Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS

–Udemy Training–
Last updated 3/2022
Duration: 13h13m | .MP4 | 720p | Language: English

Complete Machine Learning Course with Python for beginners

What you’ll learn
Master Machine Learning on Python
Make powerful analysis
Make accurate predictions
Make robust Machine Learning models
Use Machine Learning for personal purpose
Build an army of powerful Machine Learning models and know how to combine them to solve any problem
Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
Clean your input data to remove outliers
Requirements
No prior experience needed, you will learn what is needed. (A basic python knowledge will definetly increase your chances of learning fast))
Description
Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That’s just the average! And it’s not just about money – it’s interesting work too!
Machine Learning (Complete course Overview)
Foundations
Introduction to Machine Learning
Intro
Application of machine learning in different fields.
Advantage of using Python libraries. (Python for machine learning).
Python for AI & ML
Python Basics
Python functions, packages, and routines.
Working with Data structure, arrays, vectors & data frames. (Intro Based with some examples)
Jupyter notebook- installation & function
Pandas, NumPy, Matplotib, Seaborn
Applied Stastistics
Descriptive statistics
Probability & Conditional Probability
Hypothesis Testing
Inferential Statistics
Probability distributions – Types of distribution – Binomial, Poisson & Normal distribution
Machine Learning
Supervised Learning
Multiple variable Linear regression
Regression
Introduction to Regression
Simple linear regression
Model Evaluation in Regression Models
Evaluation Metrics in Regression Models
Multiple Linear Regression
Non-Linear Regression
Naïve bayes classifiers
Multiple regression
K-NN classification
Support vector machines
Unsupervised Learning
Intro to Clustering
K-means clustering
High-dimensional clustering
Hierarchical clustering
Dimension Reduction-PCA
Classification
Introduction to Classification
K-Nearest Neighbours
Evaluation Metrics in Classification
Introduction to decision tress
Building Decision Tress
Into Logistic regression
Logistic regression vs Linear Regression
Logistic Regression training
Support vector machine
Ensemble Techniques
Decision Trees
Bagging
Random Forests
Boosting
Featurization, Model selection & Tuning
Feature engineering
Model performance
ML pipeline
Grid search CV
K fold cross-validation
Model selection and tuning
Regularising Linear models
Bootstrap sampling
Randomized search CV
Recommendation Systems
Introduction to recommendation systems
Popularity based model
Hybrid models
Content based recommendation system
Collaborative filtering
Additional Modules
EDA
Pandas-profiling library
Time series forecasting
ARIMA Approach
Model Deployment
Kubernetes
Capstone Project
If you’ve got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry – and prepare you for a move into this hot career path.
Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won’t find academic, deeply mathematical coverage of these algorithms in this course – the focus is on practical understanding and application of them. At the end, you’ll be given a
final project
to apply what you’ve learned!
Our Learner’s Review: Excellent course. Precise and well-organized presentation. The complete course is filled with a lot of learning not only theoretical but also practical examples. Mr. Risabh is kind enough to share his practical experiences and actual problems faced by data scientists/ML engineers. The topic of "The ethics of deep learning" is really a gold nugget that everyone must follow. Thank you, 1stMentor and SelfCode Academy for this wonderful course.
Who this course is for:
Beginner Python Developers enthusiastic about Learning Machine Learning and Data Science
Anyone interested in Machine Learning.
Students who have at least high school knowledge in math 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 want to create added value to their business by using powerful Machine Learning tools.

More info: https://www.udemy.com/course/machine-learning-a-z-with-python-with-project-beginner