Machine Learning using Python : Learn Hands-On

Video: .MP4, 1280×720 30 fps | Audio: AAC, 48kHz, 2ch | Duration: 07:18:01
Genre: eLearning | Language: English + Subtitles | Size: 4.49 GB
Naive Bayes Classifier, Decision tree, PCA, kNN classifier, linear regression, logistic regression,SVM classifier

Naive Bayes Classifier, Decision tree, PCA, kNN classifier, linear regression, logistic regression,SVM classifier

What you’ll learn
Linear Regression, SVR, Decision Tree Regression, Random Forest Regression
Machine Learning, Deep Learning, AI and Data Science Basic Concepts
Python package “Numpy” for numerical computation, Python package “Matplotlib” for visualization and plotting, Python package “pandas” for data analysis
Polynomial Regression
Logistic Regression
K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification
Random Forest Classification
Clustering: K-Means, Hierarchical Clustering
Data Visualization in Python with MatPlotLib and Seaborn
Dimensionality Reduction: PCA, PCA sklearn
Supervised Learning & Unsupervised Learning
Support Vector Machine
Curse of Dimensionality
Neural Networks
Applications of ML/AI/DS and Job prospects
K-Nearest Neighbour Classifier, Naïve Bayes Classifier, Decision Tree Classifier, Support Vector Machine Classifier, Random Forest Classifier (We shall use Python built-in libraries to solve classification problems using above mentioned classification algorithms)
Linear Algebra Review: Eigen value decomposition.
Multi-layered Perceptron (MLP) and its architecture.
Learning Rule : Back-Propagation
High dimensionality in data set and its problems.
Environment Setup : Anaconda and Jupyter Notebook
Using in-built Python libraries for solving linear regression problem.
Python implementation of Gradient Descent update rule for logistic regression.

Requirements
Knowledge of computer
Basic knowledge in math and statistics

Description
Learn to use Python, the ideal programming language for Machine Learning, with this comprehensive course from Hands-On System. Python plays a important role in the adoption of Machine Learning (ML) in the business environment.
Now a day’s Machine Learning is one of the most sought after skills in industry. After completion of this course students will understand and apply the concepts of machine learning and applied statistics for real world problems.
The topics we will be covering in this course are: Python libraries for data manipulation and visualization such as numpy, matplotlib and pandas. Linear Algebra, Exploratory Data Analysis, Linear Regression, Various Classification techniques, Clustering, Dimensionality reduction and Artificial Neural Networks.
This course is designed for Students who are pursuing bachelor’s or master’s degree in Statistics, Mathematics, Computer Science, Economics or any engineering fields. The students should have a little bit of knowledge in coding and undergraduate level mathematics.
Terminal competencies of the course, one would have learnt about tools to train machines based on real-world situations using Machine Learning algorithms, as well as to create complex algorithms and neural networks. During the latter stage of the course, learners will be introduced to real-world use cases of Machine Learning with Python for a Hands-On learning experience which would prepare them to create applications efficiently.

Who this course is for:
Anyone interested in Machine Learning.
Any students in college who want to start a career in Data Science.