1 - Installing Anaconda Distribution for Windows (158.54 MB) 10 - Creating NumPy Array with Ones Function (13.54 MB) 11 - Creating NumPy Array with Full Function (8 MB) 12 - Creating NumPy Array with Arange Function (8.73 MB) 13 - Creating NumPy Array with Eye Function (9.3 MB) 14 - Creating NumPy Array with Linspace Function (4.76 MB) 15 - Creating NumPy Array with Random Function (39.9 MB) 16 - Properties of NumPy Array (19.13 MB) 17 - Identifying the Largest Element of a Numpy Array (13.21 MB) 18 - Detecting Least Element of Numpy Array Min Ar (7.5 MB) 19 - Reshaping a NumPy Array Reshape Function (20.31 MB) 20 - Concatenating Numpy Arrays Concatenate Functio (28.79 MB) 21 - Splitting OneDimensional Numpy Arrays The Split (15.79 MB) 22 - Splitting TwoDimensional Numpy Arrays Split (22.56 MB) 23 - Sorting Numpy Arrays Sort Function (12.54 MB) 24 - Indexing Numpy Arrays (19.54 MB) 25 - Slicing OneDimensional Numpy Arrays (16.29 MB) 26 - Slicing TwoDimensional Numpy Arrays (25.57 MB) 27 - Assigning Value to OneDimensional Arrays (13.57 MB) 28 - Assigning Value to TwoDimensional Array (26.4 MB) 29 - Fancy Indexing of OneDimensional Arrrays (13.19 MB) 3 - Installing Anaconda Distribution for MacOs (71.5 MB) 30 - Fancy Indexing of TwoDimensional Arrrays (29.6 MB) 31 - Combining Fancy Index with Normal Indexing (9.43 MB) 32 - Combining Fancy Index with Normal Slicing (12.05 MB) 33 - Operations with Comparison Operators (16.24 MB) 34 - Arithmetic Operations in Numpy (83.18 MB) 35 - Statistical Operations in Numpy (36.61 MB) 36 - Solving SecondDegree Equations with NumPy (18.32 MB) 5 - Installing Anaconda Distribution for Linux (178.11 MB) 6 - Introduction to NumPy Library (54.55 MB) 7 - The Power of NumPy (48.2 MB) 8 - Creating NumPy Array with The Array Function (23.56 MB) 9 - Creating NumPy Array with Zeros Function (21.28 MB) 109 - K Nearest Neighbors Algorithm Theory (17.44 MB) 110 - K Nearest Neighbors Algorithm with Python Part 1 (19.8 MB) 111 - K Nearest Neighbors Algorithm with Python Part 2 (41.55 MB) 112 - K Nearest Neighbors Algorithm with Python Part 3 (19.67 MB) 113 - Hyperparameter Optimization Theory (34.74 MB) 114 - Hyperparameter Optimization with Python (34.49 MB) 115 - Decision Tree Algorithm Theory (24.77 MB) 116 - Decision Tree Algorithm with Python Part 1 (22.6 MB) 117 - Decision Tree Algorithm with Python Part 2 (32.26 MB) 118 - Decision Tree Algorithm with Python Part 3 (8.98 MB) 119 - Decision Tree Algorithm with Python Part 4 (33.63 MB) 120 - Decision Tree Algorithm with Python Part 5 (25.41 MB) 121 - Random Forest Algorithm Theory (18.01 MB) 122 - Random Forest Algorithm with Pyhon Part 1 (28.54 MB) 123 - Random Forest Algorithm with Pyhon Part 2 (27.32 MB) 124 - Support Vector Machine Algorithm Theory (14.96 MB) 125 - Support Vector Machine Algorithm with Python Part 1 (48.01 MB) 126 - Support Vector Machine Algorithm with Python Part 2 (33.12 MB) 127 - Support Vector Machine Algorithm with Python Part 3 (28.56 MB) 128 - Support Vector Machine Algorithm with Python Part 4 (23.14 MB) 129 - Unsupervised Learning Overview (12.05 