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 KMeans 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 toppaying 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 â€“ Types of distribution â€“ 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
 NonLinear Regression
 NaĂ¯ve bayes classifiers
 Multiple regression
 KNN classification
 Support vector machines
 Unsupervised Learning
 Intro to Clustering
 Kmeans clustering
 Highdimensional clustering
 Hierarchical clustering
 Dimension ReductionPCA
 Classification
 Introduction to Classification
 KNearest 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 crossvalidation
 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
 Pandasprofiling 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. Itâ€™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 aÂ final projectÂ to apply what you’ve learned!
Our Learner’s Review: Excellent course. Precise and wellorganized 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, 1stMentorÂ 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.