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
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.
