AI and ML

ByTechPath

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Level Beginner 3 students Duration Lifetime access 60 Lessons Language English

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Description

Curriculum

60 Sections
60 Lessons
Lifetime

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Descriptive Statistics
1
- 1.1
  Learn Data Science: Practical Statistics, Machine Learning, and NLP Tools and More
  91 Minutes
Probability
1
- 2.1
  Statistics & Probability Course: Percentiles, Quartiles, Distributions & Conditional Probability
  77 Minutes
Discrete Probaility Distribution
1
- 3.1
  Statistics and Probability: Distributions, Normalization & Sample Analysis
  66 Minutes
Continous Probability Distribution
1
- 4.1
  Learn Normal and Uniform Distributions: Z-Tables, Standardization & ML Applications
  83 Minutes
Central Limit Theorem
1
- 5.1
  Central Limit Theorem, Z-Test, T-Test & Confidence Levels
  75 Minutes
Hypothesis Testing
1
- 6.1
  Hypothesis Testing: Z-Test, T-Test, Errors & Real-Life Applications
  87 Minutes
Z test and T-test
1
- 7.1
  Hypothesis Testing and Statistical Analysis: Z-Test, T-Test, Sample Calculations & Exam Strategies
  93 Minutes
Sampling & Hypothesis Testing Essentials
1
- 8.1
  Statistical Analysis, Real-Life Applications & Exam Success Tips
  87 Minutes
Degrees of Freedom & Statistical Testing Basics
1
- 9.1
  Data Analysis, Sampling, and Real-World Applications
  76 Minutes
Python Fundamental I
1
- 10.1
  Python Data Types & Operations Guide: Lists, Tuples, Dictionaries, Sets & Machine Learning Basics
  61 Minutes
Python Fundamental II
1
- 11.1
  Python Programming Essentials: Functions, Loops, Data Structures & Logic Building
  80 Minutes
Python Fundamental III
1
- 12.1
  Machine Learning Foundations: Python Basics, Math Concepts, OOP & Practical Coding Tips
  71 Minutes
Numpy - I
1
- 13.1
  TensorFlow & Data Operations: Voice Input, Matrix Math, Image Processing, and Machine Learning Basics
  80 Minutes
Numpy II
1
- 14.1
  AI & Image Processing with Python: Data Analytics, Machine Learning, and Visualization Techniques and More
  86 Minutes
Pandas I
1
- 15.1
  Cleaning and preprocessing raw data using Pandas
  78 Minutes
Pandas II
1
- 16.1
  Data Analysis with Pandas: Login Security, Pivot Tables, Merging & Indexing Techniques in Python
  96 Minutes
Exploratory Data Analysis
1
- 17.1
  Data Visualization & Correlation Analysis: Reporting, Pattern Detection, and Practical Data Insights
  82 Minutes
Data Visualization
1
- 18.1
  Data Visualization & Analysis with ML Tools: Graphs, Classification, Reporting & Real-World Applications
  80 Minutes
IPL Data Analysis
1
- 19.1
  Cricket Data Analysis with Python: IPL Insights, Data Cleaning, Visualization & Predictive Modeling
  59 Minutes
Handling Missing Values
1
- 20.1
  Data Preprocessing in Machine Learning: Handling Missing Values, Outliers, Sampling & Model Optimization
  89 Minutes
Handling Categorical Variable
1
- 21.1
  Data Preprocessing for Machine Learning: Handling Missing Values, Feature Engineering & Model Optimization
  81 Minutes
Feature Transformation
1
- 22.1
  Applying various other feature transformation techniques, feature engineering, and more.
  59 Minutes
Outlier Detection
1
- 23.1
  Real-World Data Analytics & Machine Learning: Projects, Preprocessing, Case Studies & Career Tips
  61 Minutes
FIFA Analysis I
1
- 24.1
  Player Performance & Club Analysis Using Data Science: Visualization, Prediction & Python Tips
  57 Minutes
FIFA Analysis II
1
- 25.1
  Football Team Analysis with Data Science: Player Comparison, Team Formation & Sports Analytics Using Python
  45 Minutes
Feature Selection 1
1
- 26.1
  Data Analysis, Hypothesis Testing & Machine Learning with Real-World Examples
  85 Minutes
Feature Selection II
1
- 27.1
  Statistical Testing, Correlation Analysis & Feature Selection for Effective Data Modeling and Marketing Strategy
  79 Minutes
Linear Algebra for ML
1
- 28.1
  Data Modeling, Mathematical Techniques & Practical Applications in Machine Learning and Education
  85 Minutes
ML Introduction I
1
- 29.1
  Machine Learning: Algorithms, Data Preparation, and Real-World Applications
  88 Minutes
