Machine Learning Track

Machine Learning:
Beginner

Name: Machine Learning Course for Beginners
Availability: InStock

4.8 (1,230 reviews)

Introduction to Machine Learning: Beginner. Develop skills in model tuning, feature engineering, and using tools like scikit-learn and TensorFlow for predictive analytics.

Created by Avanteia

12,580 Total Enrolled

15 September 2024 Last Updated

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1 Month Duration

Certificate On Completion

Introduction

Overview

Develop skills in model tuning, feature engineering, and using tools like scikit-learn and TensorFlow for predictive analytics.

Python ML Statistics Scikit-learn Preprocessing TensorFlow

Goal

Learning Outcome

Understand core machine learning concepts, build basic models, and apply simple algorithms to analyze and predict data patterns.

Curriculum

Syllabus

Click any module to expand and view topics and hands-on labs included.

What is ML? Types (Supervised, Unsupervised, Reinforcement)
ML vs AI vs Deep Learning
Real-world applications
ML workflow & pipeline

Hands-on Lab

Install Python & Jupyter/Colab Run a basic ML pipeline on Iris dataset in Scikit-learn

Python essentials (functions, OOP, file handling)
NumPy, Pandas for data manipulation
Matplotlib, Seaborn for visualization

Hands-on Lab

Load CSV dataset in Pandas Perform summary statistics Plot graphs using Matplotlib/Seaborn

Data cleaning (missing values, outliers)
Categorical encoding (One-hot, Label Encoding)
Scaling (MinMax, StandardScaler)
Feature selection & extraction

Hands-on Lab

Handle missing values in Titanic dataset Apply feature scaling on dataset in Scikit-learn

Probability basics, Bayes Theorem
Distributions (Normal, Bernoulli, Binomial, Poisson)
Hypothesis testing (t-test, chi-square)

Hands-on Lab

Simulate coin toss & dice using Python Test significance using SciPy

Results

What You Will Learn

ML Fundamentals

Understand supervised, unsupervised, and reinforcement learning with real-world applications and workflows.

Python for ML

Master Python essentials, NumPy, Pandas, and visualization libraries for data manipulation and analysis.

Data Preprocessing

Clean data, handle missing values, encode categorical variables, and apply feature scaling and selection.

Probability & Statistics

Apply probability distributions, Bayes Theorem, and hypothesis testing to validate ML models and data insights.

Testimonials

What Our Students Say

The Level 2 course took my skills to a professional level. The Active Directory labs and privilege escalation modules are exactly what I needed to land my first pentesting job. Avanteia's hands-on approach is unmatched.

Vikram Patil Penetration Tester, Mumbai

I completed the Beginner course first and immediately enrolled in Intermediate. The malware analysis and reverse engineering modules were eye-opening. The 2-month duration is perfect for working professionals.

Sneha Kadam Cybersecurity Analyst, Pune

The cloud security and wireless hacking modules are incredibly relevant. I used the skills from this course to secure my company's AWS infrastructure. Highly recommended for anyone serious about cybersecurity.

Rahul Menon Security Engineer, Bangalore

Machine Learning: Beginner

Machine Learning:Beginner

Overview

Learning Outcome

Syllabus

What You Will Learn

ML Fundamentals

Python for ML

Data Preprocessing

Probability & Statistics

What Our Students Say

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Machine Learning:
Beginner

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