MB) 130 - K Means Clustering Algorithm Theory (11.34 MB) 131 - K Means Clustering Algorithm with Python Part 1 (18.81 MB) 132 - K Means Clustering Algorithm with Python Part 2 (21.58 MB) 133 - K Means Clustering Algorithm with Python Part 3 (22.89 MB) 134 - K Means Clustering Algorithm with Python Part 4 (20.64 MB) 135 - Hierarchical Clustering Algorithm Theory (24.06 MB) 136 - Hierarchical Clustering Algorithm with Python Part 1 (20.98 MB) 137 - Hierarchical Clustering Algorithm with Python Part 2 (20.97 MB) 138 - Principal Component Analysis PCA Theory (29.43 MB) 139 - Principal Component Analysis PCA with Python Part 1 (15.06 MB) 140 - Principal Component Analysis PCA with Python Part 2 (5.09 MB) 141 - Principal Component Analysis PCA with Python Part 3 (22.08 MB) 142 - What is the Recommender System Part 1 (14.66 MB) 143 - What is the Recommender System Part 2 (12.35 MB) 38 - Introduction to Pandas Library (23.37 MB) 39 - Creating a Pandas Series with a List (44.13 MB) 40 - Creating a Pandas Series with a Dictionary (14.42 MB) 41 - Creating Pandas Series with NumPy Array (9.06 MB) 42 - Object Types in Series (15.43 MB) 43 - Examining the Primary Features of the Pandas Seri (12.31 MB) 44 - Most Applied Methods on Pandas Series (39.27 MB) 45 - Indexing and Slicing Pandas Series (23.33 MB) 46 - Creating Pandas DataFrame with List (16.95 MB) 47 - Creating Pandas DataFrame with NumPy Array (8.93 MB) 48 - Creating Pandas DataFrame with Dictionary (11.6 MB) 49 - Examining the Properties of Pandas DataFrames (19.02 MB) 50 - Element Selection Operations in Pandas DataFrames Lesson 1 (22.11 MB) 51 - Element Selection Operations in Pandas DataFrames Lesson 2 (22.34 MB) 52 - Top Level Element Selection in Pandas DataFramesLesson 1 (27.7 MB) 53 - Top Level Element Selection in Pandas DataFramesLesson 2 (22.39 MB) 54 - Top Level Element Selection in Pandas DataFramesLesson 3 (16.37 MB) 55 - Element Selection with Conditional Operations in Pandas Data Frames (34.22 MB) 56 - Adding Columns to Pandas Data Frames (24.68 MB) 57 - Removing Rows and Columns from Pandas Data frames (9.52 MB) 58 - Null Values in Pandas Dataframes (78.39 MB) 59 - Dropping Null Values Dropna Function (24.63 MB) 60 - Filling Null Values Fillna Function (38.38 MB) 61 - Setting Index in Pandas DataFrames (34.46 MB) 62 - MultiIndex and Index Hierarchy in Pandas DataFrames (27.18 MB) 63 - Element Selection in MultiIndexed DataFrames (21.2 MB) 64 - Selecting Elements Using the xs Function in MultiIndexed DataFrames (26.92 MB) 65 - Concatenating Pandas Dataframes Concat Function (54.12 MB) 66 - Merge Pandas Dataframes Merge Function Lesson 1 (43.15 MB) 67 - Merge Pandas Dataframes Merge Function Lesson 2 (26.03 MB) 68 - Merge Pandas Dataframes Merge Function Lesson 3 (71.27 MB) 69 - Merge Pandas Dataframes Merge Function Lesson 4 (36.04 MB) 70 - Joining Pandas Dataframes Join Function (48.19 MB) 71 - Loading a Dataset from the Seaborn Library (31.36 MB) 72 - Examining the Data Set 1 (31.51 MB) 73 - Aggregation Functions in Pandas DataFrames (110.64 MB) 74 - Examining the Data Set 2 (37.24 MB) 