ML Introduction II
1
- 30.1
  Machine Learning: Algorithms, Data Management, and Model Optimization
  89 Minutes
Linear Regression I
1
- 31.1
  Machine Learning Fundamentals: Model Optimization, Loss Functions, and Practical Applications
  75 Minutes
Polynomial Regression and Regularization
1
- 32.1
  Machine Learning: Regression Models, Data Preprocessing, and Hyperparameter Tuning
  78 Minutes
K Nearest Neighbor
1
- 33.1
  Machine Learning Fundamentals: Model Fitting, Distance Metrics, and Cross-Validation Techniques
  84 Minutes
Performance Metrics
1
- 34.1
  Machine Learning Evaluation: Metrics, Confusion Matrix, and Model Performance Optimization
  88 Minutes
Practical Implemantation
1
- 35.1
  Data Preprocessing to Model Deployment: Complete Guide to Machine Learning Accuracy, Tuning & Real-World Projects
  70 Minutes
Logistic Regression
1
- 36.1
  Logistic Regression Explained: Classification, Feature Engineering & Model Evaluation with Real-World Datasets
  57 Minutes
Naive Bayes
1
- 37.1
  Logistic Regression & Conditional Probability with Real-Life Examples and Exam Tips
  77 Minutes
SVM I
1
- 38.1
  SVM & Classification in Machine Learning: Hyperplanes, Feature Transformation, and Optimization
  83 Minutes
SVM II
1
- 39.1
  Machine Learning : Logistic Regression, Feature Engineering & Real-Life Applications
  64 Minutes
Decision Tree
1
- 40.1
  Decision Trees, Entropy & Feature Engineering in Machine Learning
  75 Minutes
Ensemble Learning
1
- 41.1
  Ensemble Learning, Model Optimization & Practical Machine Learning Techniques
  86 Minutes
Dimensionality Reduction
1
- 42.1
  Feature Selection, PCA & Dimensionality Reduction Techniques in Machine Learning
  42 Minutes
Principal Component Analysis
1
- 43.1
  Data Analysis, Optimization Techniques & Variance in Machine Learning
  56 Minutes
Feature Engineering & ML with Real-World Insights
1
- 44.1
  Data Projection & Real-World ML Applications with Network Troubleshooting Insights
  44 Minutes
DBSCAN Algorithm
1
- 45.1
  Density-Based Clustering Techniques in Machine Learning
  70 Minutes
Recommendation System Content Based and Collaborative Based Filtering
1
- 46.1
  Content, Similarity & User-Based Models and Recommendation Systems with Machine Learning
  78 Minutes
Matrix Factorization (Recommendation System)
1
- 47.1
  Matrix Factorization and PCA for Building Advanced Recommendation Systems in Machine Learning
  62 Minutes
Machine Learning & Deep Learning Basics
1
- 48.1
  Machine Learning and Deep Learning: Models, Techniques, and Applications and More
  86 Minutes
Neural Network Single Perceptron
1
- 49.1
  Artificial Neural Networks (ANN) Basics and Applications
  68 Minutes
ML Exam Prep & Motivation
1
- 50.1
  Exam Preparation & Machine Learning Tips with Motivation
  70 Minutes
Edu & ML Tips with Motivation
1
- 51.1
  Education, Exams & ML Basics with Motivation
  75 Minutes
Machine Learning Basics & Activation Functions
1
- 52.1
  Introduction to Machine Learning, Activation Functions & Practical Applications
  81 Minutes
Activation Functions & Optimizers
1
- 53.1
  Machine Learning Optimization: Activation Functions, Gradient Descent & Hyperparameter Tuning
  145 Minutes
Momentum
1
- 54.1
  Machine Learning Optimization, Networking, and Data Management
  67 Minutes
Tensorflow Introduction
1
- 55.1
  Setup, Training, and Model, Computer Vision & Machine Learning
  87 Minutes
CNN Introduction
1
- 56.1
  Techniques, Transfer Learning & Real-World Applications and Image Classification with Neural Networks
  83 Minutes
CNN Implementation
1
- 57.1
  Image Classification with Neural Networks: Data Prep, Model Training & Real-World Applications
  73 Minutes
ML Projects & Image Processing in Daily Life
1
- 58.1
  Real-Life Applications of Machine Learning: From Image Processing to Technical Troubleshooting and Model
  92 Minutes
ML Model Building & Architecture Explained
1
- 59.1
  ML Architectures, Model Training & Real-World Applications
  66 Minutes
Model Architecture & Optimization
1
- 60.1
  Neural Network Architectures & Model Optimization Techniques
  74 Minutes

Instructor

TechPath

32 Students12 Courses

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