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Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS

Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS

Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS

Complete Machine Learning Course with Python for beginners


Preview this course GET COUPON CODE


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.
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