75 - Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes (77.05 MB) 76 - Advanced Aggregation Functions Aggregate Function (23.4 MB) 77 - Advanced Aggregation Functions Filter Function (19.47 MB) 78 - Advanced Aggregation Functions Transform Function (36.81 MB) 79 - Advanced Aggregation Functions Apply Function (49.23 MB) 80 - Examining the Data Set 3 (29.71 MB) 81 - Pivot Tables in Pandas Library (65.95 MB) 82 - Accessing and Making Files Available (41.53 MB) 83 - Data Entry with Csv and Txt Files (38.85 MB) 84 - Data Entry with Excel Files (17.79 MB) 85 - Outputting as an CSV Extension (22.36 MB) 86 - Outputting as an Excel File (17.35 MB) 144 - AI Machine Learning and Deep Learning (9.87 MB) 145 - History of Machine Learning (13.63 MB) 146 - Turing Machine and Turing Test (23.61 MB) 147 - What is Deep Learning (12.67 MB) 148 - Learning Representations From Data (23.14 MB) 149 - Workflow of Machine Learning (18.86 MB) 150 - Machine Learning Methods (30.94 MB) 151 - Supervised Machine Learning Methods 1 (20.06 MB) 152 - Supervised Machine Learning Methods 2 (37.03 MB) 153 - Supervised Machine Learning Methods 3 (35.24 MB) 154 - Supervised Machine Learning Methods 4 (79.33 MB) 155 - Gathering data (10.61 MB) 156 - Data preprocessing (15.38 MB) 157 - Choosing the right algorithm and model (43.51 MB) 158 - Training and testing the model (25.77 MB) 159 - Evaluation (14.42 MB) 160 - What Is ANN (14.83 MB) 161 - Anatomy of Neural Network (26.73 MB) 162 - Optimizers in Ai (26.13 MB) 163 - What is TensorFlow (38.14 MB) 164 - What is CNN (56.48 MB) 165 - Understanding RNN and LSTM Networks (31.05 MB) 166 - What is Transfer Learning (46.14 MB) 167 - What Is Data Science (12.97 MB) 168 - Data literacy in Data Science (6.61 MB) 169 - What is Numpy (15.87 MB) 170 - Why Numpy (8.15 MB) 87 - What is Machine Learning (20.3 MB) 88 - Machine Learning Terminology (8.92 MB) 90 - Classification vs Regression in Machine Learning (12.5 MB) 91 - Machine Learning Model Performance Evaluation Classification Error Metrics (106.19 MB) 92 - Evaluating Performance Regression Error Metrics in Python (29.38 MB) 93 - Machine Learning With Python (93.47 MB) 94 - What is Supervised Learning in Machine Learning (26.47 MB) 95 - Linear Regression Algorithm Theory in Machine Learning AZ (22.23 MB) 96 - Linear Regression Algorithm With Python Part 1 (54.91 MB) 97 - Linear Regression Algorithm With Python Part 2 (78.48 MB) 98 - Linear Regression Algorithm With Python Part 3 (51.76 MB) 99 - Linear Regression Algorithm With Python Part 4 (67.59 MB) 100 - What is Bias Variance TradeOff (36.27 MB) 101 - What is Logistic Regression Algorithm in Machine Learning (17.61 MB) 102 - Logistic Regression Algorithm with Python Part 1 (85.28 MB) 103 - Logistic Regression Algorithm with Python Part 2 (60.29 MB) 104 - Logistic Regression Algorithm with Python Part 3 (25.21 MB) 105 - Logistic Regression Algorithm with Python Part 4 (34.62 MB) 106 - Logistic Regression Algorithm with Python Part 5 (23.85 MB) 107 - KFold CrossValidation Theory (11.55 MB) 108 - KFold CrossValidation with Python (37.74 MB)