-Means, SGDClassifier, Decision Tree, etc...

• Multiple Linear Logistic and L1 Regression L2.

• PCA, Autoencoders, and for Dimensionality Reduction.

• From simple neural nets, Multilayer Perceptron to Deep Convolutional Networks and LSTM.

• Implementing Transfer Learning Methodology.

• NLP: RegExp Stemming/Lemming, POS-tag tokenization, noise removal IDF-TF.

• Computer Vision

Course Content:

• Make use of Python to implement Machine Learning
• Get to know the Theoretical concepts for the practical Coding Implementation
• Designing apps that can provide predictions using various Machine Learning Models
• Implement Machine Learning Algorithms KNN, SVM, Decision Tree, NB, Random Forest, etc...
• Data Clustering using K-Means and Hierarchical Clustering
• Python Libraries: Numpy, Pandas, Scikit-learn, Matplotlib
• Learn how to handle textual Images, Text, and Videos Data with Python
• Machine Learning Concepts (Confusion matrix, Evaluation Metrics, etc...)
• Tensorflow, and Keras with examples of practical projects and examples
• Resampling, Reduction of Dimensionality, Data Scaling, Regression Regularization, Over-fitting, and Under-fitting
• Deep learning and neural networks from the fundamentals of neural Nets and CONVNETS to LSTM and CONVNETS
• Practical Textual data processing (Noise Removal Normalization, Tokenization, etc.) ...) by using NLT
• Transfer Learning Approach
• Find solutions to real-world problems with Machine Learning
• Create Real-world projects using numerous approaches and Algorithms
• Find out the Machine Learning Algorithm to employ for what problem.

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