Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS
Complete Machine Learning Course with Python for beginners
Note: 4.4/5 (165 notes) 22,538 students
Instructor(s): Prashant Mishra
Last update: 2022-03-12
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
- No prior experience needed, you will learn what is needed. (A basic python knowledge will definetly increase your chances of learning fast))
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)
Introduction to Machine Learning
Application of machine learning in different fields.
Advantage of using Python libraries. (Python for machine learning).
Python for AI & ML
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
Probability & Conditional Probability
Probability distributions – Types of distribution – Binomial, Poisson & Normal distribution
Multiple variable Linear regression
Introduction to Regression
Simple linear regression
Model Evaluation in Regression Models
Evaluation Metrics in Regression Models
Multiple Linear Regression
Naïve bayes classifiers
Support vector machines
Intro to Clustering
Introduction to Classification
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
Featurization, Model selection & Tuning
Grid search CV
K fold cross-validation
Model selection and tuning
Regularising Linear models
Randomized search CV
Introduction to recommendation systems
Popularity based model
Content based recommendation system
Time series forecasting
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.
- Udemy Reviews Update
- Introduction to Machine Learning
- Introduction to Machine Learning
- Applied Statistics
- Statistics 101
- Descriptive Statistics
- Descriptive Statistics (Part-2)
- Measures of Spread
- Conditional Probability
- Probability Distribution
- Hypothesis Testing
- ntroduction to Python
- Python Installation
- Python IDE
- Python Basics II
- Data Structures
- Data Visualisation
- Data Transformation
- Let’s dig Machine Learning
- Machine Learning Intro
- Linear Regression
- Logistic Regression
- Naïve Bayes
- Decision Tree
- Hierarchical Clustering
- Ensemble ML
- Our Project (Recomendation System)
- Recommendations System
Time remaining or 899 enrolls left
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